The Increasing Spectrum of Indications of Whole-Body MRI Beyond Oncology: Imaging Answers to Clinical Needs.

Whole-body coverage using MRI was developed almost 2 decades ago. The first applications focused on the investigation of the skeleton to detect neoplastic disease, mainly metastases from solid cancers, and involvement by multiple myeloma and lymphoma. But the extensive coverage of the whole musculoskeletal system, combined with the exquisite sensitivity of MRI to tissue alteration in relation to different pathologic conditions, mainly inflammation, has led to the identification of a growing number of indications outside oncology. Seronegative rheumatisms, systemic sclerosis, inflammatory diseases involving muscles or fascias, and multifocal osseous, vascular, or neurologic diseases represent currently validated or emerging indications of whole-body MRI (WB-MRI). We first illustrate the most valuable indications of WB-MRI in seronegative rheumatisms that include providing significant diagnostic information in patients with negative or ambiguous MRI of the sacroiliac joints and the lumbar spine, assessing disease activity in advanced (ankylosed) central disease, and evaluating the peripherally dominant forms of spondyloarthropathy. Then we review the increasing indications of WB-MRI in other rheumatologic and nonneoplastic disorders, underline the clinical needs, and illustrate the role of WB-MRI in the positive diagnosis and evaluation of disease burden, therapeutic decisions, and treatment monitoring.

Fat Suppression with Dixon Techniques in Musculoskeletal Magnetic Resonance Imaging: A Pictorial Review.

Dixon techniques are part of the methods used to suppress the signal of fat in MRI. They present many advantages compared with other fat suppression techniques including (1) the robustness of fat signal suppression, (2) the possibility to combine these techniques with all types of sequences (gradient echo, spin echo) and different weightings (T1-, T2-, proton density-, intermediate-weighted sequences), and (3) the availability of images both with and without fat suppression from one single acquisition. These advantages have opened many applications in musculoskeletal imaging. We first review the technical aspects of Dixon techniques including their advantages and disadvantages. We then illustrate their applications for the imaging of different body parts, as well as for tumors, neuromuscular disorders, and the imaging of metallic hardware.

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Structured sparsity through reweighting and application to diffusion MRI

We consider the problem of multiple correlated sparse signals reconstruction and propose a new implementation of structured sparsity through a reweighting scheme. We present a particular application for diffusion Magnetic Resonance Imaging data and show how this procedure can be used for fibre orientation reconstruction in the white matter of the brain. In that framework, our structured sparsity prior can be used to exploit the fundamental coherence between fibre directions in neighbour voxels. Our method approaches the ℓ0 minimisation through a reweighted ℓ1-minimisation scheme. The weights are here defined in such a way to promote correlated sparsity between neighbour signals.

On evaluating the accuracy and biological plausibility of diffusion MRI tractograms

In diffusion MRI, traditional tractography algorithms do not recover truly quantitative tractograms and the structural connectivity has to be estimated indirectly by counting the number of fiber tracts or averaging scalar maps along them. Recently, global and efficient methods have emerged to estimate more quantitative tractograms by combining tractography with local models for the diffusion signal, like the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework. In this abstract, we show the importance of using both (i) proper multi-compartment diffusion models and (ii) adequate multi-shell acquisitions, in order to evaluate the accuracy and the biological plausibility of the tractograms.

Accuracy of cognitive MRI-targeted biopsy in hitting prostate cancer-positive regions of interest.

PURPOSE: Prostate cancer (PCa) diagnosis relies on clinical suspicion leading to systematic transrectal ultrasound-guided biopsy (TRUSGB). Multiparametric magnetic resonance imaging (mpMRI) allows for targeted biopsy of suspicious areas of the prostate instead of random 12-core biopsy. This method has been shown to be more accurate in detecting significant PCa. However, the precise spatial accuracy of cognitive targeting is unknown. METHODS: Consecutive patients undergoing mpMRI-targeted TRUSGB with cognitive registration (MRTB-COG) followed by robot-assisted radical prostatectomy were included in the present analysis. The regions of interest (ROIs) involved by the index lesion reported on mpMRI were subsequently targeted by two experienced urologists using the cognitive approach. The 27 ROIs were used as spatial reference. Mapping on radical prostatectomy specimen was used as reference to determine true-positive mpMRI findings. Per core correlation analysis was performed. RESULTS: Forty patients were included. Overall, 40 index lesions involving 137 ROIs (mean ROIs per index lesion 3.43) were identified on MRI. After correlating these findings with final pathology, 117 ROIs (85 %) were considered as true-positive lesions. A total of 102 biopsy cores directed toward such true-positive ROIs were available for final analysis. Cognitive targeted biopsy hit the target in 82 % of the cases (84/102). The only identified risk factor for missing the target was an anterior situated ROI (p = 0.01). CONCLUSION: In experienced hands, cognitive MRTB-COG allows for an accuracy of 82 % in hitting the correct target, given that it is a true-positive lesion. Anterior tumors are less likely to be successfully targeted.

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole- brain suffers from distortions caused by the standard echo- planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a âeurooetheoretically correctâeuro and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.

Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.

Characterisation of anatomical properties of the human basal ganglia using magnetic resonance imaging

The detailed in-vivo characterization of subcortical brain structures is essential not only to understand the basic organizational principles of the healthy brain but also for the study of the involvement of the basal ganglia in brain disorders. The particular tissue properties of basal ganglia - most importantly their high iron content, strongly affect the contrast of magnetic resonance imaging (MRI) images, hampering the accurate automated assessment of these regions. This technical challenge explains the substantial controversy in the literature about the magnitude, directionality and neurobiological interpretation of basal ganglia structural changes estimated from MRI and computational anatomy techniques. My scientific project addresses the pertinent need for accurate automated delineation of basal ganglia using two complementary strategies: ? Empirical testing of the utility of novel imaging protocols to provide superior contrast in the basal ganglia and to quantify brain tissue properties; ? Improvement of the algorithms for the reliable automated detection of basal ganglia and thalamus Previous research demonstrated that MRI protocols based on magnetization transfer (MT) saturation maps provide optimal grey-white matter contrast in subcortical structures compared with the widely used Tl-weighted (Tlw) images (Helms et al., 2009). Under the assumption of a direct impact of brain tissue properties on MR contrast my first study addressed the question of the mechanisms underlying the regional specificities effect of the basal ganglia. I used established whole-brain voxel-based methods to test for grey matter volume differences between MT and Tlw imaging protocols with an emphasis on subcortical structures. I applied a regression model to explain the observed grey matter differences from the regionally specific impact of brain tissue properties on the MR contrast. The results of my first project prompted further methodological developments to create adequate priors for the basal ganglia and thalamus allowing optimal automated delineation of these structures in a probabilistic tissue classification framework. I established a standardized workflow for manual labelling of the basal ganglia, thalamus and cerebellar dentate to create new tissue probability maps from quantitative MR maps featuring optimal grey-white matter contrast in subcortical areas. The validation step of the new tissue priors included a comparison of the classification performance with the existing probability maps. In my third project I continued investigating the factors impacting automated brain tissue classification that result in interpretational shortcomings when using Tlw MRI data in the framework of computational anatomy. While the intensity in Tlw images is predominantly

Myocardial perfusion scintigraphy in the detection of silent ischemia in asymptomatic diabetic patients

OBJECTIVE: This study was aimed to evaluate myocardial perfusion in asymptomatic patients with type 1 (DM1) and type 2 diabetes mellitus (DM2) without previous diagnoses of coronary artery disease (CAD) or cerebral infarction. MATERIALS AND METHODS: Fifty-nine consecutive asymptomatic patients (16 DM1, 43 DM2) underwent myocardial perfusion scintigraphy with 99mTc-sestamibi (MPS). They were evaluated for body mass index, metabolic control of DM, type of therapy, systemic arterial hypertension, dyslipidemia, nephropathy, retinopathy, peripheral neuropathy, smoking, and familial history of CAD. RESULTS: MPS was abnormal in 15 patients (25.4%): 12 (20.3%) with perfusion abnormalities, and 3 with isolated left ventricular dysfunction. The strongest predictors for abnormal myocardial perfusion were: age 60 years and above (p = 0.017; odds ratio [OR] = 6.0), peripheral neuropathy (p = 0.028; OR = 6.1), nephropathy (p = 0.031; OR = 5.6), and stress ECG positive for ischemia (p = 0.049; OR = 4.08). CONCLUSION: Silent myocardial ischemia occurs in more than one in five asymptomatic diabetic patients. The strongest predictors of ischemia in this study were: patient age, peripheral neuropathy, nephropathy, retinopathy and a stress ECG positive for ischemia.

Diffusion-weighted magnetic resonance imaging: biomarker for treatment response in oncology

The authors report a case where a quantitative assessment of the apparent diffusion coefficient (ADC) of liver metastasis in a patient undergoing chemotherapy has shown to be an effective early marker for predicting therapeutic response, anticipating changes in tumor size. A lesion with lower initial ADC value and early increase in such value in the course of the treatment tends to present a better therapeutic response.

Susceptibility weighted imaging: differentiating between calcification and hemosiderin

Objective:To present a detailed explanation on the processing of magnetic susceptibility weighted imaging (SWI), demonstrating the effects of echo time and sensitive mask on the differentiation between calcification and hemosiderin.Materials and Methods:Computed tomography and magnetic resonance (magnitude and phase) images of six patients (age range 41– 54 years; four men) were retrospectively selected. The SWI images processing was performed using the Matlab’s own routine.Results:Four out of the six patients showed calcifications at computed tomography images and their SWI images demonstrated hyperintense signal at the calcification regions. The other patients did not show any calcifications at computed tomography, and SWI revealed the presence of hemosiderin deposits with hypointense signal.Conclusion:The selection of echo time and of the mask may change all the information on SWI images, and compromise the diagnostic reliability. Amongst the possible masks, the authors highlight that the sigmoid mask allows for contrasting calcifications and hemosiderin on a single SWI image.