Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon Tiramisu segmentation engine, our proposed deep networks use variational and specially designed targeted dropouts for faster and robust convergence, and utilize multi-contrast MRI scans as input data. In our experiments, we have used 150 scans from 50 distinct subjects from the Baltimore Longitudinal Study of Aging (BLSA). The proposed system made use of both labeled and unlabeled data with high efficacy for training, and outperformed the current state-of-the-art methods with dice scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% for muscle, fat, IMAT, bone, and bone marrow tissues, respectively. Our results indicate that the proposed system can be useful for clinical research studies where volumetric and distributional tissue quantification is pivotal and labeling is a significant issue. To the best of our knowledge, the proposed system is the first attempt at multi-tissue segmentation using a single end-to-end semi-supervised deep learning framework for multi-contrast thigh MRI scans.Comment: 20 pages, 9 figures, Journal of Signal Processing System

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams

Diffusion-tensor MRI methods to study and evaluate muscle architecture

The thesis describes the development of various approaches for measuring muscle architectural parameters using Diffusion Tensor MR Imaging (DTI). It also illustrates how to apply them to study changes in muscle architecture after an injury prevention program.In Chapter 2, because manual segmentation of muscles is cumbersome, we validated a semi-automatic framework for estimating DTI indices in upper leg muscles. This method reduced segmentation time by a factor of three in a cross-sectional study design and can be used fully automatically in a longitudinal assessment of changes in DTI indices.Chapter 3 was a feasibility study measuring fiber orientation changes with DTI in calf muscles and sub-compartments of the Soleus and Tibialis Anterior during plantarflexion and dorsiflexion. Differences in fiber orientations corresponded to the known agonist-antagonist function of the muscles. This shows that DTI can be utilized to assess changes in muscle orientation due to posture or training.In Chapter 4, we compared DTI fiber tractography for Vastus Lateralis fiber architecture assessment with 3D ultrasonography (3D-US). We discovered that both methods have their advantages and disadvantages, with the agreement between the two techniques being moderate.Finally, in Chapter 5, we examined the effects of a hamstring injury prevention exercise on the muscle architectural parameters of basketball players. DTI was employed to quantify changes in fiber orientation and length using tractography and fiber orientation maps. It was observed that the Semitendinosus fascicle length increased after the Nordics exercise, while the Biceps Femoris long head fiber orientation decreased following the Divers intervention

Imaging biomarkers in the idiopathic inflammatory myopathies

Idiopathic inflammatory myopathies (IIMs) are a group of acquired muscle diseases with muscle inflammation, weakness, and other extra-muscular manifestations. IIMs can significantly impact the quality of life, and management of IIMs often requires a multi-disciplinary approach. Imaging biomarkers have become an integral part of the management of IIMs. Magnetic resonance imaging (MRI), muscle ultrasound, electrical impedance myography (EIM), and positron emission tomography (PET) are the most widely used imaging technologies in IIMs. They can help make the diagnosis and assess the burden of muscle damage and treatment response. MRI is the most widely used imaging biomarker of IIMs and can assess a large volume of muscle tissue but is limited by availability and cost. Muscle ultrasound and EIM are easy to administer and can even be performed in the clinical setting, but they need further validation. These technologies may complement muscle strength testing and laboratory studies and provide an objective assessment of muscle health in IIMs. Furthermore, this is a rapidly progressing field, and new advances are going to equip care providers with a better objective assessment of IIMS and eventually improve patient management. This review discusses the current state and future direction of imaging biomarkers in IIMs

Refinement of quantitative MRI as an outcome measure in inherited neuropathies

Recent acceleration in discovery of potential drug treatments for inherited neuromuscular diseases (NMD) heralds the urgent need for scientifically sound clinical trials. Given the rarity and slow progression of most of these conditions, simply increasing sample size, or extending trial duration to increase study power, are not viable options. Trials in NMD are in desperate need of highly responsive outcome measures. Lower limb muscle quantitative MRI (qMRI) allows non-invasive assessment of sequelae of nerve and muscle disease. It has recently been shown to be reliable, valid and responsive in a number of NMD, but further refinement is vital in order to ensure the ability to undertake rigorous and meaningful clinical trials in small cohorts of patients with rare and slowly progressive diseases, over short durations. This thesis aims to examine and improve qMRI responsiveness for application in trials for NMD. Two separate inherited neuropathies have been studied. In Charcot-Marie-Tooth disease type 1A (CMT1A), extended follow up has revealed that qMRI has large internal responsiveness over five years, measuring significant fat fraction (FF) change of 0.7 ± 0.6%/year with standardised response mean (SRM) of 1.07 over 5 years. Excellent validity of qMRI-determined FF is confirmed by strong correlation with clinical measures at baseline, and longitudinal validity is demonstrated for the first time in CMT1A with strong correlation with changes in CMT examination score and remaining muscle area. In Hereditary Sensory Neuropathy type 1, qMRI measures significant FF change at all anatomical levels examined, with large responsiveness at calf levels (distal calf FF change 2.2 ± 2.7%, SRM=0.83; proximal calf FF change 2.6 ± 3.0%, SRM=0.84 over 12 months). In both diseases, significant muscle fat gradients are shown to exist with the potential to devastate or enhance longitudinal analysis. FF change is predicted by baseline FF and other MRI measures in both diseases. Improvement in qMRI responsiveness is demonstrated through a host of evidence-based manipulations aimed at maximising and homogenising mean FF change. Quantitative MRI determined FF is shown to be highly responsive as an outcome measure in two different inherited neuropathies, and is ready to be used as a primary outcome measure in drug trials for rare neuromuscular diseases

Quantitative MRI Musculoskeletal Techniques: An Update.

OBJECTIVE. For many years, MRI of the musculoskeletal system has relied mostly on conventional sequences with qualitative analysis. More recently, using quantitative MRI applications to complement qualitative imaging has gained increasing interest in the MRI community, providing more detailed physiologic or anatomic information. CONCLUSION. In this article, we review the current state of quantitative MRI, technical and software advances, and the most relevant clinical and research musculoskeletal applications of quantitative MRI

Reference values for volume, fat content and shape of the hip abductor muscles in healthy individuals from Dixon MRI

Healthy hip abductor muscles are a good indicator of a healthy hip and an active lifestyle, as they are greatly involved in human daily activities. Fatty infiltration and muscle atrophy are associated with loss of strength, loss of mobility and hip disease. However, these variables have not been widely studied in this muscle group. We aimed to characterize the hip abductor muscles in a group of healthy individuals to establish reference values for volume, intramuscular fat content and shape of this muscle group. To achieve this, we executed a cross-sectional study using Dixon MRI scans of 51 healthy subjects. We used an automated segmentation method to label GMAX, GMED, GMIN and TFL muscles, measured normalized volume (NV) using lean body mass, fat fraction (FF) and lean muscle volume for each subject and computed non-parametric statistics for each variable grouped by sex and age. We measured these variables for each axial slice and created cross-sectional area and FF axial profiles for each muscle. Finally, we generated sex-specific atlases with FF statistical images. We measured median (IQR) NV values of 12.6 (10.8-13.8), 6.3 (5.6-6.7), 1.6 (1.4-1.7) and 0.8 (0.6-1.0) cm3/kg for GMAX, GMED, GMIN and TFL, and median (IQR) FF values of 12.3 (10.1-15.9)%, 9.8 (8.6-11.2)%, 10.0 (9.0-12.0)% and 10.2 (7.8-13.5)% respectively. FF values were significantly higher for females for the four muscles (p < 0.01), but there were no significant differences between the two age groups. When comparing individual muscles, we observed a significantly higher FF in GMAX than in the other muscles. The reported novel reference values and axial profiles for volume and FF of the hip abductors, together with male and female atlases, are tools that could potentially help to quantify and detect early the deteriorating effects of hip disease or sarcopenia

A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy

Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semi-automated system to quantify MRI biomarkers of GRMD