Motion Compensated Unsupervised Deep Learning for 5D MRI

We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated free-breathing 5D MRI simplifies the scan planning, improves patient comfort, and offers several clinical benefits over breath-held 2D exams, including isotropic spatial resolution and the ability to reslice the data to arbitrary views. However, the current reconstruction algorithms for 5D MRI take very long computational time, and their outcome is greatly dependent on the uniformity of the binning of the acquired data into different physiological phases. The proposed algorithm is a more data-efficient alternative to current motion-resolved reconstructions. This motion-compensated approach models the data in each cardiac/respiratory bin as Fourier samples of the deformed version of a 3D image template. The deformation maps are modeled by a convolutional neural network driven by the physiological phase information. The deformation maps and the template are then jointly estimated from the measured data. The cardiac and respiratory phases are estimated from 1D navigators using an auto-encoder. The proposed algorithm is validated on 5D bSSFP datasets acquired from two subjects.Comment: MICCAI 2023 conference pape

Cardiac MRI Imaging Features of Erdheim–Chester Disease: A Case Review

Erdheim–Chester disease (ECD) is a disease of non-Langerhans cell histiocyte multisystemic proliferation. The pathogenesis is related to accumulation of histiocytes across the body, leading to multiple organ failure, and thus necessitating an early diagnosis. In all ECD cases, BRAF and RAS mutations are critical. Clinical manifestations affect individuals between the fourth and seventh decades of life. The most common symptoms reported are central nervous system involvement with functional disability, and bone pain with osteosclerosis of long bones. Other reported symptoms are skin involvement with xanthelasma, diabetes insipidus, cardiovascular involvement with pericardial effusion and tamponade, perivascular thickening, and right atrial and atrioventricular grove infiltration, leading to heart failure. Females may develop galactorrhoea due to deposition in the pituitary gland, with or without menstrual irregularities. Only few publications address the cardiac MRI findings of ECD. The authors present a case of cardiac involvement of ECD and associated cardiac MRI findings. The patient presented with multisystemic disease with bone pain, diplopia, cardiac arrythmia, and dyspnoea

Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension

The role of reliable, non-invasive imaging-based recognition of pulmonary hypertension (PH) remains a diagnostic challenge. The aim of the current pilot radiomics study was to assess the diagnostic performance of cardiac MRI (cMRI)-based texture features to accurately predict PH. The study involved IRB-approved retrospective analysis of cMRIs from 72 patients (42 PH and 30 healthy controls) for the primary analysis. A subgroup analysis was performed including patients from the PH group with left ventricle ejection fraction ≥ 50%. Texture features were generated from mid-left ventricle myocardium using balanced steady-state free precession (bSSFP) cine short-axis imaging. Forty-five different combinations of classifier models and feature selection techniques were evaluated. Model performance was assessed using receiver operating characteristic curves. A multilayer perceptron model fitting using full feature sets was the best classifier model for both the primary analysis (AUC 0.862, accuracy 78%) and the subgroup analysis (AUC 0.918, accuracy 80%). Model performance demonstrated considerable variation between the models (AUC 0.523–0.918) based on the chosen model–feature selection combination. Cardiac MRI-based radiomics recognition of PH using texture features is feasible, even with preserved left ventricular ejection fractions