Free-breathing late gadolinium enhancement CMR with a fixed short scan time using CosMo

To evaluate the performance of compressed sensing for motion correction (CosMo) [1] in compensating the respiratory motion of the heart in 3D late gadolinium enhancement (LGE) CMR

Left Atrial scar assessment using imaging with isotropic spatial resolution and compressed sensing

Summary. We assess left atrial scar using late gadolinium enhancement (LGE) with isotropic spatial resolution of $1.4^3 mm^3$ by using highly accelerated LOST [1] reconstruction. Background. Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia [2]. Pulmonary vein isolation (PVI) using radiofrequency (RF)-ablation is the leading treatment for AF. Recently, LGE imaging of the LA has been used to identify atrial wall scar due to RF-ablation [3]. However, current LGE methods have limited spatial resolution that substantially impact assessment of scar in the complex geometry of PVs and LA. In this study, we sought to utilize prospective random k-space sampling and LOST [1] for accelerated LGE imaging, where reduction in imaging time was traded-off for improved isotropic spatial-resolution. Methods. 23 patients with history of AF (6 females, $58.1 \pm 9.6$ years, 9 pre-PVI, 2 with history of PVI; 8 post-PVI; 3 with both pre and post-PVI) were recruited for this study. LGE images were acquired 10-to-20 minutes after bolus infusion of 0.2 mmol/kg Gd-DTPA. Free-breathing ECG-triggered navigator-gated inversion-recovery GRE sequences were used for all acquisitions ($TR/TE/ \alpha = 5.2/2.6ms/25°, FOV=320×320×100mm$). The PV inflow artifact reduction technique in [4] was also utilized. For each patient, a standard non-isotropic 3D LGE scan ($1.4×1.4×4.0mm^3$) and a 3-fold-accelerated highresolution 3D LGE scan ($1.4^3 mm^3$) were performed, with randomized acquisition order. For random undersampling, central k-space (45×35 in ky-kz) was fullysampled, edges randomly discarded, and phase reordering performed as in [5]. Acquisition times were ~4 mins assuming 100% scan-efficiency at 70bpm for both scans. All undersampled data were reconstructed offline using LOST [1]. LOST-reconstructed high-resolution, and standard LGE images were scored by two blinded readers for diagnostic value, presence of LGE(yes/no); and image quality in axial(Ax), coronal(Co) and sagittal (Sa) views (1=poor,4=excellent). Results. Three cases were declared non-diagnostic due to contrast-washout and imperfect inversion-time. LGE was visually present in 14 of the remaining 20 patients based on standard-LGE images, and 12 based on LOST-reconstructed ones (disagreement on one pre- and one postPVI patient). Figure 1 and 2 show comparisons of isotropic vs. non-isotropic LGE images in two patients. Image scores for LOST-LGE: $Ax=2.90 \pm 0.70, Sa=3.33 \pm 0.66, Co=3.00 \pm 0.63$; and standard LGE: $Ax=3.76 \pm 0.54, Sa=2.48 \pm 0.60, Co=2.24 \pm 0.44$, where differences were significant in all views. Conclusions. LOST allows isotropic spatial-resolution in LGE for assessment of LA and PV scar. Isotropic resolution allows reformatting LGE images in any orientation and facilitates assessment of scar. Further clinical study is needed to assess if the improved spatial resolution will impact the diagnostic interpretation of data

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

Myocardial Approximate Spin-lock Dispersion Mapping using a Simultaneous T2 and TRAFF2 Mapping at 3T MRI

Ischemic heart disease (IHD) is one of the leading causes of death worldwide. Myocardial infarction (MI) represents a third of all IHD cases, and cardiac magnetic resonance imaging (MRI) is often used to assess its damage to myocardial viability. Late gadolinium enhancement (LGE) is the current gold standard, but the use of gadolinium-based agents limits the clinical applicability in some patients. Spin-lock (SL) dispersion has recently been proposed as a promising non-contrast biomarker for the assessment of MI. However, at 3T, the required range of SL preparations acquired at different amplitudes suffers from specific absorption rate (SAR) limitations and off-resonance artifacts. Relaxation Along a Fictitious Field (RAFF) is an alternative to SL preparations with lower SAR requirements, while still sampling relaxation in the rotating frame. In this study, a single breath-hold simultaneous TRAFF2 and T2 mapping sequence is proposed for SL dispersion mapping at 3T. Excellent reproducibility (coefficient of variations lower than 10%) was achieved in phantom experiments, indicating good intrascan repeatability. The average myocardial TRAFF2, T2, and SL dispersion obtained with the proposed sequence (68.0±10.7 ms, 44.0±4.0 ms, and 0.4±0.2 ×10-4 s2, respectively) were comparable to the reference methods (62.7±11.7 ms, 41.2±2.4 ms, and 0.3±0.2x 10-4s2, respectively). High visual map quality, free of B0 and B1+ related artifacts, for T2, TRAFF2, and SL dispersion maps were obtained in phantoms and in vivo, suggesting promise in clinical use at 3T. Clinical relevance - and imaging promises non-contrast assessment of scar and focal fibrosis in a single breath-hold using approximate spin-lock dispersion mapping

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo