Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data

Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A common strategy among DL methods is the physics-based approach, where a regularized iterative algorithm alternating between data consistency and a regularizer is unrolled for a finite number of iterations. This unrolled network is then trained end-to-end in a supervised manner, using fully-sampled data as ground truth for the network output. However, in a number of scenarios, it is difficult to obtain fully-sampled datasets, due to physiological constraints such as organ motion or physical constraints such as signal decay. In this work, we tackle this issue and propose a self-supervised learning strategy that enables physics-based DL reconstruction without fully-sampled data. Our approach is to divide the acquired sub-sampled points for each scan into training and validation subsets. During training, data consistency is enforced over the training subset, while the validation subset is used to define the loss function. Results show that the proposed self-supervised learning method successfully reconstructs images without fully-sampled data, performing similarly to the supervised approach that is trained with fully-sampled references. This has implications for physics-based inverse problem approaches for other settings, where fully-sampled data is not available or possible to acquire.Comment: 5 Pages, 5 Figure

Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI

Purpose: To develop an improved self-supervised learning strategy that efficiently uses the acquired data for training a physics-guided reconstruction network without a database of fully-sampled data. Methods: Currently self-supervised learning for physics-guided reconstruction networks splits acquired undersampled data into two disjoint sets, where one is used for data consistency (DC) in the unrolled network and the other to define the training loss. The proposed multi-mask self-supervised learning via data undersampling (SSDU) splits acquired measurements into multiple pairs of disjoint sets for each training sample, while using one of these sets for DC units and the other for defining loss, thereby more efficiently using the undersampled data. Multi-mask SSDU is applied on fully-sampled 3D knee and prospectively undersampled 3D brain MRI datasets, which are retrospectively subsampled to acceleration rate (R)=8, and compared to CG-SENSE and single-mask SSDU DL-MRI, as well as supervised DL-MRI when fully-sampled data is available. Results: Results on knee MRI show that the proposed multi-mask SSDU outperforms SSDU and performs closely with supervised DL-MRI, while significantly outperforming CG-SENSE. A clinical reader study further ranks the multi-mask SSDU higher than supervised DL-MRI in terms of SNR and aliasing artifacts. Results on brain MRI show that multi-mask SSDU achieves better reconstruction quality compared to SSDU and CG-SENSE. Reader study demonstrates that multi-mask SSDU at R=8 significantly improves reconstruction compared to single-mask SSDU at R=8, as well as CG-SENSE at R=2. Conclusion: The proposed multi-mask SSDU approach enables improved training of physics-guided neural networks without fully-sampled data, by enabling efficient use of the undersampled data with multiple masks

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

Giant Cell Tumor within the Proximal Tibia after ACL Reconstruction

26-year-old female with prior anterior cruciate ligament reconstruction developed an enlarging lytic bone lesion around the tibial screw with sequential imaging over the course of one year demonstrating progression of this finding, which was confirmed histologically to be a giant cell tumor of bone. The lesion originated around the postoperative bed, making the diagnosis challenging during the early course of the presentation. The case demonstrates giant cell tumor which originated in the metaphysis and subsequently grew to involve the epiphysis; therefore, early course of the disease not involving the epiphysis should not exclude this diagnosis

Gradient-Modulated PETRA MRI

Image blurring that results from off-resonance and fast T2* signal decay is a common issue in radial ultrashort echo time magnetic resonance imaging (MRI) sequences. One solution is to use a higher readout bandwidth, but this may be impractical for some techniques such as pointwise-encoding time reduction with radial acquisition (PETRA), which is a hybrid method of zero echo time and single-point imaging techniques. Specifically, PETRA has severe specific absorption rate (SAR) and radiofrequency (RF) pulse peak power limitations when using higher bandwidths in human measurements. In this study, we introduce gradient modulation (GM) to PETRA to reduce image-blurring artifacts while keeping SAR and RF peak power low. GM-PETRA tolerance to image blurring was evaluated in simulations and experiments by comparison with the conventional PETRA technique. We performed inner ear imaging of a healthy subject at 7 T. GM-PETRA showed significantly less image blurring as a result of off-resonance and fast T2* signal decay compared to PETRA. In in vivo imaging, GM-PETRA nicely captured complex structures of the inner ear such as the cochlea and semicircular canals. GM can improve PETRA image quality and mitigate SAR and RF peak power limitations without special hardware modification in clinical scanners

Improved Visualization of Cartilage Canals Using Quantitative Susceptibility Mapping

<div><p>Purpose</p><p>Cartilage canal vessels are critical to the normal function of epiphyseal (growth) cartilage and damage to these vessels is demonstrated or suspected in several important developmental orthopaedic diseases. High-resolution, three-dimensional (3-D) visualization of cartilage canals has recently been demonstrated using susceptibility weighted imaging (SWI). In the present study, a quantitative susceptibility mapping (QSM) approach is evaluated for 3-D visualization of the cartilage canals. It is hypothesized that QSM post-processing improves visualization of the cartilage canals by resolving artifacts present in the standard SWI post-processing while retaining sensitivity to the cartilage canals.</p><p>Methods</p><p>Ex vivo distal femoral specimens from 3- and 8-week-old piglets and a 1-month-old human cadaver were scanned at 9.4 T with a 3-D gradient recalled echo sequence suitable for SWI and QSM post-processing. The human specimen and the stifle joint of a live, 3-week-old piglet also were scanned at 7.0 T. Datasets were processed using the standard SWI method and truncated k-space division QSM approach. To compare the post-processing methods, minimum/maximum intensity projections and 3-D reconstructions of the processed datasets were generated and evaluated.</p><p>Results</p><p>Cartilage canals were successfully visualized using both SWI and QSM approaches. The artifactual splitting of the cartilage canals that occurs due to the dipolar phase, which was present in the SWI post-processed data, was eliminated by the QSM approach. Thus, orientation-independent visualization and better localization of the cartilage canals was achieved with the QSM approach. Combination of GRE with a mask based on QSM data further improved visualization.</p><p>Conclusions</p><p>Improved and artifact-free 3-D visualization of the cartilage canals was demonstrated by QSM processing of the data, especially by utilizing susceptibility data as an enhancing mask. Utilizing tissue-inherent contrast, this method allows noninvasive assessment of the vasculature in the epiphyseal cartilage in the developing skeleton and potentially increases the opportunity to diagnose disease of this tissue in the preclinical stages, when treatment likely will have increased efficacy.</p></div

Comparison of QSM, plain GRE, SWI and QSM-WI at 7.0 T <i>in vivo</i>.

<p>Comparison of QSM, GRE, SWI and QSM-WI of a 3-week-old piglet scanned at 7.0 T <i>in vivo</i>. In the first pane, showing an axial plane perpendicular to B0, the datasets appeared visually similar. In the second pane, with views parallel to B0, artifactual splitting of the vessels was observed for the SWI data while both QSM datasets and the unprocessed GRE appeared artifact-free.</p

Main pre- and post-processing steps for SWI, QSM and QSM-WI.

<p>Main pre- and post-processing steps depicted for a single slice (in a plane parallel to <i>B</i>₀) from the distal femur of a 3-week-old pig at 9.4 T. Original GRE magnitude (A) and phase (B). Generation of segmentation mask was initiated with a single-slice manual ROI (C), which was extended to the entire 3-D volume automatically (D), generating a segmentation mask for further processing (E). In SWI post-processing, high-pass filtering of the phase was first done using homodyne filtering (F). The phase was converted to a negative phase mask (G) and the SWI data was generated by applying the phase mask to the original magnitude data (H). Finally the segmentation mask was also applied to the SWI data for further visualizations (I). For QSM post-processing, the phase was first processed using Laplacian and SHARP filtering (J) and, in turn, converted to a quantitative susceptibility map with k-space inversion (K) and masked with the segmentation and contrast-inverted to match the appearance of SWI (L). Finally, the susceptibility map was converted into an enhancing mask (M) and finally applied to the magnitude data to generate a QSM-WI dataset (N).</p

Quantitative susceptibility values of the cartilage canals.

<p>Relative susceptibility values of the cartilage canals with respect to the surrounding tissue in a 1-month-old human cadaveric distal femur scanned at 9.4 T (A) and at 7.0 T (B), and in a 3-week-old piglet scanned at 7.0 T <i>in vivo</i> (C) as a function of the truncation factor used in the k-space dipole inversion. Inset images in A-C depict single slices from the quantitative susceptibility maps at truncation factor values of 0.5, 5 and 20 at an intensity scale normalized with the intensity of the cartilage canals to facilitate visual comparison of the streaking artifacts. The second row shows the susceptibility histograms acquired for the corresponding cartilage canal ROIs for the respective specimens as a function of the truncation factor (D-F).</p