Purpose: Multicomponent analysis of MRI T 2 relaxation time (mcT 2 ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T 2 values. This voxel-based approach is challenging due to the large ambiguity in the multi-T 2 space and the low SNR of MRI signals. Herein, we present a data-driven mcT 2 analysis, which utilizes the statistical strength of identifying spatially global mcT 2 motifs in white matter segments before deconvolving the local signal at each voxel.Methods: Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT 2 motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps. Results: Validations are shown for computer-generated signals, uniquely designed subvoxel mcT 2 phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT 2 phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. Conclusion:We conclude that studying global tissue motifs prior to performing voxel-wise mcT 2 analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T 2 space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.
Background Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast‐weighted images, where pathology is detected once it reached a certain visual threshold. Computer‐aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology. Purpose To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network. Study Type Prospective. Population A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions. Field Strength/Sequence A 3.0 T, T2‐weighted multi‐echo spin‐echo (MESE) sequence. Assessment MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T2 values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T2). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels. Statistical Tests Diagnostic performance of the two approaches was compared using z‐tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P‐value <0.05 was considered statistically significant. Results ORs of identifying pathology were significantly higher for CAD vis‐à‐vis visual inspection for all lesions' severity levels. For a 6% change in T2 value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant. Data Conclusion CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study. Level of Evidence 1 Technical Efficacy Stage 3
High-resolution animal imaging is an integral part of preclinical drug development and the investigation of diseases' pathophysiology. Quantitative mapping of T 2 relaxation times (qT 2 ) is a valuable tool for both preclinical and research applications, providing high sensitivity to subtle tissue pathologies. High-resolution T 2 mapping, however, suffers from severe underestimation of T 2 values due to molecular diffusion. This affects both single-echo and multi-echo spin echo (SSE and MESE), on top of the well-known contamination of MESE signals by stimulated echoes, and especially on high-field and preclinical scanners in which high imaging gradients are used in comparison to clinical scanners. Methods: Diffusion bias due to imaging gradients was analyzed by quantifying the effective b-value for each coherence pathway in SSE and MESE protocols, and incorporating this information in a joint T 2 -diffusion reconstruction algorithm. Validation was done on phantoms and in vivo mouse brain using a 9.4T and a 7T MRI scanner.Results: Underestimation of T 2 values due to strong imaging gradients can reach up to 70%, depending on scan parameters and on the sample's diffusion coefficient. The algorithm presented here produced T 2 values that agreed with reference spectroscopic measurements, were reproducible across scan settings, and reduced the average bias of T 2 values from −33.5 ± 20.5% to −0.1 ± 3.6%. Conclusions:A new joint T 2 -diffusion reconstruction algorithm is able to negate imaging gradient-related underestimation of T 2 values, leading to reliable mapping of T 2 values at high resolutions.
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