Purpose To develop a bipolar multi‐echo MRI method for the accurate estimation of the adipose tissue fatty acid composition (FAC) using clinically relevant protocols at clinical field strength. Methods The proposed technique jointly estimates confounding factors (field map, R2*, eddy‐current phase) and triglyceride saturation state parameters by fitting multi‐echo gradient echo acquisitions to a complex signal model. The noise propagation behavior was improved by applying a low‐rank enforcing denoising technique and by addressing eddy‐current‐induced phase discrepancies analytically. The impact of the total echo train duration on the FAC parameter map accuracy was analyzed in an oil phantom at 3T. Accuracy and reproducibility assessment was based on in vitro oil phantom measurements at two field strengths (3T and 1.5T) and with two different protocols. Repeatability was assessed in vivo in patients (n = 8) with suspected fatty liver disease using test–retest acquisitions in the abdominal subcutaneous, perirenal and mesenteric fat depots. Results Echo train readout durations of at least five times the conventional in‐phase time were required for accurate FAC estimation in areas of high fat content. In vitro, linear regression and Bland–Altman analyses demonstrated strong (r > 0.94) and significant (P ≪ 0.01) correlations between measured and reference FACs for all acquisitions, with smaller overall intercepts and biases at 3T compared to 1.5T. In vivo, reported mean absolute differences between test and retest were 1.54%, 3.31%, and 2.63% for the saturated, mono‐unsaturated, and poly‐unsaturated fat component, respectively. Conclusions Accurate and reproducible MRI‐based FAC quantification within a breath‐hold is possible at clinical field strengths.

Purpose To develop a free‐breathing hepatic fat and quantification method by extending a previously described stack‐of‐stars model‐based fat‐water separation technique with additional modeling of the transverse relaxation rate . Methods The proposed technique combines motion‐robust radial sampling using a stack‐of‐stars bipolar multi‐echo 3D GRE acquisition with iterative model‐based fat‐water separation. Parallel‐Imaging and Compressed‐Sensing principles are incorporated through modeling of the coil‐sensitivity profiles and enforcement of total‐variation (TV) sparsity on estimated water, fat, and parameter maps. Water and fat signals are used to estimate the confounder‐corrected proton‐density fat fraction (PDFF). Two strategies for handling respiratory motion are described: motion‐averaged and motion‐resolved reconstruction. Both techniques were evaluated in patients ( n = 14) undergoing a hepatobiliary research protocol at 3T. PDFF and parameter maps were compared to a breath‐holding Cartesian reference approach. Results Linear regression analyses demonstrated strong ( r > 0.96) and significant ( P ≪ .01) correlations between radial and Cartesian PDFF measurements for both the motion‐averaged reconstruction (slope: 0.90; intercept: 0.07%) and the motion‐resolved reconstruction (slope: 0.90; intercept: 0.11%). The motion‐averaged technique overestimated hepatic values (slope: 0.35; intercept: 30.2 1/s) compared to the Cartesian reference. However, performing a respiratory‐resolved reconstruction led to better value consistency (slope: 0.77; intercept: 7.5 1/s). Conclusions The proposed techniques are promising alternatives to conventional Cartesian imaging for fat and quantification in patients with limited breath‐holding capabilities. For accurate estimation, respiratory‐resolved reconstruction should be used.

Movement analysis of infants’ body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.

Implants used to correct pathological varus-valgus deformities (VVD) and leg length discrepancies (LLD) may not be optimized for the specific treatment, as suggested by their off-label use. Detailed analysis of this issue has been limited by the poorly understood mechanical behavior of the growing physis and ignorance of the loads acting on the implants. The aim of this study was to predict and compare the loading conditions of a growth modulation implant in VVD and LLD treatments. Idealized finite element (FE) models of the juvenile distal femur treated with the Eight-Plate implant were developed for VVD and LLD. Bone growth was simulated using thermal strains. The axial force in the plate was compared between the two treatments. Case-specific plate forces were predicted by virtually reproducing the screw deformation visible on radiographs of LLD (N = 4) and VVD (N = 4) clinical cases. The simple FE models reproduced the clinical implant deformations well. The resulting forces ranged from 129 to 580 N for the VVD patients. For LLD, this range was from 295 to 1002 N per plate, that is, 590-2004 N for the entire physis. The higher forces in LLD could be explained by restricted screw divergence in the double-sided implant application. For the first time, the loading conditions of a growth modulation implant were investigated and compared between two treatments by FE analyses, and the range of case-specific loads was predicted. These simulation tools may be utilized for guiding appropriate usage and for efficient development of implants. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1398-1405, 2018.

Artificial intelligence (AI) systems, especially those employing machine learning methods, are often considered black boxes, that is, systems whose inner workings and decisional logics remain fundamentally opaque to human understanding. In this article, we set out to clarify what the new General Data Protection Regulation (GDPR) says on profiling and automated decision-making employing opaque systems. More specifically, we focus on the application of such systems in the domain of healthcare. We conducted a conceptual analysis of the notion of opacity (black box) using concrete examples of existing or envisaged medical applications. Our analysis distinguishes among three forms of opacity: (i) lack of disclosure, (ii) epistemic opacity, and (iii) explanatory opacity. For each type of opacity, we discuss where it originates from, and how it can be dealt with according to the GDPR in the context of healthcare. This analysis can offer insights regarding the contested issue of the explainability of AI systems in medicine, and its potential effects on the patient-doctor relationship.

Objective: In MRI-based radiation therapy planning, mitigating patient-specific distortion with standard high bandwidth scans can result in unnecessary sacrifices of signal to noise ratio (SNR). This study investigates a technique for distortion detection and mitigation on a patient specific basis. Approach: Fast B0 mapping was performed using a previously developed technique for high-resolution, large dynamic range field mapping without the need for phase unwrapping algorithms. A phantom study was performed to validate the method. Distortion mitigation was validated by reducing geometric distortion with increased acquisition bandwidth (BW) and confirmed by both the B0 mapping technique and manual measurements. Images and contours from 25 brain stereotactic radiosurgery (SRS) patients and 95 targets were analyzed to estimate the range of geometric distortions expected in the brain and to estimate bandwidth required to keep all treatment targets within the ± 0.5 mm iso-distortion contour. Main Results: The phantom study showed, at 3T, the technique can measure distortions with a mean absolute error of 0.12 mm (0.18 ppm), and a maximum error of 0.37 mm (0.6 ppm). For image acquisition at 3T and 1.0 mm resolution, mean absolute distortion under 0.5 mm in patients required bandwidths from 109 to 200 Hz/px for patients with the least and most distortion, respectively. Maximum absolute distortion under 0.5 mm required bandwidths from 120 to 390 Hz/px. Significance: The method for B0 mapping was shown to be valid and may be applied to assess distortion clinically. Future work will adapt the readout bandwidth to prospectively mitigate distortion with the goal to improve radiosurgery treatment outcomes by reducing healthy tissue exposure.

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Whole-liver [Formula: see text] mapping with high accuracy and precision is feasible in one breath-hold using spatiotemporally incoherent, sparse 3D Cartesian sampling.