Publications https://www.imi-tristan.eu/ en Translation from rats to humans https://www.imi-tristan.eu/translation-rats-humans <span>Translation from rats to humans</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-09-21T12:00:00Z">21.09.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Translation from rat to human: issues and current status (conference abstract)</strong></p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-789463a0-19fd-488d-96b9-f72b8d88a236 coh-component-instance-789463a0-19fd-488d-96b9-f72b8d88a236 ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-5cb937e3-d8c7-471b-aa63-279cb1f8223d coh-component-instance-5cb937e3-d8c7-471b-aa63-279cb1f8223d ssa-instance-9401bffa1af4f64b2ab72b1662282bbc coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>Translation from rat to human</h2> <div class="page-head"> <p><strong>Translation from rat to human: issues and current status</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by Steven Sourbron</em></p> <p><br /> HTN Meeting 2022</p> <p>Abstract</p> <p>The TRISTAN project has current completed all preclinical technical and biological validation work, and has started the process of translating methods from rats to humans. While similar methods exist and have been deployed in other liver applications, the particular focus on DILI and DDI’s poses technical constraints that require new solutions. In particular, the aim to characterize uptake rates of Gadoxetate into cells as well as the slow excretion from cells into bile requires long acquisitions – especially in the presence of strongly inhibited uptake and excretion. The aim of this talk is to sketch out the trajectory of clinical translation in TRISTAN so far and present detail on challenges encountered. </p> <p>One issue in human quantitative MRI in general is the large heterogeneity of clinical scanners by multiple vendors, the lack of standardisation in hardware and software and the fact that these are devices optimized for qualitative rather than quantitative imaging. This is in contrast to preclinical MRI scanners which are scientific instruments by definition and at higher fields are currently supplied by one single vendor. In order to estimate and help understand the impact of this scanner heterogeneity under real-world conditions of today, the first step towards clinical translation was a multi-vendor multi-site repeatability and reproducibility study of quantitative MRI in the absence of Gadoxetate [Tadimalla et al JMRI 2022]. The methodology, results and conclusions of this study will be reviewed briefly, as well as the practical experience of running them. </p> <p>The second stage towards clinical translation of the assay is an experimental medicine study in healthy volunteers, aiming to (1) derive benchmark values for Gadoxetate uptake and extraction under normal conditions and in the presence of a strong inhibitor (rifampicin), and (2) demonstrate that the effect of a strong inhibitor can be characterized reliably and consistently across subjects. Because these are the first Gadoxetate-enhanced dynamic data in humans, the study has an adaptive design allowing in the initial stages for modifications in the methodology should the data indicate the need to do so. Initially the dose of Gadoxetate for these studies is chosen at the lowest feasible value, and the study design allows this too to be stepped up if noise levels are deemed too high for reliable quantification. In this second part of the talk, I will present our first experience with the adaptive stage of the study, showing methodology and results in 3 volunteers with and without rifampicin.  </p> <p>We will conclude the talk with an outlook on clinical studies that are planned in the next stage of the development.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>Translation from rat to human</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> <div><a href="/taxonomy/term/121" hreflang="en">Liver</a></div> </div> </div> Thu, 26 Jan 2023 13:03:29 +0000 gunnar.schuetz 491 at https://www.imi-tristan.eu Assess liver transporters in rats https://www.imi-tristan.eu/assess-liver-transporters-rats <span>Assess liver transporters in rats</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-09-21T12:00:00Z">21.09.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Assessment of hepatic transporter function in rats using dynamic gadoxetate-enhanced MRI: A reproducibility study (conference abstract)</strong></p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-6df967bd-54a0-410a-b083-d38c68d59072 coh-component-instance-6df967bd-54a0-410a-b083-d38c68d59072 ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-a62c58c6-e239-4d36-920b-e005691a0119 coh-component-instance-a62c58c6-e239-4d36-920b-e005691a0119 ssa-instance-2be081c88f3e4e6a5bc5995aba05bf2b coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>Assess reproducibility of liver transporter function</h2> <div class="page-head"> <p><strong>Assessment of hepatic transporter function in rats using dynamic gadoxetate-enhanced MRI: A reproducibility study </strong></p> <p><em>by Catherine D. G. Hines, Sirisha Tadimalla, Claudia Green, Iina Laitinen, Ebony R. Gunwhy, Steven Sourbron, Issam Ben Khedhiri, Paul D. Hockings, Gunnar Schütz, John C. Waterton</em></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><br /> HTN Meeting 2022.</p> <p>Abstract</p> <p>Background: Drug-induced perturbations of liver transporter fluxes contribute to both drug-induced liver injury and drug-drug interactions, which are significant problems in healthcare and in drug development. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using gadoxetate has been proposed for assessing liver transporter-mediated drug injury, with compartmental modelling yielding gadoxetate hepatic plasma clearance (Ktrans) and biliary efflux (kbh) rate constants as biomarkers.  </p> <p>Purpose/Hypothesis: Quantify the reproducibility and repeatability of gadoxetate Ktrans and kbh in the absence of drugs and investigate robustness by comparing with the effect size of a potent inhibitor, as measured by the TRISTAN rat assay. </p> <p>Study Type: data collected from five retroprospective and eight prospective longitudinal substudies. </p> <p>Population/Subjects/Phantom/Specimen/Animal Model: 76 male Wistar-Han rats. </p> <p>Field Strength/Sequence: Two 4.7T and two 7T Bruker (Rheinstetten, Germany) scanners at three facilities using a T2-weighted (T2W) spin echo sequence for anatomy identification and a retrospectively triggered 3D Fast Low Angle Shot (FLASH) RF-spoiled gradient echo T1W acquisition. </p> <p>Assessment: 13 substudies covering three centres, two MRI field strengths, three time periods, and two substances were assessed (ndatasets=108). All 13 substudies included between three to eight rats either scanned once (baseline: Day 1) with saline or study-specific vehicle (nrats=76) or twice (follow-up, 2-7 days apart: Day 2) with saline (nrats=19) or 10 mg/kg of the strong inhibitor rifampicin (nrats=13). </p> <p>Methods: Images were analysed using a tracer kinetic (TK) two-compartment exchange model that characterises Ktrans and kbh kinetics of gadoxetate using liver ROIs with a standardised arterial input function derived from a simplified model of the rat circulation. Average Ktrans and kbh values from each study along with 95% confidence intervals were reported as the TRISTAN rat assay. From the assay, reproducibility (between-substudies) and repeatability (between-day) errors were quantified for saline data, only. Reproducibility errors were then deconstructed across centres, field strengths, and time periods to examine the relative impact of different variables. Effect sizes were calculated from data where a follow-up scan of rifampicin was acquired. One-way ANOVAs and paired T-tests were also performed, where p&lt;0.05 was considered to be statistically significant. </p> <p>Results: Reproducibility errors were 31% and 43% for Ktrans and kbh. Differences between substudies were significant. When isolating variables, reproducibility errors were as follows for choice of (i) centre: Ktrans&lt;26% (p=0.13), kbh&lt;93% (p=0.03); (ii) field strength: Ktrans&lt;16% (p=0.51), kbh&lt;84% (p=0.34); (iii) time period: Ktrans&lt;29% (p=0.35), kbh&lt;54% (p=0.008). Differences between baseline and follow-up saline data were not significant, with repeatability errors (Ktrans=14+/-2%; kbh=7+/-12%) much smaller than reproducibility errors. Rifampicin significantly reduced Ktrans (-170+/-8%) and kbh (-130+/-23%) across all centres. </p> <p>Conclusion: The TRISTAN rat assay is sufficiently robust for quantifying inhibition levels over &gt;50% in absolute or relative terms. This safely includes potent inhibitors like rifampicin (&gt;130% inhibition). For weaker inhibitors (20%-50% inhibition) only relative changes can be measured reliably. Inhibition levels below 20% cannot be quantified. This would require further technical development to reduce the uncertainty caused by the choice of centre, field strength, and drifts over time. </p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>Assess liver transporters in rats: reproducibility</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/121" hreflang="en">Liver</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Thu, 26 Jan 2023 12:57:19 +0000 gunnar.schuetz 486 at https://www.imi-tristan.eu DL for lung cavity estimation from Xe and H MRI https://www.imi-tristan.eu/dl-lung-cavity-estimation-xe-and-h-mri <span>DL for lung cavity estimation from Xe and H MRI</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-11-14T12:00:00Z">14.11.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI</strong></p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-d4b3dbca-44c0-425d-94be-5a6a25d1d5d4 coh-component-instance-d4b3dbca-44c0-425d-94be-5a6a25d1d5d4 ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-0818d426-278c-4dee-9c0b-351e8528a718 coh-component-instance-0818d426-278c-4dee-9c0b-351e8528a718 ssa-instance-2fddca648005ed3d507bbe4e65c55b86 coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>DL for lung cavity estimation from Xe- and H-MRI</h2> <div class="page-head"> <p><strong>A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by Joshua R. Astley BEng, Alberto M. Biancardi PhD, Helen Marshall PhD, Paul J. C. Hughes PhD, Guilhem J. Collier PhD, Laurie J. Smith PhD, James A. Eaden PhD, Rod Hughes MD, Jim M. Wild PhD, Bilal A. Tahir PhD</em></p> <p><br /> JMRI (2022). <a href="https://doi.org/10.1002/jmri.28519">doi: 10.1002/jmri.28519</a></p> <p>Abstract</p> <p>Background<br /> Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton (1H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single-channel, mono-modal deep learning (DL)-based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single-channel alternatives.</p> <p>Purpose<br /> We hypothesized that a DL-based dual-channel approach, leveraging both 1H-MRI and Xenon-129-MRI (129Xe-MRI), can generate LCEs more accurately than single-channel alternatives.</p> <p>Study Type<br /> Retrospective.</p> <p>Population<br /> A total of 480 corresponding 1H-MRI and 129Xe-MRI scans from 26 healthy participants (median age [range]: 11 [8–71]; 50% females) and 289 patients with pulmonary pathologies (median age [range]: 47 [6–83]; 51% females) were split into training (422 scans [88%]; 257 participants [82%]) and testing (58 scans [12%]; 58 participants [18%]) sets.</p> <p>Field Strength/Sequence<br /> 1.5-T, three-dimensional (3D) spoiled gradient-recalled 1H-MRI and 3D steady-state free-precession 129Xe-MRI.</p> <p>Assessment<br /> We developed a multimodal DL approach, integrating 129Xe-MRI and 1H-MRI, in a dual-channel convolutional neural network. We compared this approach to single-channel alternatives using manually edited LCEs as a benchmark. We further assessed a fully automatic DL-based framework to calculate VDPs and compared it to manually generated VDPs.</p> <p>Statistical Tests<br /> Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single-channel and dual-channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland–Altman analysis and paired t-tests compared manual and DL-generated VDPs. A P value &lt; 0.05 was considered statistically significant.</p> <p>Results<br /> The dual-channel approach significantly outperformed single-channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867–0.978), 1.68 mm (37.0–0.778), and 0.066 (0.246–0.045), respectively. DL-generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710).</p> <p>Data Conclusion<br /> Our dual-channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>DL for lung cavity estimation from Xe and H MRI</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/136" hreflang="en">Lung</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Thu, 26 Jan 2023 12:46:20 +0000 gunnar.schuetz 481 at https://www.imi-tristan.eu DIILD mouse model https://www.imi-tristan.eu/diild-mouse-model <span>DIILD mouse model</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-09-06T12:00:00Z">06.09.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Translational chronic drug-induced ILD mouse model, characterized by low-grade inflammation, fibrosis and dilated large airways (conference abstract)</strong></p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-17a87e76-f267-4268-b34f-24645a3b6b79 coh-component-instance-17a87e76-f267-4268-b34f-24645a3b6b79 ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-61ce50b2-9140-42f3-8f6d-5ce8c8f3252d coh-component-instance-61ce50b2-9140-42f3-8f6d-5ce8c8f3252d ssa-instance-652d1f6fb1d718d937e75a376befe8c3 coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>DIILD mouse model</h2> <div class="page-head"> <p><strong>Translational chronic drug-induced ILD mouse model, characterized by low-grade inflammation, fibrosis and dilated large airways</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by I Mahmutovic Persson, J Liu, R In 'T Zandt, N Fransén Petterson, A Örbom, H Falk-Håkansson, C Carvalho, K Von Wachenfeldt, L E Olsson</em></p> <p><br /> European Respiratory Journal 2022 60: 581 (conference abstract). <a href="https://doi.org/10.1183/13993003.congress-2022.581">doi: 10.1183/13993003.congress-2022.581</a></p> <p>Abstract</p> <p>Many systemically administrated drugs have reportedly shown to cause drug-induced interstitial lung disease (DIILD). Early disease detection is important in order to gain the best treatment outcomes. Here, we aimed to develop non-invasive MRI biomarkers, to allow for assessment of disease progression in a chronic model of bleomycin (BL)-induced ILD.</p> <p>Methods: C57BL/6 mice received i.p. injections of BL (or Saline as control) 2 d/wk, for 4 wks. MRI (RARE and UTE sequences) was performed in wks 3 and 4, as well as 1-2 wks after final dosing. Lung sections from each group were stained with Masson’s-Trichrome followed by modified Ashcroft scoring.</p> <p>Results: BL-challenged mice showed increased lung/body weight-ratio (p&lt;0.05) while significant signs of low-grade inflammation and fibrosis (p&lt;0.05) were found by histological analysis, indicating lesions emanating from the vascular side. Fibrosis progression was most apparent during the resting period (4+2wk) (p&lt;0.001). These changes were also visualized by MRI (RARE), with increasing lesion size over time (p&lt;0.05). MRI (UTE) analysis also showed increasing airway diameter during disease progression in the BL group.</p> <p>Conclusion: With non-invasive MRI we could map the lesions and follow the progression of dilated airways over time. This model is clinically relevant and therefore suitable to use for studying DIILD as well as progressive fibrosis.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>DIILD mouse model</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/136" hreflang="en">Lung</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Thu, 26 Jan 2023 12:14:04 +0000 gunnar.schuetz 476 at https://www.imi-tristan.eu Methotrexate DIILD in rodents https://www.imi-tristan.eu/methotrexate-diild-rodents <span>Methotrexate DIILD in rodents</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-09-06T12:00:00Z">06.09.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Methotrexate induces DIILD sporadically and at low incidence in rodents, similar to clinical scenario in humans (conference abstract)</strong><br />  </p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-87bce4cf-fe1f-460d-861d-1463c8994d8f coh-component-instance-87bce4cf-fe1f-460d-861d-1463c8994d8f ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-0c41e338-c031-437a-a19d-0cd9dacc4c17 coh-component-instance-0c41e338-c031-437a-a19d-0cd9dacc4c17 ssa-instance-2f204ccc78a054c849b3178c752fe081 coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>Methotrexate DIILD in rodents</h2> <div class="page-head"> <p><strong>Methotrexate induces DIILD sporadically and at low incidence in rodents, similar to clinical scenario in humans</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by N Fransén Pettersson, C Carvalho, I Mahmutovic Persson, H Falk-Håkansson, J Liu, L E Olsson, K Von Wachenfeldt</em></p> <p><br /> European Respiratory Journal 2022 60: 2542 (conference abstract). <a href="https://doi.org/10.1183/13993003.congress-2022.2542">doi: 10.1183/13993003.congress-2022.2542</a></p> <p>Abstract</p> <p>Drug-induced interstitial lung disease (DIILD) is underdiagnosed with increasing incidence. To better diagnose and treat DIILD, translational animal models are warranted. Methotrexate (MTX) is commonly used in patients who present with DIILD. Studies indicate that MTX alone does not induce DIILD, rather underlying genetic/environmental factors or combination therapies seem to have an impact on DIILD development. Here, we attempted to develop a translational chronic model of MTX-induced ILD.</p> <p>MTX was given to mice or rats, by different routes/concentrations/time points (Table). Histological assessment was the main readout in all studies, with additional MRI and lung function measurements, in selected animal groups. Histology showed limited fibrosis and/or inflammation in MTX treated animals, which developed in some of the animals, across various treatment groups. Appeared lung lesions resided from the vasculature. In cases of detected ILD, also systemic effects in other organs were present. However, there was no correlation between disease severity/dose/frequency of MTX administration.</p> <p>Conclusion: Neither disease incidence nor severity correlated with MTX concentration or exposure route/time. These observations correspond to clinical observations, where MTX treatment alone does not seem to induce DIILD. Therefore, using MTX only as a DIILD-inducing agent in preclinical research poses great challenges.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>Methotrexate DIILD in rodents</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/136" hreflang="en">Lung</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Thu, 26 Jan 2023 12:09:41 +0000 gunnar.schuetz 471 at https://www.imi-tristan.eu tissue distribution of GLP1 by PET vs Autoradiography https://www.imi-tristan.eu/tissue-distribution-glp1-pet-vs-autoradiography <span>tissue distribution of GLP1 by PET vs Autoradiography</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-06-30T12:00:00Z">30.06.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Comparison of the Tissue Distribution of a Long-Circulating Glucagon-like Peptide-1 Agonist Determined by Positron Emission Tomography and Quantitative Whole-Body Autoradiography</strong></p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-c4543d68-1687-4233-864a-0adba8c25dab coh-component-instance-c4543d68-1687-4233-864a-0adba8c25dab ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-856b769f-5a76-443f-9af1-1f0899db6b42 coh-component-instance-856b769f-5a76-443f-9af1-1f0899db6b42 ssa-instance-8ba1c1f3324c2fff48cf7cc8c1bcfb67 coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>tissue distribution of GLP1 by PET vs Autoradiography</h2> <div class="page-head"> <p><strong>Comparison of the Tissue Distribution of a Long-Circulating Glucagon-like Peptide-1 Agonist Determined by Positron Emission Tomography and Quantitative Whole-Body Autoradiography</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by Eduardo Felipe Alves Fernandes, Jonas Wilbs, Rene Raavé, Christian Borch Jacobsen, Hanne Toftelund, Hans Helleberg, Milou Boswinkel, Sandra Heskamp, Magnus Bernt Frederik Gustafsson, and Inga Bjørnsdottir</em></p> <p><br /> ACS Pharmacol. Transl. Sci. 2022, 5, 8, 616–624. <a href="https://doi.org/10.1021/acsptsci.2c00075">doi: 10.1021/acsptsci.2c00075</a></p> <p>Abstract</p> <p>Positron emission tomography (PET) is a molecular imaging modality that enables non-invasive visualization of tracer distribution and pharmacology. Recently, peptides with long half-lives allowed once-a-week dosing of glucagon-like peptide-1 receptor (GLP-1R) agonists with therapeutic applications in diabetes and obesity. PET imaging for such long-lived peptides is hindered by the typically used short-lived radionuclides. Zirconium-89 (<sup>89</sup>Zr) emerged as a promising PET radionuclide with a sufficiently long half-life to be applied for biodistribution studies of long-circulating biomolecules. A comparison between the biodistribution profiles obtained via <sup>89</sup>Zr-PET and the current standard, quantitative whole-body autoradiography (QWBA), will be valuable for the development of novel peptide drugs. We determined the PET biodistribution of a <sup>89</sup>Zr-labeled acylated peptide agonist of GLP-1R and compared it to the profile obtained by QWBA using analogous tritiated tracers for up to 1 week after administration. The plasma metabolic profile was obtained and identification was done for the tritiated tracers. We found that, at early time points, the biodistribution profiles agreed between PET and QWBA. At the latertime points, the <sup>89</sup>Zr tracer remained primarily trapped in the kidneys. The introduction of desferrioxamine (DFO) chelator reduced the peptide stability, and UPLC-MS analysis identified a circulating metabolite arising from DFO hydrolysis. Kidney accumulation of radiolabeled peptides and DFO metabolic instability may compromise biodistribution studies using <sup>89</sup>Zr-PET to support the development of new biopharmaceuticals. PET and QWBA biodistribution data correlated well during the absorption phase, but new and more stable 89Zr chelators are needed for a more accurate description of the elimination phase.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>Tissue distribution of GLP1 by PET vs Autoradiography</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/141" hreflang="en">Biologics-Pet</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Thu, 26 Jan 2023 12:02:49 +0000 gunnar.schuetz 466 at https://www.imi-tristan.eu MTT and pulmonary blood flow https://www.imi-tristan.eu/mtt-and-pulmonary-blood-flow <span>MTT and pulmonary blood flow</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-09-06T12:00:00Z">06.09.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Meta-analysis of mean transit time and pulmonary blood flow in the lung (conference abstract)</strong><br />  </p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-3d38db35-e1e6-4335-bcf0-8504becd8afc coh-component-instance-3d38db35-e1e6-4335-bcf0-8504becd8afc ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-e712d596-5850-48f2-bd07-ea22e2076806 coh-component-instance-e712d596-5850-48f2-bd07-ea22e2076806 ssa-instance-27ec3c22f418d633b83b8decdf7b089b coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>MEta-analysis of MTT and pulomary blood flow</h2> <div class="page-head"> <p><strong>Meta-analysis of mean transit time and pulmonary blood flow in the lung</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by M Tibiletti, L Edwards, J Naish, G J M Parker, J C Waterton</em></p> <p><br /> European Respiratory Journal 2022 60: 2147. <a href="https://doi.org/10.1183/13993003.congress-2022.2147">doi: 10.1183/13993003.congress-2022.2147</a></p> <p>Abstract</p> <p>Introduction: CT and MRI can assess mean transit time (MTT) and pulmonary blood flow (PBF) in the lung. We aimed to determine whether MTT and PBF in the healthy lung are consistent across studies and differentiated from results in disease.</p> <p>Method: A systematic literature search was conducted in PubMed to identify studies that quantified MTT and/or PBF in the lung. Inclusion criteria were limited to MRI or CT, English language, human subjects, injection of intravenous contrast agent, and quantitative values determined by indicator dilution theory. The weighted mean and standard deviation (SD) of MTT and PBF were estimated from the healthy volunteers’ (HV) values reported, weighted by number of subjects.</p> <p>Results: We identified 34 studies for meta-analysis after exclusions, summarised in figure 1. In HV, weighted MTT was 5.91±1.84s (10 studies) and the weighted PBF 246±93 ml/100ml/min (14 studies).</p> <p>Conclusion: MTT was consistent across studies in healthy volunteers and similarly in diseased subjects, with few values outside of the normal range. In comparison, PBF values were consistently markedly reduced in multiple diseases.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>MTT and pulmonary blood flow</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/136" hreflang="en">Lung</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Thu, 26 Jan 2023 11:56:53 +0000 gunnar.schuetz 461 at https://www.imi-tristan.eu Manual vs AI based segmentation for dosimetry https://www.imi-tristan.eu/manual-vs-ai-based-segmentation-dosimetry <span>Manual vs AI based segmentation for dosimetry</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-10-04T12:00:00Z">04.10.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Manual Versus Artificial Intelligence-Based Segmentations as a Pre-processing Step in Whole-body PET Dosimetry Calculations</strong><br />  </p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-7a6f0986-5ff2-4f37-95ec-22337e8596f8 coh-component-instance-7a6f0986-5ff2-4f37-95ec-22337e8596f8 ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-85d595b6-c11e-4049-a4a6-b575dac4ad39 coh-component-instance-85d595b6-c11e-4049-a4a6-b575dac4ad39 ssa-instance-b19c766515efe67f95d5b2c464842c5f coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>Manual vs AI based segmentation for Dosimetry</h2> <div class="page-head"> <p><strong>Manual Versus Artificial Intelligence-Based Segmentations as a Pre-processing Step in Whole-body PET Dosimetry Calculations</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by Joyce van Sluis, Walter Noordzij, Elisabeth G. E. de Vries, Iris C. Kok, Derk Jan A. de Groot, Mathilde Jalving, Marjolijn N. Lub-de Hooge, Adrienne H. Brouwers &amp; Ronald Boellaard </em></p> <p><br /> Mol Imaging Biol (2022). <a href="https://doi.org/10.1007/s11307-022-01775-5">doi: 10.1007/s11307-022-01775-5</a></p> <p>Abstract</p> <p>Purpose<br /> As novel tracers are continuously under development, it is important to obtain reliable radiation dose estimates to optimize the amount of activity that can be administered while keeping radiation burden within acceptable limits.</p> <p>Organ segmentation is required for quantification of specific uptake in organs of interest and whole-body dosimetry but is a time-consuming task which induces high interobserver variability. Therefore, we explored using manual segmentations versus an artificial intelligence (AI)-based automated segmentation tool as a pre-processing step for calculating whole-body effective doses to determine the influence of variability in volumetric whole-organ segmentations on dosimetry.</p> <p>Procedures<br /> PET/CT data of six patients undergoing imaging with <sup>89</sup>Zr-labelled pembrolizumab were included. Manual organ segmentations were performed, using in-house developed software, and biodistribution information was obtained. Based on the activity biodistribution information, residence times were calculated. The residence times served as input for OLINDA/EXM version 1.0 (Vanderbilt University, 2003) to calculate the whole-body effective dose (mSv/MBq).</p> <p>Subsequently, organ segmentations were performed using RECOMIA, a cloud-based AI platform for nuclear medicine and radiology research. The workflow for calculating residence times and whole-body effective doses, as described above, was repeated.</p> <p>Results<br /> Data were acquired on days 2, 4, and 7 post-injection, resulting in 18 scans. Overall analysis time per scan was approximately 4 h for manual segmentations compared to ≤ 30 min using AI-based segmentations. Median Jaccard similarity coefficients between manual and AI-based segmentations varied from 0.05 (range 0.00–0.14) for the pancreas to 0.78 (range 0.74–0.82) for the lungs. Whole-body effective doses differed minimally for the six patients with a median difference in received mSv/MBq of 0.52% (range 0.15–1.95%).</p> <p>Conclusion<br /> This pilot study suggests that whole-body dosimetry calculations can benefit from fast, automated AI-based whole organ segmentations.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>Manual vs AI based segmentation for dosimetry</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/141" hreflang="en">Biologics-Pet</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Thu, 26 Jan 2023 10:15:14 +0000 gunnar.schuetz 456 at https://www.imi-tristan.eu longitudinal lung UTE-MRI vs CT for ILD https://www.imi-tristan.eu/longitudinal-lung-ute-mri-vs-ct-ild <span>longitudinal lung UTE-MRI vs CT for ILD</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>26.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-05-12T12:00:00Z">12.05.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Longitudinal comparison of quantitative UTE lung MRI and CT biomarkers in interstitial lung disease (conference abstract)</strong></p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-00b8ae6f-4787-4d0c-8ad4-ef761249cad1 coh-component-instance-00b8ae6f-4787-4d0c-8ad4-ef761249cad1 ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-2ab3cf33-8cbd-4398-b8e5-09cfaaf7bb84 coh-component-instance-2ab3cf33-8cbd-4398-b8e5-09cfaaf7bb84 ssa-instance-8c9fb521a50e227d1e88b550b8b2ffc1 coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>Longitudinal comparison of lung UTE-MRI and CT</h2> <div class="page-head"> <p><strong>Longitudinal comparison of quantitative UTE lung MRI and CT biomarkers in interstitial lung disease</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by Ho-Fung Chan, Timothy J Baldwin, Harry Barker, Neil J Stewart, James A Eaden, Paul J.C Hughes, Nicholas D Weatherley, Joshua Astley, Bilal A Tahir, Kevin M Johnson, Ronald A Karwoski, Brian J Bartholmai, Marta Tibiletti, Colm T Leonard, Sarah Skeoch, Nazia Chaudhuri, Ian N Bruce, Geoff J.M Parker, Stephen M Bianchi, and Jim M Wild</em></p> <p><br /> ISMRM 2022 conference abstract</p> <p>Synopsis<br /> UTE lung MRI approaches the diagnostic quality of CT, opening up the possibility for longitudinal follow-up of interstitial lung disease (ILD) progression. Two quantitative biomarkers of UTE lung signal were developed for monitoring longitudinal change in ILD and benchmarked against quantitative CT CALIPER measurements. Normalized UTE lung signal and UTE high percentage (based on 95% cutoff of healthy UTE lung values) was significantly different between nine healthy volunteers and sixteen ILD patients. Longitudinal change in UTE biomarkers correlated with change in CT CALIPER ILD% in the ILD patients, and most-strongly correlated to CT ground-glass changes in the lung parenchyma.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>Longitudinal comparison of lung UTE-MRI and CT for ILD</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/136" hreflang="en">Lung</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Thu, 26 Jan 2023 10:08:27 +0000 gunnar.schuetz 451 at https://www.imi-tristan.eu Bias, Repeatability and Reproducibility of Liver T1 Mapping https://www.imi-tristan.eu/bias-repeatability-and-reproducibility-liver-t1-mapping <span>Bias, Repeatability and Reproducibility of Liver T1 Mapping</span> <span><span lang="" about="/user/276" typeof="schema:Person" property="schema:name" datatype="">gunnar.schuetz</span></span> <span>25.01.2023</span> <div> <div>Private</div> <div>Public</div> </div> <div><time datetime="2022-02-27T12:00:00Z">27.02.2022</time> </div> <div> <div> <article about="/taxonomy/term/131"> <div> <div class="coh-container ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-style-article-teaser---type-1---sgm ssa-instance-ea845bf1059b2bf24bd9378c77b148fa coh-ce-cpt_article_teaser_type_1-7c69df1" > <div class="coh-container text-wrapper" > <h4 class="coh-heading headline " > <b>Article Title and Summary</b> </h4> <div class="coh-wysiwyg description" > <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.</p> </div> <a href="/#" class="coh-link ssa-component coh-component ssa-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 coh-component-instance-f0598579-1399-4f64-8cf9-8ba150a93307 read-more enable-basic-hover ssa-instance-109eadc83f29bfa49f29249e9fcf3828 coh-ce-cpt_read_more_button-34304a6b" title="Read more" target="_self" data-analytics-layer="[{&quot;trigger&quot;:&quot;click&quot;,&quot;value&quot;:&quot;gaGenericEvent&quot;,&quot;key&quot;:&quot;event&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventCategory&quot;,&quot;value&quot;:&quot;CTA&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventLabel&quot;,&quot;value&quot;:&quot;www.imi-tristan.eu/#&quot;},{&quot;trigger&quot;:&quot;click&quot;,&quot;key&quot;:&quot;eventAction&quot;,&quot;value&quot;:&quot;Read more&quot;}]" > <span class="coh-inline-element">Read more</span> </a> </div> </div> </div> </article> </div> </div> <div><p><strong>Bias, Repeatability and Reproducibility of Liver T1 Mapping With Variable Flip Angles</strong></p> </div> <div><ul class="coh-style-approval-code-list"><li class="coh-approval-code coh-page-approval-code">123</li></ul></div> <div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-f4c0a258-c1ab-4115-88c4-c2eec09afdb5 coh-component-instance-f4c0a258-c1ab-4115-88c4-c2eec09afdb5 ssa-instance-c84533920c1dd61e22c2748af1f6d37c coh-ce-cpt_text_component-109d695a" > <div id="head-box"><div class="container head-box sub" title="Publication" style="background-image:url('/sites/g/files/vrxlpx12716/files/2020-12/background-header-publications.jpg');"><div class="row"><div class="headline transparent-box-black"><h1 class="hind-light p-md-5"><span class="green-font hind-semibold">Publications</span><br />Take a look</h1></div></div></div></div> </div> <div class="coh-wysiwyg ssa-component coh-component ssa-component-instance-0b919e2d-700d-4741-b58e-91cb7ec9b25c coh-component-instance-0b919e2d-700d-4741-b58e-91cb7ec9b25c ssa-instance-47c54f8c014a216bb63fd223a598cf0e coh-ce-cpt_text_component-109d695a" > <div class="container publication-section"> <div class="spacer"> <div class="row"> <div class="col-sm-12 col-lg-12 p-md-5 "><!-- post content --> <div class="blog-post"> <div> <h2>REpeatability and Reproducibility of Liver T<sub>1</sub> Mapping</h2> <div class="page-head"> <p><strong>Bias, Repeatability and Reproducibility of Liver T1 Mapping With Variable Flip Angles</strong></p> </div> <div> <div class="paragraph " style="clear:both;"> <div class="paragraph-box-text"> <p><em>by Sirisha Tadimalla PhD, Daniel J. Wilson PhD, David Shelley BSc, Gavin Bainbridge BSc, Margaret Saysell BSc, Iosif A. Mendichovszky MD, Martin J. Graves PhD, J. Ashley Guthrie MB, John C. Waterton PhD, Geoffrey J.M. Parker PhD, Steven P. Sourbron PhD</em></p> <p><br /> JMRI 2022, 56(4), 1042-1052. <a href="https://doi.org/10.1002/jmri.28127">doi: 10.1002/jmri.28127</a></p> <p>Abstract</p> <p>Background<br /> Three-dimensional variable flip angle (VFA) methods are commonly used for T1 mapping of the liver, but there is no data on the accuracy, repeatability, and reproducibility of this technique in this organ in a multivendor setting.</p> <p>Purpose<br /> To measure bias, repeatability, and reproducibility of VFA T1 mapping in the liver.</p> <p>Study Type<br /> Prospective observational.</p> <p>Population<br /> Eight healthy volunteers, four women, with no known liver disease.</p> <p>Field Strength/Sequence<br /> 1.5-T and 3.0-T; three-dimensional steady-state spoiled gradient echo with VFAs; Look-Locker.</p> <p>Assessment<br /> Traveling volunteers were scanned twice each (30 minutes to 3 months apart) on six MRI scanners from three vendors (GE Healthcare, Philips Medical Systems, and Siemens Healthineers) at two field strengths. The maximum period between the first and last scans among all volunteers was 9 months. Volunteers were instructed to abstain from alcohol intake for at least 72 hours prior to each scan and avoid high cholesterol foods on the day of the scan.</p> <p>Statistical Tests<br /> Repeated measures ANOVA, Student t-test, Levene's test of variances, and 95% significance level. The percent error relative to literature liver T1 in healthy volunteers was used to assess bias. The relative error (RE) due to intrascanner and interscanner variation in T1 measurements was used to assess repeatability and reproducibility.</p> <p>Results<br /> The 95% confidence interval (CI) on the mean bias and mean repeatability RE of VFA T1 in the healthy liver was 34 ± 6% and 10 ± 3%, respectively. The 95% CI on the mean reproducibility RE at 1.5 T and 3.0 T was 29 ± 7% and 25 ± 4%, respectively.</p> <p>Data Conclusion<br /> Bias, repeatability, and reproducibility of VFA T1 mapping in the liver in a multivendor setting are similar to those reported for breast, prostate, and brain.</p> </div> </div> <!-- end paragraph --></div> </div> </div> </div> </div> </div> </div> </div> </div> <div>Repeatability and reproducibility of liver T1 mapping</div> <div> <div>Article categories</div> <div> <div><a href="/taxonomy/term/121" hreflang="en">Liver</a></div> <div><a href="/taxonomy/term/126" hreflang="en">Publications</a></div> </div> </div> Wed, 25 Jan 2023 16:12:49 +0000 gunnar.schuetz 446 at https://www.imi-tristan.eu