Real-Time Magnetic Resonance Imaging

Clinical magnetic resonance imaging evaluation of glymphatic function

The glymphatic system is a system of specialized perivascular spaces in the brain that facilitates removal of toxic waste solutes from the brain. Evaluation of glymphatic system function by means of magnetic resonance imaging (MRI) has thus far been largely focused on rodents because of the limitations of intrathecal delivery of gadolinium-based contrast agents to humans. This review discusses MRI methods that can be employed clinically for glymphatic-related measurements intended for early diagnosis, prevention, and the treatment of various neurological conditions. Although glymphatic system-based MRI research is in its early stages, recent studies have identified promising noninvasive MRI markers associated with glymphatic system alterations in neurological diseases. However, further optimization in data acquisition, validation, and modeling are needed to investigate the glymphatic system within the clinical setting.

A Learned-SVD Approach to the Electromagnetic Inverse Source Problem

We propose an artificial intelligence approach based on deep neural networks to tackle a canonical 2D scalar inverse source problem. The learned singular value decomposition (L-SVD) based on hybrid autoencoding is considered. We compare the reconstruction performance of L-SVD to the Truncated SVD (TSVD) regularized inversion, which is a canonical regularization scheme, to solve an ill-posed linear inverse problem. Numerical tests referring to far-field acquisitions show that L-SVD provides, with proper training on a well-organized dataset, superior performance in terms of reconstruction errors as compared to TSVD, allowing for the retrieval of faster spatial variations of the source. Indeed, L-SVD accommodates a priori information on the set of relevant unknown current distributions. Different from TSVD, which performs linear processing on a linear problem, L-SVD operates non-linearly on the data. A numerical analysis also underlines how the performance of the L-SVD degrades when the unknown source does not match the training dataset.

Four-dimensional flow MRI for quantitative assessment of cerebrospinal fluid dynamics: Status and opportunities

Neurological disorders can manifest with altered neurofluid dynamics in different compartments of the central nervous system. These include alterations in cerebral blood flow, cerebrospinal fluid (CSF) flow, and tissue biomechanics. Noninvasive quantitative assessment of neurofluid flow and tissue motion is feasible with phase contrast magnetic resonance imaging (PC MRI). While two-dimensional (2D) PC MRI is routinely utilized in research and clinical settings to assess flow dynamics through a single imaging slice, comprehensive neurofluid dynamic assessment can be limited or impractical. Recently, four-dimensional (4D) flow MRI (or time-resolved three-dimensional PC with three-directional velocity encoding) has emerged as a powerful extension of 2D PC, allowing for large volumetric coverage of fluid velocities at high spatiotemporal resolution within clinically reasonable scan times. Yet, most 4D flow studies have focused on blood flow imaging. Characterizing CSF flow dynamics with 4D flow (i.e., 4D CSF flow) is of high interest to understand normal brain and spine physiology, but also to study neurological disorders such as dysfunctional brain metabolite waste clearance, where CSF dynamics appear to play an important role. However, 4D CSF flow imaging is challenged by the long T1 time of CSF and slower velocities compared with blood flow, which can result in longer scan times from low flip angles and extended motion-sensitive gradients, hindering clinical adoption. In this work, we review the state of 4D CSF flow MRI including challenges, novel solutions from current research and ongoing needs, examples of clinical and research applications, and discuss an outlook on the future of 4D CSF flow.

Measurement of Scapholunate Joint Space Width on Real-Time MRI-A Feasibility Study

INTRODUCTION

The scapholunate interosseous ligament is pivotal for wrist stability, and its impairment can result in instability and joint degeneration. This study explores the application of real-time MRI for dynamic assessment of the scapholunate joint during wrist motion with the objective of determining its diagnostic value in efficacy in contrast to static imaging modalities.

MATERIALS AND METHODS

Ten healthy participants underwent real-time MRI scans during wrist ab/adduction and fist-clenching maneuvers. Measurements were obtained at proximal, medial, and distal landmarks on both dynamic and static images with statistical analyses conducted to evaluate the reliability of measurements at each landmark and the concordance between dynamic measurements and established static images. Additionally, inter- and intraobserver variabilities were evaluated.

RESULTS

Measurements of the medial landmarks demonstrated the closest agreement with static images and exhibited the least scatter. Distal landmark measurements showed a similar level of agreement but with increased scatter. Proximal landmark measurements displayed substantial deviation, which was accompanied by an even greater degree of scatter. Although no significant differences were observed between the ab/adduction and fist-clenching maneuvers, both inter- and intraobserver variabilities were significant across all measurements.

CONCLUSIONS

This study highlights the potential of real-time MRI in the dynamic assessment of the scapholunate joint particularly at the medial landmark. Despite promising results, challenges such as measurement variability need to be addressed. Standardization and integration with advanced image processing methods could significantly enhance the accuracy and reliability of real-time MRI, paving the way for its clinical implementation in dynamic wrist imaging studies.

Training deep learning based dynamic MR image reconstruction using open-source natural videos

To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N = 692) or from synthetic MR data simulated from Inter4K natural videos (N = 588). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N = 104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac cine (short axis, four chambers, N = 20) and speech cine (N = 10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. In simulated data, DL networks trained with cardiac data outperformed DL networks trained with natural videos, both of which outperformed CS (p < 0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions.The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR)

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy

BACKGROUND

Magnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality's utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality.

METHODS

We trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 T MRI-linac enabling real-time super-resolution imaging. We integrated this framework with the targeting system of the MRI-linac to demonstrate real-time beam adaptation with super-resolution-based imaging. We tested the integrated system using large publicly available datasets, healthy volunteer imaging, phantom imaging, and beam tracking experiments using bicubic interpolation as a baseline comparison.

RESULTS

Deep learning-based super-resolution increases the spatial resolution of real-time MRI across a variety of experiments, offering measured performance benefits compared to bicubic interpolation. The temporal resolution is not compromised as measured by a real-time adaptation latency experiment. These two effects, an increase in the spatial resolution with a negligible decrease in the temporal resolution, leads to a net increase in the spatiotemporal resolution.

CONCLUSIONS

Deployed super-resolution neural networks can increase the spatiotemporal resolution of real-time MRI. This has applications to domains such as MRI-guided radiation therapy and interventional procedures.

ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function

BACKGROUND

Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion.

METHODS

A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Caliński-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function.

RESULTS

A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p < 0.001), and the Caliński-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p < 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p < 0.05), whereas the SNR and CNR values were not significantly different (p = 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible.

CONCLUSION

ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.

Navigating Neural Landscapes: A Comprehensive Review of Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) Applications in Epilepsy

This review comprehensively explores the evolving role of neuroimaging, specifically magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS), in epilepsy research and clinical practice. Beginning with a concise overview of epilepsy, the discussion emphasizes the crucial importance of neuroimaging in diagnosing and managing this complex neurological disorder. The review delves into the applications of advanced MRI techniques, including high-field MRI, resting-state fMRI, and connectomics, highlighting their impact on refining our understanding of epilepsy's structural and functional dimensions. Additionally, it examines the integration of machine learning in the analysis of intricate neuroimaging data. Moving to the clinical domain, the review outlines the utility of neuroimaging in pre-surgical evaluations and the monitoring of treatment responses and disease progression. Despite significant strides, challenges and limitations are discussed in the routine clinical incorporation of neuroimaging. The review explores promising developments in MRI and MRS technology, potential advancements in imaging biomarkers, and the implications for personalized medicine in epilepsy management. The conclusion underscores the transformative potential of neuroimaging and advocates for continued exploration, collaboration, and technological innovation to propel the field toward a future where tailored, effective interventions improve outcomes for individuals with epilepsy.

Real-time water/fat imaging at 0.55T with spiral out-in-out-in sampling

PURPOSE

To develop an efficient and flexible water/fat separated real-time MRI (RT-MRI) method using spiral out-in-out-in (OIOI) sampling and balanced SSFP (bSSFP) at 0.55T.

METHODS

A bSSFP sequence with golden-angle spiral OIOI readout was developed, capturing three echoes to allow water/fat separation. A low-latency reconstruction that combines all echoes was available for online visualization. An offline reconstruction provided water and fat RT-MRI in two steps: (1) image reconstruction with spatiotemporally constrained reconstruction (STCR) and (2) water/fat separation with hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (HIDEAL). In healthy volunteers, spiral OIOI was acquired in the wrist during a radial-to-ulnar deviation maneuver, in the heart without breath-hold and cardiac gating, and in the lower abdomen during free-breathing for visualizing small bowel motility.

RESULTS

We demonstrate successful water/fat separated RT-MRI for all tested applications. In the wrist, resulting images provided clear depiction of ligament gaps and their interactions during the radial-to-ulnar deviation maneuver. In the heart, water/fat RT-MRI depicted epicardial fat, provided improved delineation of epicardial coronary arteries, and provided high blood-myocardial contrast for ventricular function assessment. In the abdomen, water-only RT-MRI captured small bowel mobility clearly with improved water-fat contrast.

CONCLUSIONS

We have demonstrated a novel and flexible bSSFP spiral OIOI sequence at 0.55T that can provide water/fat separated RT-MRI with a variety of application-specific temporal resolution and spatial resolution requirements.

Utility of a 2D kinematic HASTE sequence in magnetic resonance imaging assessment of adjacent segment degeneration following anterior cervical discectomy and fusion

OBJECTIVES

To evaluate a dynamic half-Fourier acquired single turbo spin echo (HASTE) sequence following anterior cervical discectomy and fusion (ACDF) at the junctional level for adjacent segment degeneration comparing dynamic listhesis to radiographs and assessing dynamic cord contact and deformity during flexion-extension METHODS: Patients with ACDF referred for cervical spine MRI underwent a kinematic flexion-extension sagittal 2D HASTE sequence in addition to routine sequences. Images were independently reviewed by three radiologists for static/dynamic listhesis, and compared to flexion-extension radiographs. Blinded assessment of the HASTE sequence was performed for cord contact/deformity between neutral, flexion, and extension, to evaluate concordance between readers and inter-modality agreement. Inter-reader agreement for dynamic listhesis and impingement grade and inter-modality agreement for dynamic listhesis on MRI and radiographs was assessed using the kappa coefficient and percentage concordance.

RESULTS

A total of 28 patients, mean age 60.2 years, were included. Mean HASTE acquisition time was 42 s. 14.3% demonstrated high grade dynamic stenosis (> grade 4) at the adjacent segment. There was substantial agreement for dynamic cord impingement with 70.2% concordance (kappa = 0.62). Concordance across readers for dynamic listhesis using HASTE was 81.0% (68/84) (kappa = 0.16) compared with 71.4% (60/84) (kappa = 0.40) for radiographs. Inter-modality agreement between flexion-extension radiographs and MRI assessment for dynamic listhesis across the readers was moderate (kappa = 0.41; 95% confidence interval: 0.16 to 0.67).

CONCLUSIONS

A sagittal flexion-extension HASTE cine sequence provides substantial agreement between readers for dynamic cord deformity and moderate agreement between radiographs and MRI for dynamic listhesis.

CLINICAL RELEVANCE STATEMENT

Degeneration of the adjacent segment with instability and myelopathy is one of the most common causes of pain and neurological deterioration requiring re-operation following cervical fusion surgery.

KEY POINTS

• A real-time kinematic 2D sagittal HASTE flexion-extension sequence can be used to assess for dynamic listhesis, cervical cord, contact and deformity. • The additional kinematic cine sequence was well tolerated and the mean acquisition time for the 2D HASTE sequence was 42 s (range 31-44 s). • A sagittal flexion-extension HASTE cine sequence provides substantial agreement between readers for dynamic cord deformity and moderate agreement between radiographs and MRI for dynamic listhesis.

New clinical opportunities of low-field MRI: heart, lung, body, and musculoskeletal

Contemporary whole-body low-field MRI scanners (< 1 T) present new and exciting opportunities for improved body imaging. The fundamental reason is that the reduced off-resonance and reduced SAR provide substantially increased flexibility in the design of MRI pulse sequences. Promising body applications include lung parenchyma imaging, imaging adjacent to metallic implants, cardiac imaging, and dynamic imaging in general. The lower cost of such systems may make MRI favorable for screening high-risk populations and population health research, and the more open configurations allowed may prove favorable for obese subjects and for pregnant women. This article summarizes promising body applications for contemporary whole-body low-field MRI systems, with a focus on new platforms developed within the past 5 years. This is an active area of research, and one can expect many improvements as MRI physicists fully explore the landscape of pulse sequences that are feasible, and as clinicians apply these to patient populations.

Magnetic resonance imaging-guided radiofrequency ablation of breast cancer: a current state of the art review

With a gradual increase in breast cancer incidence and mortality rates and an urgent need to improve patient prognosis and cosmetology, magnetic resonance imaging (MRI)-guided radiofrequency ablation (RFA) therapy has attracted wide attention as a new treatment method for breast cancer. MRI-RFA results in a higher complete ablation rate and extremely low recurrence and complication rates. Thus, it may be used as an independent treatment for breast cancer or adjuvant to breast-conserving surgery to reduce the extent of breast resection. Furthermore, with MRI guidance, accurate control of RFA can be achieved, and breast cancer treatment can enter a new stage of minimally invasive, safe, and comprehensive therapy. With progress in MR thermometry technology, the applications of MRI are expected to broaden.

Speech production real-time MRI at 0.55 T

PURPOSE

To demonstrate speech-production real-time MRI (RT-MRI) using a contemporary 0.55T system, and to identify opportunities for improved performance compared with conventional field strengths.

METHODS

Experiments were performed on healthy adult volunteers using a 0.55T MRI system with high-performance gradients and a custom 8-channel upper airway coil. Imaging was performed using spiral-based balanced SSFP and gradient-recalled echo (GRE) pulse sequences using a temporal finite-difference constrained reconstruction. Speech-production RT-MRI was performed with three spiral readout durations (8.90, 5.58, and 3.48 ms) to determine trade-offs with respect to articulator contrast, blurring, banding artifacts, and overall image quality.

RESULTS

Both spiral GRE and bSSFP captured tongue boundary dynamics during rapid consonant-vowel syllables. Although bSSFP provided substantially higher SNR in all vocal tract articulators than GRE, it suffered from banding artifacts at TR > 10.9 ms. Spiral bSSFP with the shortest readout duration (3.48 ms, TR = 5.30 ms) had the best image quality, with a 1.54-times boost in SNR compared with an equivalent GRE sequence. Longer readout durations led to increased SNR efficiency and blurring in both bSSFP and GRE.

CONCLUSION

High-performance 0.55T MRI systems can be used for speech-production RT-MRI. Spiral bSSFP can be used without suffering from banding artifacts in vocal tract articulators, provide better SNR efficiency, and have better image quality than what is typically achieved at 1.5 T or 3 T.

A deep unrolled neural network for real-time MRI-guided brain intervention

Accurate navigation and targeting are critical for neurological interventions including biopsy and deep brain stimulation. Real-time image guidance further improves surgical planning and MRI is ideally suited for both pre- and intra-operative imaging. However, balancing spatial and temporal resolution is a major challenge for real-time interventional MRI (i-MRI). Here, we proposed a deep unrolled neural network, dubbed as LSFP-Net, for real-time i-MRI reconstruction. By integrating LSFP-Net and a custom-designed, MR-compatible interventional device into a 3 T MRI scanner, a real-time MRI-guided brain intervention system is proposed. The performance of the system was evaluated using phantom and cadaver studies. 2D/3D real-time i-MRI was achieved with temporal resolutions of 80/732.8 ms, latencies of 0.4/3.66 s including data communication, processing and reconstruction time, and in-plane spatial resolution of 1 × 1 mm2. The results demonstrated that the proposed method enables real-time monitoring of the remote-controlled brain intervention, and showed the potential to be readily integrated into diagnostic scanners for image-guided neurosurgery.

Real-time speech MRI datasets with corresponding articulator ground-truth segmentations

The use of real-time magnetic resonance imaging (rt-MRI) of speech is increasing in clinical practice and speech science research. Analysis of such images often requires segmentation of articulators and the vocal tract, and the community is turning to deep-learning-based methods to perform this segmentation. While there are publicly available rt-MRI datasets of speech, these do not include ground-truth (GT) segmentations, a key requirement for the development of deep-learning-based segmentation methods. To begin to address this barrier, this work presents rt-MRI speech datasets of five healthy adult volunteers with corresponding GT segmentations and velopharyngeal closure patterns. The images were acquired using standard clinical MRI scanners, coils and sequences to facilitate acquisition of similar images in other centres. The datasets include manually created GT segmentations of six anatomical features including the tongue, soft palate and vocal tract. In addition, this work makes code and instructions to implement a current state-of-the-art deep-learning-based method to segment rt-MRI speech datasets publicly available, thus providing the community and others with a starting point for developing such methods.

Anterior-posterior glenohumeral translation in shoulders with traumatic anterior instability: a systematic review of the literature

Background

Reports of glenohumeral translation in shoulders with traumatic anterior instability have been presented. The aim of this systematic review was to investigate anterior-posterior translation in shoulders with traumatic anterior instability.

Methods

This systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies including patients aged ≥15 years with previous traumatic anterior shoulder dislocation or subluxation were included. The outcome was anterior-posterior glenohumeral translation. A search of PubMed, Embase, and Cochrane library was performed on July 17, 2022. Two reviewers individually screened titles and abstracts, reviewed full text, extracted data, and performed quality assessment.

Results

Twenty studies (582 unstable shoulders in total) of varying quality were included. There was a lack of standardization and unity across studies. Radiography, ultrasound, computed tomography, magnetic resonance imaging, motion tracking, instrumentation, and manual testing were used to assess the glenohumeral translation. The glenohumeral translation in unstable shoulders ranged from 0.0 ± 0.8 mm to 11.6 ± 3.7 mm, as measured during various motion tasks, arm positions, and application of external force. The glenohumeral translation was larger or more anteriorly directed in unstable shoulders than in stable when contralateral healthy shoulders or a healthy control group were included in the studies. Several studies found that the humeral head was more anteriorly located on the glenoid in the unstable shoulders.

Conclusion

This systematic review provides an overview of the current literature on glenohumeral translation in traumatic anterior shoulder instability. It was not able to identify a threshold for abnormal translation in unstable shoulders, due to the heterogeneity of data. The review supports that not only the range of translation but also the direction hereof as well as the location of the humeral head on the glenoid seem to be part of the pathophysiology. Technical development and increased attention to research methodology in recent years may provide more knowledge and clarity on this topic in the future.

Real-time MRI of the moving wrist at 0.55 tesla

OBJECTIVES

Magnetic resonance imaging (MRI) using 1.5T or 3.0T systems is routinely employed for assessing wrist pathology; however, due to off-resonance artifacts and high power deposition, these high-field systems have drawbacks for real-time (RT) imaging of the moving wrist. Recently, high-performance 0.55T MRI systems have become available. In this proof-of-concept study, we tested the hypothesis that RT-MRI during continuous, active, and uninterrupted wrist motion is feasible with a high-performance 0.55T system at temporal resolutions below 100 ms and that the resulting images provide visualization of tissues commonly interrogated for assessing dynamic wrist instability.

METHODS

Participants were scanned during uninterrupted wrist radial-ulnar deviation and clenched fist maneuvers. Resulting images (nominal temporal resolution of 12.7-164.6 ms per image) were assessed for image quality. Feasibility of static MRI to supplement RT-MRI acquisition was also tested.

RESULTS

The RT images with temporal resolutions < 100 ms demonstrated low distortion and image artifacts, and higher reader assessment scores. Static MRI scans showed the ability to assess anatomical structures of interest in the wrist.

CONCLUSION

RT-MRI of the wrist at a high temporal resolution, coupled with static MRI, is feasible with a high-performance 0.55T system, and may enable improved assessment of wrist dynamic dysfunction and instability.

ADVANCES IN KNOWLEDGE

Real-time MRI of the moving wrist is feasible with high-performance 0.55T and may improve the evaluation of dynamic dysfunction of the wrist.

Evaluation of a novel MRI-safe needle guidance toolkit for streamlined arthrography procedures

Currently, Magnetic Resonance arthrography procedures require two rooms and two imaging modalities: fluoroscopically guided needle insertion in a fluoroscopy suite, followed by diagnostic MRI in a separate MRI suite. The use of fluoroscopy for needle placement exposes patients to ionizing radiation, which is an important concern, especially in pediatrics. The need for two different rooms and coordinating times for these rooms complicates hospital resource scheduling and logistics. In addition, the added delays could expose younger children to additional risks associated with the use of general anesthesia. To address these issues, we propose a new technique to streamline the arthrography procedure. Our proposed technology aims to eliminate exposure to ionizing radiation and to streamline arthrography procedures that are conducted solely under MRI. This toolkit consists of a 3D slicer-based user interface, a spatially unique silicone grid template, and a hand-held needle guidance device. Together, these tools are intended to simplify and shorten the procedure while maintaining accuracy and precision comparable to the current gold standard procedure. In our cadaver study, we evaluated the feasibility and accuracy of our novel MRI-safe Needle Guidance Toolkit for MRI arthrography procedures, achieving an average targeting accuracy of 3.2 ± 1.0 mm. The results presented in this study showed the feasibility and promise of our novel MRI-safe needle guidance toolkit for arthrography procedures.

Cardiac Magnetic Resonance Imaging to Determine Single Ventricle Function in a Pediatric Population is Feasible in a Large Trial Setting: Experience from the Single Ventricle Reconstruction Trial Longitudinal Follow up

The Single Ventricle Reconstruction (SVR) Trial was a randomized prospective trial designed to determine survival advantage of the modified Blalock-Taussig-Thomas shunt (BTTS) vs the right ventricle to pulmonary artery conduit (RVPAS) for patients with hypoplastic left heart syndrome. The primary aim of the long-term follow-up (SVRIII) was to determine the impact of shunt type on RV function. In this work, we describe the use of CMR in a large cohort follow up from the SVR Trial as a focused study of single ventricle function. The SVRIII protocol included short axis steady-state free precession imaging to assess single ventricle systolic function and flow quantification. There were 313 eligible SVRIII participants and 237 enrolled, ages ranging from 10 to 12.5 years. 177/237 (75%) participants underwent CMR. The most common reasons for not undergoing CMR exam were requirement for anesthesia (n = 14) or ICD/pacemaker (n = 11). A total of 168/177 (94%) CMR studies were diagnostic for RVEF. Median exam time was 54 [IQR 40-74] minutes, cine function exam time 20 [IQR 14-27] minutes, and flow quantification time 18 [IQR 12-25] minutes. There were 69/177 (39%) studies noted to have intra-thoracic artifacts, most common being susceptibility artifact from intra-thoracic metal. Not all artifacts resulted in non-diagnostic exams. These data describe the use and limitations of CMR for the assessment of cardiac function in a prospective trial setting in a grade-school-aged pediatric population with congenital heart disease. Many of the limitations are expected to decrease with the continued advancement of CMR technology.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR)

Objective

3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study the anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly undersampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly undersampled data.

Approach

STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model which deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis (PCA). The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as MR data acquired clinically from a healthy human subject. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS).

Main results

STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean±S.D. center-of-mass error of 1.0±0.4 mm, compared to 3.4±1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.

Significance

STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Machine learning assists in increasing the time resolution of X-ray computed tomography applied to mineral precipitation in porous media

Many subsurface engineering technologies or natural processes cause porous medium properties, such as porosity or permeability, to evolve in time. Studying and understanding such processes on the pore scale is strongly aided by visualizing the details of geometric and morphological changes in the pores. For realistic 3D porous media, X-Ray Computed Tomography (XRCT) is the method of choice for visualization. However, the necessary high spatial resolution requires either access to limited high-energy synchrotron facilities or data acquisition times which are considerably longer (e.g. hours) than the time scales of the processes causing the pore geometry change (e.g. minutes). Thus, so far, conventional benchtop XRCT technologies are often too slow to allow for studying dynamic processes. Interrupting experiments for performing XRCT scans is also in many instances no viable approach. We propose a novel workflow for investigating dynamic precipitation processes in porous media systems in 3D using a conventional XRCT technology. Our workflow is based on limiting the data acquisition time by reducing the number of projections and enhancing the lower-quality reconstructed images using machine-learning algorithms trained on images reconstructed from high-quality initial- and final-stage scans. We apply the proposed workflow to induced carbonate precipitation within a porous-media sample of sintered glass-beads. So we were able to increase the temporal resolution sufficiently to study the temporal evolution of the precipitate accumulation using an available benchtop XRCT device.

Methodology for quantitative evaluation of mandibular condyles motion symmetricity from real-time MRI in the axial plane

Diagnosis of temporomandibular disorders is currently based on clinical examination and static MRI. Real-time MRI enables tracking of condylar motion and, thus, evaluation of their motion symmetricity (which could be associated with temporomandibular joint disorders). The purpose of this work is to propose an acquisition protocol, an image processing approach, and a set of parameters enabling objective assessment of motion asymmetry; to check the reliability and find the limitations of the approach, and to verify if the automatically calculated parameters are associated with the motion symmetricity. A rapid radial FLASH sequence was used to acquire a dynamic set of axial images for 10 subjects. One more subject was involved to estimate the dependence of the motion parameters on the slice placement. The images were segmented with a semi-automatic approach based on U-Net convolutional neural network, and the condyles' mass centers were projected on the mid-sagittal axis. Resulting projection curves were used for the extraction of various motion parameters including latency, velocity peak delay, and maximal displacement between the right and the left condyle. These automatically calculated parameters were compared with the physicians' scores. The proposed segmentation approach allowed a reliable center of mass tracking. Latency and velocity peak delay were found to be invariant to the slice position, and maximal displacement difference considerably varied. The automatically calculated parameters demonstrated a significant correlation with the experts' scores. The proposed acquisition and data processing protocol enables the automatizable extraction of quantitative parameters that characterize the symmetricity of condylar motion.

Accelerating GRAPPA reconstruction using SoC design for real-time cardiac MRI

Real-time cardiac MRI is a rapidly developing area of research that has the potential to improve the diagnosis and treatment of cardiovascular diseases. However, the acquisition of high-quality real-time cardiac MR (CMR) images is challenging as it requires a high frame rate and temporal resolution. To overcome this challenge, there have been recent efforts on several approaches including hardware-based improvements and image reconstruction techniques such as compressed sensing and parallel MRI. The use of parallel MRI techniques such as GRAPPA (Generalized Autocalibrating Partial Parallel Acquisition) is a promising approach for improving the temporal resolution of MRI and expanding its applications in clinical practice. However, the GRAPPA algorithm involves a significant amount of computation, particularly for high acceleration factors and large datasets. This can result in long reconstruction times, which can limit the ability to achieve real-time imaging or high frame rates. One solution to this challenge is to use specialized hardware i.e. field-programmable gate arrays (FPGAs). In this work, a novel 32-bit floating-point FPGA-based GRAPPA accelerator is proposed with an aim to reconstruct high-quality cardiac MR images at higher frame rates, making it well suited for real-time clinical applications. The proposed FPGA-based accelerator consists of custom-designed data processing units named as dedicated computational engines (DCEs) that allow for a continuous flow of data between the calibration and synthesis stages of GRAPPA reconstruction process. This greatly increases the throughput and reduces the latency of the overall proposed system. Moreover, a high-speed memory module (DDR4-SDRAM) is integrated with the proposed architecture to store the multi-coil MR data. An on-chip quad-core ARM Cortex-A53 processor is used to manage access control information required for data transfer between the DCEs and DDR4-SDRAM. The proposed accelerator is implemented on Xilinx Zynq UltraScale + MPSoC using high-level synthesis (HLS) and hardware descriptive language (HDL) with an aim to explore the trade-offs between the reconstruction time, resource utilization and design effort. Several experiments have been performed using in-vivo cardiac datasets i.e. 18-receiver coil and 30-receiver coil to evaluate the performance of the proposed accelerator. A comparison is performed with the contemporary CPU and GPU-based GRAPPA reconstruction methods in terms of reconstruction time, frames-per-second and reconstruction accuracy (RMSE and SNR). The results show that the proposed accelerator achieves speed-up factors up to 121× and 9× as compared to the contemporary CPU-based and GPU-based GRAPPA reconstruction methods, respectively. Moreover, it has been demonstrated that the proposed accelerator can achieve reconstruction rates of up to ∼27 frames-per-second while maintaining the visual quality of the reconstructed images.

AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow

Over the last decade, artificial intelligence (AI) has made an enormous impact on a wide range of fields, including science, engineering, informatics, finance, and transportation [...].

Contrast-optimal simultaneous multi-slice bSSFP cine cardiac imaging at 0.55 T

PURPOSE

To determine if contemporary 0.55 T MRI supports the use of contrast-optimal flip angles (FA) for simultaneous multi-slice (SMS) balanced SSFP (bSSFP) cardiac function assessment, which is impractical at conventional field strengths because of excessive SAR and/or banding artifacts.

METHODS

Blipped-CAIPI bSSFP was combined with spiral sampling for ventricular function assessment at 0.55 T. Cine movies with single band and SMS factors of 2 and 3 (SMS 2 and 3), and FA ranging from 60° to 160°, were acquired in seven healthy volunteers. Left ventricular blood and myocardial signal intensity (SI) normalized by background noise and blood-myocardium contrast were measured and compared across acquisition settings.

RESULTS

Myocardial SI was slightly higher in single band than in SMS and decreased with an increasing FA. Blood SI increased as the FA increased for single band, and increment was small for FA ≥120°. Blood SI for SMS 2 and 3 increased with an increasing FA up to ∼100°. Blood-myocardium contrast increased with an increasing FA for single band, peaked at FA = 160° (systole: 28.43, diastole: 29.15), attributed mainly to reduced myocardial SI when FA ≥120°. For SMS 2, contrast peaked at 120° (systole: 21.43, diastole: 19.85). For SMS 3, contrast peaked at 120° in systole (16.62) and 100° in diastole (19.04).

CONCLUSIONS

Contemporary 0.55 T MR scanners equipped with high-performance gradient systems allow the use of contrast-optimal FA for SMS accelerated bSSFP cine examinations without compromising image quality. The contrast-optimal FA was found to be 140° to 160° for single band and 100° to 120° for SMS 2 and 3.

Evaluation of a novel 8-channel RX coil for speech production MRI at 0.55 T

OBJECTIVE

Speech production MRI benefits from lower magnetic fields due to reduced off-resonance effects at air-tissue interfaces and from the use of dedicated receiver coils due to higher SNR and parallel imaging capability. Here we present a custom designed upper airway coil for ¹H imaging at 0.55 Tesla and evaluate its performance in comparison with a vendor-provided prototype 16-channel head/neck coil.

MATERIALS AND METHODS

Four adult volunteers were scanned with both custom speech and prototype head-neck coils. We evaluated SNR gains of each of the coils over eleven upper airway volumes-of-interest measured relative to the integrated body coil. We evaluated parallel imaging performance of both coils by computing g-factors for SENSE reconstruction of uniform and variable density Cartesian sampling schemes with R = 2, 3, and 4.

RESULTS

The dedicated coil shows approximately 3.5-fold SNR efficiency compared to the head-neck coil. For R = 2 and 3, both uniform and variable density samplings have g-factor values below 1.1 in the upper airway region. For R = 4, g-factor values are higher for both trajectories.

DISCUSSION

The dedicated coil configuration allows for a significant SNR gain over the head-neck coil in the articulators. This, along with favorable g values, makes the coil useful in speech production MRI.

Interpretable Dimension Reduction for MRI Channel Suppression

Channel suppression can reduce the redundant information in multiple channel receiver coils and accelerate reconstruction speed to meet real-time imaging requirements. The principal component analysis has been used for channel suppression, but it is difficult to be interpreted because all channels contribute to principal components. Furthermore, the importance of interpretability in machine learning has recently attracted increasing attention in radiology. To improve the interpretability of PCA-based channel suppression, a sparse PCA method is proposed to reduce the most coils' loadings to be zero. Channel suppression is formulated as solving a nonlinear eigenvalue problem using the inverse power method instead of the direct matrix decomposition. Experimental results of in vivo data show that the sparse PCA-based channel suppression not only improves the interpretability with sparse channels, but also improves reconstruction quality compared to the standard PCA-based reconstruction with the similar reconstruction time.

Real-time cardiac MRI using an undersampled spiral k-space trajectory and a reconstruction based on a variational network

PURPOSE

Cardiac MRI represents the gold standard to determine myocardial function. However, the current clinical standard protocol, a segmented Cartesian acquisition, is time-consuming and can lead to compromised image quality in the case of arrhythmia or dyspnea. In this article, a machine learning-based reconstruction of undersampled spiral k-space data is presented to enable free breathing real-time cardiac MRI with good image quality and short reconstruction times.

METHODS

Data were acquired in free breathing with a 2D spiral trajectory corrected by the gradient system transfer function. Undersampled data were reconstructed by a variational network (VN), which was specifically adapted to the non-Cartesian sampling pattern. The network was trained with data from 11 subjects. Subsequently, the imaging technique was validated in 14 subjects by quantifying the difference to a segmented reference acquisition, an expert reader study, and by comparing derived volumes and functional parameters with values obtained using the current clinical gold standard.

RESULTS

The scan time for the entire heart was below 1 min. The VN reconstructed data in about 0.9 s per image, which is considerably shorter than conventional model-based approaches. The VN furthermore performed better than a U-Net and not inferior to a low-rank plus sparse model in terms of achieved image quality. Functional parameters agreed, on average, with reference data.

CONCLUSIONS

The proposed VN method enables real-time cardiac imaging with both high spatial and temporal resolution in free breathing and with short reconstruction time.

A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images

Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 participants performing linguistically motivated speech tasks, alongside the corresponding public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each participant.