Multi-coil Magnetic Resonance Imaging Research Articles

Joint Cross-Attention Network With Deep Modality Prior for Fast MRI Reconstruction.

Current deep learning-based reconstruction models for accelerated multi-coil magnetic resonance imaging (MRI) mainly focus on subsampled k-space data of single modality using convolutional neural network (CNN). Although dual-domain information and data consistency constraint are commonly adopted in fast MRI reconstruction, the performance of existing models is still limited mainly by three factors: inaccurate estimation of coil sensitivity, inadequate utilization of structural prior, and inductive bias of CNN. To tackle these challenges, we propose an unrolling-based joint Cross-Attention Network, dubbed as jCAN, using deep guidance of the already acquired intra-subject data. Particularly, to improve the performance of coil sensitivity estimation, we simultaneously optimize the latent MR image and sensitivity map (SM). Besides, we introduce Gating layer and Gaussian layer into SM estimation to alleviate the "defocus" and "over-coupling" effects and further ameliorate the SM estimation. To enhance the representation ability of the proposed model, we deploy Vision Transformer (ViT) and CNN in the image and k-space domains, respectively. Moreover, we exploit pre-acquired intra-subject scan as reference modality to guide the reconstruction of subsampled target modality by resorting to the self- and cross-attention scheme. Experimental results on public knee and in-house brain datasets demonstrate that the proposed jCAN outperforms the state-of-the-art methods by a large margin in terms of SSIM and PSNR for different acceleration factors and sampling masks. Our code is publicly available at https://github.com/sunkg/jCAN.

FFVN: An explicit feature fusion-based variational network for accelerated multi-coil MRI reconstruction

Magnetic Resonance Imaging (MRI) is a leading diagnostic imaging modality that supports high contrast of soft tissues with no invasiveness or radiation. Nonetheless, it suffers from long scan time owing to the inherent physics in its data acquisition process, hampering its development and applications. Traditional strategies such as Compressed Sensing (CS) and Parallel Imaging (PI) allow for MRI acceleration via sub-sampling strategy, and multiple coils, respectively. When Deep Learning (DL) joins in, both strategies get re-vitalized to achieve even faster reconstruction in various reconstruction methods, among which the variational network is a previously proposed method that combines the mathematical structure of variational models with DL for fast MRI reconstruction. However, in our study we observe that the information of MR features is either not efficiently or explicitly exploited in former works based on the variational network. Instead, we introduce a variational network with explicit feature fusion that combines the CS, PI, with DL for accelerated multi-coil MRI reconstruction. By explicitly leveraging the extra information via feature fusion following feature extraction, our proposed method achieves comparably satisfying performance to the state-of-the-art methods without too much computation overhead on a public multi-coil brain dataset under 5-fold and 10-fold acceleration.

Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations.

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

Isotropic multichannel total variation framework for joint reconstruction of multicontrast parallel MRI.

Purpose: To develop a synergistic image reconstruction framework that exploits multicontrast (MC), multicoil, and compressed sensing (CS) redundancies in magnetic resonance imaging (MRI). Approach: CS, MC acquisition, and parallel imaging (PI) have been individually well developed, but the combination of the three has not been equally well studied, much less the potential benefits of isotropy within such a setting. Inspired by total variation theory, we introduce an isotropic MC image regularizer and attain its full potential by integrating it into compressed MC multicoil MRI. A convex optimization problem is posed to model the new variational framework and a first-order algorithm is developed to solve the problem. Results: It turns out that the proposed isotropic regularizer outperforms many of the state-of-the-art reconstruction methods not only in terms of rotation-invariance preservation of symmetrical features, but also in suppressing noise or streaking artifacts, which are normally encountered in PI methods at aggressive undersampling rates. Moreover, the new framework significantly prevents intercontrast leakage of contrast-specific details, which seems to be a difficult situation to handle for some variational and low-rank MC reconstruction approaches. Conclusions: The new framework is a viable option for image reconstruction in fast protocols of MC parallel MRI, potentially reducing patient discomfort in otherwise long and time-consuming scans.

Body coil reference for inverse reconstructions of multi-coil data-the case for real-time MRI.

Real-time magnetic resonance imaging (MRI) or model-based MRI reconstructions of parametric maps require the solution of an ill-posed nonlinear inverse problem. Respective algorithms, e.g., the iteratively regularized Gauss-Newton method, implicitly combine datasets from multiple receive coils. Because these local coils may exhibit complex sensitivity profiles with rather different phase offsets, the numerical optimization may lead to phase singularities which in turn cause "black holes" in magnitude images. The purpose of this work is to develop a method for inverse reconstructions of multi-coil MRI data which avoids the generation of such spatially selective phase singularities. It is proposed to use volumetric body coil data and start the iterative reconstruction of multi-coil data with a reference image which offers proper phase information. In more detail, inverse reconstructions of multi-coil data are initialized with a complex "seed" image which is obtained by a Fast Fourier Transform (FFT) reconstruction of data from a single body coil element. This is accomplished at no additional cost as only very few body coil scans with identical conditions as the multi-coil acquisitions are needed as part of the regular prep scan period. The method is evaluated for anatomical real-time MRI and model-based phase-contrast flow MRI in real-time at 3 T. The proposed method overcomes phase singularities in all cases for arbitrary sets of receive coils. In conclusion, the automatic use of a single body coil reference image is simple, robust, and further improves the reliability of advanced MRI reconstructions from multi-coil data.

Comparison of Digital Rectal Examination, Transrectal Ultrasonography, and Multicoil Magnetic Resonance Imaging for Preoperative Evaluation of Prostate Cancer

A prospective study was designed to compare the potentials of digital rectal examination (DRE), transrectal ultrasound (TRUS), and magnetic resonance imaging (MRI) using integrated endorectal and pelvic phased-array coils for preoperative estimation of tumor volume and local extent of prostate cancer. Evaluation of 20 consecutive patients undergoing radical retropubic prostatectomy included DRE, TRUS with a 7.5-MHz transducer, and MRI on a 1.5-tesla GE Signa system. Step sections (5 mm) of the entire specimen were performed, and tumor volume and percentage of gland involved were calculated. DRE, TRUS, and endorectal and pelvic phased-array MRI showed 50, 75, and 95% of the cancers, respectively. There was a linear correlation on MRI between predicted tumor volume and pathological tumor volume (r = 0.82, p < 0.0001), but not between predicted volume on DRE or TRUS and real volume. The accuracy for detecting extracapsular penetration was 60% for DRE and TRUS and 79% for MRI. The accuracy for detecting seminal vesicle invasion was 60% for DRE, 66 for TRUS, and 89% for MRI. The negative predictive value for extracapsular and seminal vesicle extension was highest for MRI (85 and 93%, respectively). The accuracy for tumor location in the apex of the prostate was 30% for DRE, 47 for TRUS, and 89% for MRI. MRI with integrated endorectal and pelvic phased-array coils satisfactorily predicted tumor volume and tumor extent preoperatively. Multicoil MRI can assist in decision making as it is valuable in the definition of patients that may benefit from surgery and can be of help for evaluating the risk of a positive margin, especially in the apical resection.