The connection between distortion risk measures and ordered weighted averaging operators

Distortion risk measures summarize the risk of a loss distribution by means of a single value. In fuzzy systems, the Ordered Weighted Averaging (OWA) and Weighted Ordered Weighted Averaging (WOWA) operators are used to aggregate a large number of fuzzy rules into a single value. We show that these concepts can be derived from the Choquet integral, and then the mathematical relationship between distortion risk measures and the OWA and WOWA operators for discrete and finite random variables is presented. This connection offers a new interpretation of distortion risk measures and, in particular, Value-at-Risk and Tail Value-at-Risk can be understood from an aggregation operator perspective. The theoretical results are illustrated in an example and the degree of orness concept is discussed.

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.

Application of intravoxel incoherent motion perfusion imaging to shoulder muscles after a lift-off test of varying duration.

Intravoxel incoherent motion (IVIM) MRI is a method to extract microvascular blood flow information out of diffusion-weighted images acquired at multiple b-values. We hypothesized that IVIM can identify the muscles selectively involved in a specific task, by measuring changes in activity-induced local muscular perfusion after exercise. We tested this hypothesis using a widely used clinical maneuver, the lift-off test, which is known to assess specifically the subscapularis muscle functional integrity. Twelve shoulders from six healthy male volunteers were imaged at 3 T, at rest, as well as after a lift-off test hold against resistance for 30 s, 1 and 2 min respectively, in three independent sessions. IVIM parameters, consisting of perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient D* and blood flow-related fD*, were estimated within outlined muscles of the rotator cuff and the deltoid bundles. The mean values at rest and after the lift-off tests were compared in each muscle using a one-way ANOVA. A statistically significant increase in fD* was measured in the subscapularis, after a lift-off test of any duration, as well as in D. A fD* increase was the most marked (30 s, +103%; 1 min, +130%; 2 min, +156%) and was gradual with the duration of the test (in 10(-3) mm(2) /s: rest, 1.41 ± 0.50; 30 s, 2.86 ± 1.17; 1 min, 3.23 ± 1.22; 2 min, 3.60 ± 1.21). A significant increase in fD* and D was also visible in the posterior bundle of the deltoid. No significant change was consistently visible in the other investigated muscles of the rotator cuff and the other bundles of the deltoid. In conclusion, IVIM fD* allows the demonstration of a task-related microvascular perfusion increase after a specific task and suggests a direct relationship between microvascular perfusion and the duration of the effort. It is a promising method to investigate non-invasively skeletal muscle physiology and clinical perfusion-related muscular disorders.

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.