Ali Syed, MD

Abstract

BACKGROUND: Quantitative T2 * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T2 * estimates.PURPOSE: To develop and evaluate a motion-robust, simultaneous cardiac and liver T2 * imaging approach using non-Cartesian, rosette sampling and a model-based reconstruction as compared to clinical-standard Cartesian MRI.STUDY TYPE: Prospective.PHANTOM/POPULATION: Six ferumoxytol-containing phantoms (26-288mug/mL). Eight healthy subjects and 18 patients referred for clinically indicated iron overload assessment.FIELD STRENGTH/SEQUENCE: 1.5T, 2D Cartesian and rosette gradient echo (GRE) ASSESSMENT: GRE T2 * values were validated in ferumoxytol phantoms. In healthy subjects, test-retest and spatial coefficient of variation (CoV) analysis was performed during three breathing conditions. Cartesian and rosette T2 * were compared using correlation and Bland-Altman analysis. Images were rated by three experienced radiologists on a 5-point scale.STATISTICAL TESTS: Linear regression, analysis of variance (ANOVA), and paired Student's t-testing were used to compare reproducibility and variability metrics in Cartesian and rosette scans. The Wilcoxon rank test was used to assess reader score comparisons and reader reliability was measured using intraclass correlation analysis.RESULTS: Rosette R2* (1/T2 *) was linearly correlated with ferumoxytol concentration (r2 = 1.00) and not significantly different than Cartesian values (P = 0.16). During breath-holding, ungated rosette liver and heart T2 * had lower spatial CoV (liver: 18.49.3% Cartesian, 8.8%3.4% rosette, P = 0.02, heart: 37.7%14.3% Cartesian, 13.4%1.7% rosette, P = 0.001) and higher-quality scores (liver: 3.3 [3.0-3.6] Cartesian, 4.7 [4.1-4.9] rosette, P = 0.005, heart: 3.0 [2.3-3] Cartesian, 4.5 [3.8-5.0] rosette, P = 0.005) compared to Cartesian values. During free-breathing and failed breath-holding, Cartesian images had very poor to average image quality with significant artifacts, whereas rosette remained very good, with minimal artifacts (P = 0.001).DATA CONCLUSION: Rosette k-sampling with a model-based reconstruction offers a clinically useful motion-robust T2 * mapping approach for iron quantification.

View details for DOI 10.1002/jmri.27196

View details for PubMedID 32452088

Abstract

Diffusion-weighted imaging (DWI) is common for evaluating pediatric musculoskeletal lesions, but suffers from geometric distortion and intense acoustic noise.To investigate the performance of a near-silent and distortion-free DWI sequence (DW-SD) relative to standard echo-planar DWI (DW-EPI) in pediatric extremity MRI.Prospective validation study.Thirty-nine children referred for extremity MRI.DW-EPI and DW-SD, based on a rotating ultrafast sequence modified with sinusoidal diffusion preparation gradients, at 3T.DW-SD image quality (Sanat ) was assessed from 0 (nondiagnostic) to 5 (outstanding) and comparative image quality (Scomp ) (from -2 = DW-EPI more delineated to +2 = DW-SD more delineated, 0 = same). ADC measured by DW-SD and DW-EPI were compared in bone marrow, muscle, and lesions.Wilcoxon rank-sum test and confidence interval of proportions (CIOP) were calculated for Scomp , Student's t-test, coefficient of variation (COV), and Bland-Altman analysis for ADC values, and intraclass correlation coefficient (ICC) for interreader agreement.DW-SD and DW-EPI ADC values for bone marrow, muscle, and lesions were not significantly different (P = 0.3, P = 0.2, and P = 0.27, respectively) and had an overall ADC COV of 14.8% (95% confidence interval: 12.3%, 16.9%) and no significant proportional bias on Bland-Altman analysis. Sanat CIOP was rated diagnostic or better (score of 3, 4, or 5) in 72-98% of cases for bone marrow, muscle, and soft tissues. DW-SD was equivalent to or preferred over DW-EPI in muscles and soft tissues, with CIOP 86-93% and 93%, respectively. Lesions were equally visualized on DW-SD and DW-EPI in 40-51%, with DW-SD preferred in 44-56% of cases. DW-SD was rated significantly better than DW-EPI across all comparative variables that included bone marrow, muscle, soft tissue, cartilage, and lesions (P<0.05). Readers had moderate to near-perfect (ICC range = 0.45-0.85).DW-SD of the extremities provided similar ADC values and improved image quality compared with conventional DW-EPI.2 TECHNICAL EFFICACY STAGE: 2.

View details for DOI 10.1002/jmri.27330

View details for PubMedID 32815203

View details for Web of Science ID 000578080300058

Abstract

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.

View details for DOI 10.1109/isbi45749.2020.9098735

View details for PubMedID 33274013

View details for PubMedCentralID PMC7710391