MRI's transverse relaxation time (T2) is sensitive to tissues' composition and pathological state. While variations in T2 values can be used as clinical biomarkers, it is challenging to quantify this parameter in vivo due to the complexity of the MRI signal model, differences in protocol implementations, and hardware imperfections. Herein, we provide a detailed analysis of the echo modulation curve (EMC) platform, offering accurate and reproducible mapping of T2 values, from 2D multi‐slice multi‐echo spin‐echo (MESE) protocols. Computer simulations of the full Bloch equations are used to generate an advanced signal model, which accounts for stimulated echoes and transmit field (B1+) inhomogeneities. In addition to quantifying T2 values, the EMC platform also provides proton density (PD) maps, and fat‐water fraction maps. The algorithm's accuracy, reproducibility, and insensitivity to T1 values are validated on a phantom constructed by the National Institute of Standards and Technology and on in vivo human brains. EMC‐derived T2 maps show excellent agreement with ground truth values for both in vitro and in vivo models. Quantitative values are accurate and stable across scan settings and for the physiological range of T2 values, while showing robustness to main field (B0) inhomogeneities, to variations in T1 relaxation time, and to magnetization transfer. Extension of the algorithm to two‐component fitting yields accurate fat and water T2 maps along with their relative fractions, similar to a reference three‐point Dixon technique. Overall, the EMC platform allows to generate accurate and stable T2 maps, with a full brain coverage using a standard MESE protocol and at feasible scan times. The utility of EMC‐based T2 maps was demonstrated on several clinical applications, showing robustness to variations in other magnetic properties. The algorithm is available online as a full stand‐alone package, including an intuitive graphical user interface.

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Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI’s transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot–Marie–Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.

MRI's T2 relaxation time is a valuable biomarker for neuromuscular disorders and muscle dystrophies. One of the hallmarks of these pathologies is the infiltration of adipose tissue and a loss of muscle volume. This leads to a mixture of two signal components, from fat and from water, to appear in each imaged voxel, each having a specific T2 relaxation time. In this proof‐of‐concept work, we present a technique that can separate the signals from water and from fat within each voxel, measure their separate T2 values, and calculate their relative fractions. The echo modulation curve (EMC) algorithm is a dictionary‐based technique that offers accurate and reproducible mapping of T2 relaxation times. We present an extension of the EMC algorithm for estimating subvoxel fat and water fractions, alongside the T2 and proton‐density values of each component. To facilitate data processing, calf and thigh anatomy were automatically segmented using a fully convolutional neural network and FSLeyes software. The preprocessing included creating two signal dictionaries, for water and for fat, using Bloch simulations of the prospective protocol. Postprocessing included voxelwise fitting for two components, by matching the experimental decay curve to a linear combination of the two simulated dictionaries. Subvoxel fat and water fractions and relaxation times were generated and used to calculate a new quantitative biomarker, termed viable muscle index, and reflecting disease severity. This biomarker indicates the fraction of remaining muscle out of the entire muscle region. The results were compared with those using the conventional Dixon technique, showing high agreement (R = 0.98, p < 0.001). It was concluded that the new extension of the EMC algorithm can be used to quantify abnormal fat infiltration as well as identify early inflammatory processes corresponding to elevation in the T2 value of the water (muscle) component. This new ability may improve the diagnostic accuracy of neuromuscular diseases, help stratification of patients according to disease severity, and offer an efficient tool for tracking disease progression.

The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.

Although EPI phase images are useful (e.g. for Quantitative Susceptibility Mapping), they often contain phase inconsistencies in the slice-select direction which persist and can degrade QSM results. Here, we analysed three EPI datasets in healthy volunteers to characterise these phase inconsistencies and understand whether they occur or interact with interleaved or sequential slice acquisition order. We characterised a ~2Hz cryogen pump artifact in sequential data and slice-to-slice phase jumps in interleaved data. We modified a previously proposed QSM processing pipeline, including 2D (VSHARP) and 3D (PDF) background field removal that removed all through-slice artifacts observed.

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