Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels and additionally leverage long-range contextual information using a transformer-based module to alleviate ambiguities in the presence of artifacts caused by undersampling. We combine local and global dependencies to perform simultaneous coarse and fine motion estimation. The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI. The impact of motion estimation accuracy on the downstream task of motion-compensated reconstruction was analyzed. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories (Cartesian and radial) and acceleration factors of up to 16x for cardiac motion and 30x for respiratory motion and achieves superior image quality in motion-compensated reconstruction qualitatively and quantitatively compared to conventional and recent deep learning-based approaches. The code is publicly available at https://github.com/lab-midas/GMARAFT.

We propose a self-supervised feature learning assisted reconstruction (SSFL-Recon) framework for MRI reconstruction to address the limitation of existing supervised learning methods. Although recent deep learning-based methods have shown promising performance in MRI reconstruction, most require fully-sampled images for supervised learning, which is challenging in practice considering long acquisition times under respiratory or organ motion. Moreover, nearly all fully-sampled datasets are obtained from conventional reconstruction of mildly accelerated datasets, thus potentially biasing the achievable performance. The numerous undersampled datasets with different accelerations in clinical practice, hence, remain underutilized. To address these issues, we first train a self-supervised feature extractor on undersampled images to learn sampling-insensitive features. The pre-learned features are subsequently embedded in the self-supervised reconstruction network to assist in removing artifacts. Experiments were conducted retrospectively on an in-house 2D cardiac Cine dataset, including 91 cardiovascular patients and 38 healthy subjects. The results demonstrate that the proposed SSFL-Recon framework outperforms existing self-supervised MRI reconstruction methods and even exhibits comparable or better performance to supervised learning up to 16× retrospective undersampling. The feature learning strategy can effectively extract global representations, which have proven beneficial in removing artifacts and increasing generalization ability during reconstruction.

Purpose: This study aims to develop an automated framework for operator‐independent assessment of cardiac ventricular function from highly accelerated images. Methods: We introduce a deep learning framework that generates reliable ventricular volumetric parameters and strain measures from fully sampled and retrospectively accelerated MR images. This method integrates image registration, motion‐compensated reconstruction, and segmentation in a synergetic loop for mutual refinement. The evaluation was performed on an in‐house dataset of healthy and cardiovascular‐diseased subjects. We examined the performance of the underlying tasks, including registration and segmentation, and their impact on derived parameters related to ventricular function. Results: The proposed approach demonstrates robustness to undersampling artifacts and requires limited annotation, while still reducing variability and errors for segmentation and registration. This translates to a to increase in Dice similarity compared to existing deep learning methods for left endocardium, left epicardium, and right ventricular delineation. Analysis of the predicted left and right ventricular ejection fraction reveals a strong correlation () with manual measurements. Moreover, the estimated motion and segmentation masks enable consistent radial and circumferential strain measurements across accelerations up to . Conclusion: A comprehensive ventricular function analysis can be performed using highly accelerated cine MR data with minimal annotation effort. This multitasking strategy has the potential to enable more accessible cardiac function analysis within a single breath‐hold.

Objectives: Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSE_DL) for routine clinical hip MRI at 1.5 and 3 T in terms of feasibility, image quality, and diagnostic performance. Material and Methods: In this prospective, monocentric study, TSE_DL was implemented clinically and evaluated in 14 prospectively enrolled patients undergoing a clinically indicated hip MRI at 1.5 and 3T between October 2020 and May 2021. Each patient underwent two examinations: For the first exam, we used standard sequences with generalized autocalibrating partial parallel acquisition reconstruction (TSE_S). For the second exam, we implemented prospectively undersampled TSE sequences with DL reconstruction (TSE_DL). Two radiologists assessed the TSE_DL and TSE_S regarding image quality, artifacts, noise, edge sharpness, diagnostic confidence, and delineation of anatomical structures using an ordinal five-point Likert scale (1 = non-diagnostic; 2 = poor; 3 = moderate; 4 = good; 5 = excellent). Both sequences were compared regarding the detection of common pathologies of the hip. Comparative analyses were conducted to assess the differences between TSE_DL and TSE_S. Results: Compared with TSE_S, TSE_DL was rated to be significantly superior in terms of image quality (p ≤ 0.020) with significantly reduced noise (p ≤ 0.001) and significantly improved edge sharpness (p = 0.003). No difference was found between TSE_S and TSE_DL concerning the extent of artifacts, diagnostic confidence, or the delineation of anatomical structures (p > 0.05). Example acquisition time reductions for the TSE sequences of 52% at 3 Tesla and 70% at 1.5 Tesla were achieved. Conclusion: TSE_DL of the hip is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared with TSE_S, reducing the acquisition time significantly.

Objective: The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy. Materials and Methods: Between October 2020 and June 2021, seventeen participants (eight patients, nine healthy subjects; mean age: 43 ± 16 (20–70) years, eight men) were prospectively included in this study. Each patient underwent two examinations: standard MRI, including TSE sequences reconstructed with a generalized autocalibrating partial parallel acquisition reconstruction (TSE_STD), and prospectively undersampled TSE sequences reconstructed with a DL reconstruction (TSE_DL). Two radiologists evaluated the images concerning image quality, noise, edge sharpness, artifacts, diagnostic confidence, and delineation of anatomical structures using a 5-point Likert scale, and rated the images concerning the detection of common pathologies. Results: Image quality was significantly improved in TSE_DL (mean 4.35, IQR 4–5) compared to TSE_STD (mean 3.76, IQR 3–4, p = 0.008). Moreover, TSE_DL showed decreased noise (mean 4.29, IQR 3.5–5) compared to TSE_STD (mean 3.35, IQR 3–4, p = 0.004). Ratings for delineation of anatomical structures, artifacts, edge sharpness, and diagnostic confidence did not differ significantly between TSE_DL and TSE_STD (p > 0.05). Inter-reader agreement was substantial to almost perfect (κ = 0.628–0.904). No difference was found concerning the detection of pathologies between the readers and between TSE_DL and TSE_STD. Using DL, the acquisition time could be reduced by more than 35% compared to TSE_STD. Conclusion: TSE_DL provided improved image quality and decreased noise while receiving equal ratings for edge sharpness, artifacts, delineation of anatomical structures, diagnostic confidence, and detection of pathologies compared to TSE_STD. Providing more than a 35% reduction of acquisition time, TSE_DL may be clinically relevant for elbow imaging due to increased patient comfort and higher patient throughput.

Over the last decades, overall survival for most cancer types has increased due to earlier diagnosis and more effective treatments. Simultaneously, whole-body MRI-(WB-MRI) has gained importance as a radiation free staging alternative to computed tomography. The aim of this study was to evaluate the diagnostic confidence and reproducibility of a novel abbreviated 20-min WB-MRI for oncologic follow-up imaging in patients with melanoma. In total, 24 patients with melanoma were retrospectively included in this institutional review board-approved study. All patients underwent three consecutive staging examinations via WB-MRI in a clinical 3 T MR scanner with an abbreviated 20-min protocol. Three radiologists independently evaluated the images in a blinded, random order regarding image quality (overall image quality, organ-based image quality, sharpness, noise, and artifacts) and regarding its diagnostic confidence on a 5-point-Likert-Scale (5 = excellent). Inter-reader agreement and reproducibility were assessed. Overall image quality and diagnostic confidence were rated to be excellent (median 5, interquartile range [IQR] 5–5). The sharpness of anatomic structures, and the extent of noise and artifacts, as well as the assessment of lymph nodes, liver, bone, and the cutaneous system were rated to be excellent (median 5, IQR 4–5). The image quality of the lung was rated to be good (median 4, IQR 4–5). Therefore, our study demonstrated that the novel accelerated 20-min WB-MRI protocol is feasible, providing high image quality and diagnostic confidence with reliable reproducibility for oncologic follow-up imaging.