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217 results for “T2”
Solo für T2
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1665228935.6858.jpg">https://4dcity.org/imgupload/1665228935.6858.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52396937431_11392388fb_m.jpg">https://live.staticflickr.com/65535/52396937431_11392388fb_m.jpg</a> <br><br><u>Image-Metadata:</u><br>Filename: 1665228935.6858.jpg<br>Image Dimensions: 240x175<br>Megapixels: 0.04 MP<br>Filesize: 24.93 KB<br><br>Copyright: Michael Beitelsmann<br>Artist: Michael Beitelsmann
GP 2021 T2 close
Source: Objaverse 1.0 / Sketchfab
More Than 50% of Unifocal cN0 T1b/Small T2 Papillary Thyroid Carcinoma May Require Completion Thyroidectomy if Nodal Status is Evaluated
ClinicalTrials.gov study NCT06439745. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Outcomes of TAMIS-Total Mesorectal Excision Versus Transanal Loco-regional Excision for T2 Low Rectal Cancer
ClinicalTrials.gov study NCT02488577. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Effectiveness of T2* MRI in Cervical Spondylotic Myelopathy
ClinicalTrials.gov study NCT04955041. IPD Sharing: NO. Countries: 0. Publications: 0.
A Phase 1 Clinical Trial to Evaluate the Safety and Pharmacokinetic Characteristics of DW1809-T2
ClinicalTrials.gov study NCT05214690. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Characterization of the Liver Parenchyma Using Parametric T1 and T2 Magnetic Resonance Relaxometry
ClinicalTrials.gov study NCT04623528. IPD Sharing: NO. Countries: 0. Publications: 0.
Uncovering novel roles for thyroid hormones in teleost fishes: Differential transcriptome regulation in brain and liver by 3,5-T2 and 3’,3,5-T3
GEO Series GSE96046. Oreochromis niloticus. 18 samples. Type: Expression profiling by high throughput sequencing.
Salmonella Typhimurium: control vs. T2 toxin treatment
GEO Series GSE30925. Salmonella enterica subsp. enterica serovar Typhimurium str. DT104; Salmonella enterica subsp. enterica serovar Typhimurium; Salmonella enterica subsp. enterica serovar Typhimurium str. LT2; Salmonella enterica subsp. enterica serovar Typhimurium str. SL1344; Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91; Salmonella enterica subsp. enterica serovar Enteritidis. 12 samples. Type: Expression profiling by array.
test for t1 and t2
<p>t1: data on samples</p> <p>t2: data on sample replicates </p> <p> </p> <p>Both data are test samples to assess zenodo</p>
4PEHz-treated P.falciparum parasites vs. untreated extracted at timepoints t1 (12 Hours post invasion or HPI), t2 (24 HPI) and t3 (36 HPI)
GEO Series GSE47611. Plasmodium falciparum. 14 samples. Type: Expression profiling by array.
Expression data from Lbeta T2 cells upon activin treatment
GEO Series GSE5232. Mus musculus. 6 samples. Type: Expression profiling by array.
An Ribonuclease T2 Family Protein Modulates Acinetobacter baumannii Abiotic Surface Colonization
GEO Series GSE52002. Acinetobacter baumannii. 4 samples. Type: Expression profiling by array.
Data From: B1 Field inhomogeneity correction for qDESS T2 mapping: application to rapid bilateral knee imaging
<p>T2 mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of between-knee asymmetry in OA onset and progression. The quantitative double-echo in steady-state (qDESS) can provide fast simultaneous bilateral knee T2 and high-resolution morphometry for cartilage and meniscus. The qDESS uses an analytical signal model to compute T2 relaxometry maps, which require knowledge of the flip angle (FA). In the presence of B1 inhomogeneities, inconsistencies between the nominal and actual FA can affect the accuracy of T2 measurements.</p> <p>We propose a pixel-wise B1 correction method for qDESS 2 mapping exploiting an auxiliary B1 map to compute the actual FA used in the model. The technique was validated in a phantom and in vivo with simultaneous bilateral knee imaging. T2 measurements of femoral cartilage (FC) of both knees of a healthy participant were repeated longitudinally to investigate the association between T2 variation and B1. The results showed that applying the B1 correction could mitigate T2 variations that were driven by B1 inhomogeneities. Specifically, T2 left-right symmetry increased following the B1 correction. Without the B1 correction, T2 values showed a significant (p<0.05) moderate Pearson’s correlation with B1 across time points (0.68 <r<0.70 and 0.74<r<0.79, for the left and right knee, respectively). The correlations and slopes decreased using the B1 correction (0.01<r<0.02 and 0.48<r<0.55, for left and right knee, respectively) and were not statistically significant (p > 0.05). In conclusion, the study showed that B1 correction could mitigate variations driven by the sensitivity of the qDESS T2 mapping method to B1, therefore increasing the sensitivity to detect real biological changes.</p> <p>The proposed method may improve the robustness of bilateral qDESS T2 mapping, allowing for an accurate and more efficient evaluation of OA pathways and pathophysiology through longitudinal and cross-sectional studies.</p> <p><strong>This repository contains the raw data for reproducing the results reported in the paper. The data are provided in NIFTI format.</strong></p> <p> </p>
Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy
<p><strong>Introduction</strong>. This database includes the radiomic features used as covariates to train machine learning algorithms in the paper “ Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy”.</p> <p><strong>Purpose</strong>. Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning.</p> <p><strong>Method</strong>. Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset.</p> <p><strong>Results</strong>. Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2.</p> <p><strong>Conclusion</strong>. This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.</p>
doi_dedup___::55abb0135a2ecaf2b0ae4ccc426c65f2
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doi_dedup___::7ecf5a7346ad57c09f827a419d6c299a
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