Pre-excitation gradients for eddy current nulled convex optimized diffusion encoding (Pre-ENCODE).

PURPOSE: To evaluate the use of pre-excitation gradients for eddy current-nulled convex optimized diffusion encoding (Pre-ENCODE) to mitigate eddy current-induced image distortions in diffusion-weighted MRI (DWI). METHODS: DWI sequences using monopolar (MONO), ENCODE, and Pre-ENCODE were evaluated in terms of the minimum achievable echo time (TE min $$ {}_{\mathrm{min}} $$ ) and eddy current-induced image distortions using simulations, phantom experiments, and in vivo DWI in volunteers ( N = 6 $$ N=6 $$ ). RESULTS: Pre-ENCODE provided a shorter TE min $$ {}_{\mathrm{min}} $$ than MONO (71.0 ± $$ \pm $$ 17.7ms vs. 77.6 ± $$ \pm $$ 22.9ms) and ENCODE (71.0 ± $$ \pm $$ 17.7ms vs. 86.2 ± $$ \pm $$ 14.2ms) in 100 % $$ \% $$ of the simulated cases for a commercial 3T MRI system with b-values ranging from 500 to 3000 s/mm 2 $$ {}^2 $$ and in-plane spatial resolutions ranging from 1.0 to 3.0mm 2 $$ {}^2 $$ . Image distortion was estimated by intravoxel signal variance between diffusion encoding directions near the phantom edges and was significantly lower with Pre-ENCODE than with MONO (10.1 % $$ \% $$ vs. 22.7 % $$ \% $$ , p = 6 - 5 $$ p={6}^{-5} $$ ) and comparable to ENCODE (10.1 % $$ \% $$ vs. 10.4 % $$ \% $$ , p = 0 . 12 $$ p=0.12 $$ ). In vivo measurements of apparent diffusion coefficients were similar in global brain pixels (0.37 [0.28,1.45] × 1 0 - 3 $$ \times 1{0}^{-3} $$ mm 2 $$ {}^2 $$ /s vs. 0.38 [0.28,1.45] × 1 0 - 3 $$ \times 1{0}^{-3} $$ mm 2 $$ {}^2 $$ /s, p = 0 . 25 $$ p=0.25 $$ ) and increased in edge brain pixels (0.80 [0.17,1.49] × 1 0 - 3 $$ \times 1{0}^{-3} $$ mm 2 $$ {}^2 $$ /s vs. 0.70 [0.18,1.48] × 1 0 - 3 $$ \times 1{0}^{-3} $$ mm 2 $$ {}^2 $$ /s, p = 0 . 02 $$ p=0.02 $$ ) for MONO compared to Pre-ENCODE. CONCLUSION: Pre-ENCODE mitigated eddy current-induced image distortions for diffusion imaging with a shorter TE min $$ {}_{\mathrm{min}} $$ than MONO and ENCODE.

Deep Learning-Based Reconstruction for Cardiac MRI: A Review.

Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of cardiovascular disease. Deep learning (DL) has recently revolutionized the field through image reconstruction techniques that allow unprecedented data undersampling rates. These fast acquisitions have the potential to considerably impact the diagnosis and treatment of cardiovascular disease. Herein, we provide a comprehensive review of DL-based reconstruction methods for CMR. We place special emphasis on state-of-the-art unrolled networks, which are heavily based on a conventional image reconstruction framework. We review the main DL-based methods and connect them to the relevant conventional reconstruction theory. Next, we review several methods developed to tackle specific challenges that arise from the characteristics of CMR data. Then, we focus on DL-based methods developed for specific CMR applications, including flow imaging, late gadolinium enhancement, and quantitative tissue characterization. Finally, we discuss the pitfalls and future outlook of DL-based reconstructions in CMR, focusing on the robustness, interpretability, clinical deployment, and potential for new methods.

Multishot Diffusion-Weighted MRI of the Breasts in the Supine vs. Prone Position.

BACKGROUND: Diffusion-weighted imaging (DWI) may allow for breast cancer screening MRI without a contrast injection. Multishot methods improve prone DWI of the breasts but face different challenges in the supine position. PURPOSE: To establish a multishot DWI (msDWI) protocol for supine breast MRI and to evaluate the performance of supine vs. prone msDWI. STUDY TYPE: Prospective. POPULATION: Protocol optimization: 10 healthy women (ages 22-56), supine vs. prone: 24 healthy women (ages 22-62) and five women (ages 29-61) with breast tumors. FIELD STRENGTH/SEQUENCE: 3-T, protocol optimization msDWI: free-breathing (FB) 2-shots, FB 4-shots, respiratory-triggered (RT) 2-shots, RT 4-shots, supine vs. prone: RT 4-shot msDWI, T2-weighted fast-spin echo. ASSESSMENT: Protocol optimization and supine vs. prone: three observers performed an image quality assessment of sharpness, aliasing, distortion (vs. T2), perceived SNR, and overall image quality (scale of 1-5). Apparent diffusion coefficients (ADCs) in fibroglandular tissue (FGT) and breast tumors were measured. STATISTICAL TESTS: Effect of study variables on dichotomized ratings (4/5 vs. 1/2/3) and FGT ADCs were assessed with mixed-effects logistic regression. Interobserver agreement utilized Gwet's agreement coefficient (AC). Lesion ADCs were assessed by Bland-Altman analysis and concordance correlation (ρc ). P value <0.05 was considered statistically significant. RESULTS: Protocol optimization: 4-shots significantly improved sharpness and distortion; RT significantly improved sharpness, aliasing, perceived SNR, and overall image quality. FGT ADCs were not significantly different between shots (P = 0.812), FB vs. RT (P = 0.591), or side (P = 0.574). Supine vs. prone: supine images were rated significantly higher for sharpness, aliasing, and overall image quality. FGT ADCs were significantly higher supine; lesion ADCs were highly correlated (ρc  = 0.92). DATA CONCLUSION: Based on image quality, supine msDWI outperformed prone msDWI. Lesion ADCs were highly correlated between the two positions, while FGT ADCs were higher in the supine position. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 1.

Accelerated two-dimensional phase-contrast for cardiovascular MRI using deep learning-based reconstruction with complex difference estimation.

PURPOSE: To develop and validate a deep learning-based reconstruction framework for highly accelerated two-dimensional (2D) phase contrast (PC-MRI) data with accurate and precise quantitative measurements. METHODS: We propose a modified DL-ESPIRiT reconstruction framework for 2D PC-MRI, comprised of an unrolled neural network architecture with a Complex Difference estimation (CD-DL). CD-DL was trained on 155 fully sampled 2D PC-MRI pediatric clinical datasets. The fully sampled data ( n = 29 $$ n=29 $$ ) was retrospectively undersampled (6-11 × $$ \times $$ ) and reconstructed using CD-DL and a parallel imaging and compressed sensing method (PICS). Measurements of peak velocity and total flow were compared to determine the highest acceleration rate that provided accuracy and precision within ± 5 % $$ \pm 5\% $$ . Feasibility of CD-DL was demonstrated on prospectively undersampled datasets acquired in pediatric clinical patients ( n = 5 $$ n=5 $$ ) and compared to traditional parallel imaging (PI) and PICS. RESULTS: The retrospective evaluation showed that 9 × $$ \times $$ accelerated 2D PC-MRI images reconstructed with CD-DL provided accuracy and precision (bias, [95 % $$ \% $$ confidence intervals]) within ± 5 % $$ \pm 5\% $$ . CD-DL showed higher accuracy and precision compared to PICS for measurements of peak velocity (2.8 % $$ \% $$ [ - 2 . 9 $$ -2.9 $$ , 4.5] vs. 3.9 % $$ \% $$ [ - 11 . 0 $$ -11.0 $$ , 4.9]) and total flow (1.8 % $$ \% $$ [ - 3 . 9 $$ -3.9 $$ , 3.4] vs. 2.9 % $$ \% $$ [ - 7 . 1 $$ -7.1 $$ , 6.9]). The prospective feasibility study showed that CD-DL provided higher accuracy and precision than PICS for measurements of peak velocity and total flow. CONCLUSION: In a retrospective evaluation, CD-DL produced quantitative measurements of 2D PC-MRI peak velocity and total flow with ≤ 5 % $$ \le 5\% $$ error in both accuracy and precision for up to 9 × $$ \times $$ acceleration. Clinical feasibility was demonstrated using a prospective clinical deployment of our 8 × $$ \times $$ undersampled acquisition and CD-DL reconstruction in a cohort of pediatric patients.

Optimization methods for magnetic resonance imaging gradient waveform design.

The development and implementation of novel MRI pulse sequences remains challenging and laborious. Gradient waveforms are typically designed using a combination of analytical and ad hoc methods to construct each gradient waveform axis independently. This strategy makes coding the pulse sequence complicated, in addition to being time inefficient. As a consequence, nearly all commercial MRI pulse sequences fail to maximize use of the available gradient hardware or efficiently mitigate physiological effects. This results in expensive MRI systems that underperform relative to their inherent hardware capabilities. To address this problem, a software solution is proposed that incorporates numerical optimization methods into MRI pulse sequence programming. Examples are shown for rotational variant vs. invariant waveform designs, acceleration nulled velocity encoding gradients, and mitigation of peripheral nerve stimulation for diffusion encoding. The application of optimization methods to MRI pulse sequence design incorporates gradient hardware limits and the prescribed MRI protocol parameters (e.g. field-of-view, resolution, gradient moments, and/or b-value) to simultaneously construct time-optimal gradient waveforms. In many cases, the resulting constrained gradient waveform design problem is convex and can be solved on-the-fly on the MRI scanner. The result is a set of multi-axis time-optimal gradient waveforms that satisfy the design constraints, thereby increasing SNR-efficiency. These optimization methods can also be used to mitigate imaging artifacts (e.g. eddy currents) or account for peripheral nerve stimulation. The result of the optimization method is to enable easier pulse sequence gradient waveform design and permit on-the-fly implementation for a range of MRI pulse sequences.

A gradient optimization toolbox for general purpose time-optimal MRI gradient waveform design.

PURPOSE: To introduce and demonstrate a software library for time-optimal gradient waveform optimization with a wide range of applications. The software enables direct on-the-fly gradient waveform design on the scanner hardware for multiple vendors. METHODS: The open-source gradient optimization (GrOpt) toolbox was implemented in C with both Matlab and Python wrappers. The toolbox enables gradient waveforms to be generated based on a set of constraints that define the features and encodings for a given acquisition. The GrOpt optimization routine is based on the alternating direction method of multipliers (ADMM). Additional constraints enable error corrections to be added, or patient comfort and safety to be adressed. A range of applications and compute speed metrics are analyzed. Finally, the method is implemented and tested on scanners from different vendors. RESULTS: Time-optimal gradient waveforms for different pulse sequences and the constraints that define them are shown. Additionally, the ability to add, arbitrary motion (gradient moment) compensation or limit peripheral nerve stimulation is demonstrated. There exists a trade-off between computation time and gradient raster time, but it was observed that acceptable gradient waveforms could be generated in 1-40 ms. Gradient waveforms generated and run on the different scanners were functionally equivalent, and the images were comparable. CONCLUSIONS: GrOpt is an open source toolbox that enables on-the-fly optimization of gradient waveform design, subject to a set of defined constraints. GrOpt was presented for a range of imaging applications, analyzed in terms of computational complexity, and implemented to run on the scanner for a multi-vendor demonstration.

Advantages of short repetition time resting-state functional MRI enabled by simultaneous multi-slice imaging.

BACKGROUND: Recent advancements in simultaneous multi-slice (SMS) imaging techniques have enabled whole-brain resting-state fMRI (rs-fMRI) scanning at sub-second temporal resolution, providing spectral ranges much wider than the typically used range of 0.01-0.1 Hz. However, the advantages of this accelerated acquisition for rs-fMRI have not been evaluated. NEW METHOD: In this study, we used SMS Echo Planar Imaging (EPI) to probe whole-brain functional connectivity with a short repetition time (TR = 350 ms) and compared it with standard EPI with a longer TR of 2000 ms. We determined the effect of scan length and investigated the temporal filtration strategies that optimize results based on metrics of signal-noise separation and test-retest reliability using both seed-based and independent component analysis (ICA). RESULTS: We found that use of either the entire frequency range of 0.01-1.4 Hz or the entire frequency range with the exclusion of typical cardiac and respiratory frequency values tended to provide the best functional connectivity maps. COMPARISON WITH EXISTING METHODS: We found that the SMS-acquired rs-fMRI scans had improved the signal-noise separation, while preserving the same level of test-retest reliability compared to conventional EPI, and enabled the detection of reliable functional connectivity networks with scan times as short as 3 min. CONCLUSIONS: Our findings suggest that whole-brain rs-fMRI studies may benefit from the increased temporal resolution enabled by the SMS-EPI acquisition, leading to drastic scan time reductions, which in turn should enable the more widespread use of rs-fMRI in clinical research protocols.

Toward personalised diffusion MRI in psychiatry: improved delineation of fibre bundles with the highest-ever angular resolution in vivo tractography.

Diffusion MRI (dMRI) tractography is a uniquely powerful tool capable of demonstrating structural brain network abnormalities across a range of psychiatric disorders; however, it is not currently clinically useful. This is because limitations on sensitivity effectively restrict its application to scientific studies of cohorts, rather than individual patients. Recent improvements in dMRI hardware, acquisition, processing and analysis techniques may, however, overcome these measurement limitations. We therefore acquired the highest-ever angular resolution in vivo tractographic data set, and used these data to ask the question: 'is cutting-edge, optimised dMRI now sensitive enough to measure brain network abnormalities at a level that may enable personalised psychiatry?' The fibre tracking performance of this 'gold standard' data set of 1150 unique directions (11 shells) was compared to a conventional 64-direction protocol (single shell) and a clinically practical, highly optimised and accelerated 9-min protocol of 140 directions (3 shells). Three major tracts of relevance to psychiatry were evaluated: the cingulate bundle, the uncinate fasciculus and the corticospinal tract. We found up to a 34-fold improvement in tracking accuracy using the 1150-direction data set compared to the 64-direction data set, while 140-direction data offered a maximum 17-fold improvement. We also observed between 20 and 50% improvements in tracking efficiency for the 140-direction data set, a finding we then replicated in a normal cohort (n = 53). We found evidence that lower angular resolution data may introduce systematic anatomical biases. These data highlight the imminent potential of dMRI as a clinically meaningful technique at a personalised level, and should inform current practice in clinical studies.

Hybrid-Space SENSE Reconstruction for Simultaneous Multi-Slice MRI.

Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.

Phase contrast MRI with flow compensation view sharing.

PURPOSE: To develop and evaluate a technique for accelerating phase contrast MRI (PC-MRI) acquisitions without significant compromise in flow quantification accuracy. METHODS: PC-MRI is commonly acquired using interleaved flow-compensated (FC) and flow-encoded (FE) echoes. We hypothesized that FC data, which represent background phase, do not change significantly over time. Therefore, we proposed to undersample the FC data and use an FC view sharing (FCVS) approach to synthesize a composite FC frame for each corresponding FE frame. FCVS was evaluated in a flow phantom and healthy volunteers and compared with a standard FC/FE PC-MRI. RESULTS: The FCVS sequence resulted in an error of 0.0% for forward flow and 2.0% for reverse flow volume when compared with FC/FE PC-MRI in a flow phantom. Measurements in the common carotid arteries showed that the FCVS method had -1.16 cm/s bias for maximum peak velocity and -0.019 mL bias in total flow, when compared with FC/FE with the same temporal resolution, but double the total acquisition time. These results represent ≤1.3% bias error in velocity and volumetric flow quantification. CONCLUSION: FCVS can accelerate PC-MRI acquisitions while maintaining flow and velocity measurement accuracy when there is limited temporal variation in the FC data.

Velocity encoding with the slice select refocusing gradient for faster imaging and reduced chemical shift-induced phase errors.

PURPOSE: To investigate a novel phase-contrast MRI velocity-encoding technique for faster imaging and reduced chemical shift-induced phase errors. METHODS: Velocity encoding with the slice select refocusing gradient achieves the target gradient moment by time shifting the refocusing gradient, which enables the use of the minimum in-phase echo time (TE) for faster imaging and reduced chemical shift-induced phase errors. Net forward flow was compared in 10 healthy subjects (N = 10) within the ascending aorta (aAo), main pulmonary artery (PA), and right/left pulmonary arteries (RPA/LPA) using conventional flow compensated and flow encoded (401 Hz/px and TE = 3.08 ms) and slice select refocused gradient velocity encoding (814 Hz/px and TE = 2.46 ms) at 3 T. RESULTS: Improved net forward flow agreement was measured across all vessels for slice select refocused gradient compared to flow compensated and flow encoded: aAo vs. PA (1.7% ± 1.9% vs. 5.8% ± 2.8%, P = 0.002), aAo vs. RPA + LPA (2.1% ± 1.7% vs. 6.0% ± 4.3%, P = 0.03), and PA vs. RPA + LPA (2.9% ± 2.1% vs. 6.1% ± 6.3%, P = 0.04), while increasing temporal resolution (35%) and signal-to-noise ratio (33%). CONCLUSION: Slice select refocused gradient phase-contrast MRI with a high receiver bandwidth and minimum in-phase TE provides more accurate and less variable flow measurements through the reduction of chemical shift-induced phase errors and a reduced TE/repetition time, which can be used to increase the temporal/spatial resolution and/or reduce breath hold durations.

Convex gradient optimization for increased spatiotemporal resolution and improved accuracy in phase contrast MRI.

PURPOSE: To evaluate convex gradient optimization (CVX) for increased spatiotemporal resolution and improved accuracy for phase-contrast MRI (PC-MRI). METHODS: A conventional flow-compensated and flow-encoded (FCFE) PC-MRI sequence was compared with a CVX PC-MRI sequence using numerical simulations, flow phantom experiments, and in vivo experiments. Flow measurements within the ascending aorta, main pulmonary artery, and right/left pulmonary arteries of normal volunteers (N = 10) were acquired at 3T and analyzed using a conventional FCFE sequence and a CVX sequence with either higher spatial resolution or higher temporal resolution. All sequences mitigated chemical shift-induced phase errors and used equivalent breath-hold durations. RESULTS: Chemical shift-optimized PC-MRI has increased sequence efficiency when using CVX, which can provide either higher spatial or higher temporal resolution compared with conventional FCFE PC-MRI. Numerical simulations, flow phantom experiments, and in vivo experiments indicate that CVX measurements of total flow and peak velocity are increased and more accurate when compared with FCFE. CONCLUSION: CVX PC-MRI increases sequence efficiency while reducing chemical shift-induced phase errors. This can be used to provide either higher spatial or higher temporal resolution than conventional chemical shift-mitigated PC-MRI methods to provide more accurate measurements of blood flow and peak velocity.

Chemical shift-induced phase errors in phase-contrast MRI.

Phase-contrast magnetic resonance imaging is subject to numerous sources of error, which decrease clinical confidence in the reported measures. This work outlines how stationary perivascular fat can impart a significant chemical shift induced phase-contrast magnetic resonance imaging measurement error using computational simulations, in vitro, and in vivo experiments. This chemical shift error does not subtract in phase difference processing, but can be minimized with proper parameter selection. The chemical shift induced phase errors largely depend on both the receiver bandwidth and the TE. Both theory and an in vivo comparison of the maximum difference in net forward flow between vessels with and without perivascular fat indicated that the effects of chemically shifted perivascular fat are minimized by the use of high bandwidth (814 Hz/px) and an in-phase TE (high BW-TE(IN)). In healthy volunteers (N = 10) high BW-TE(IN) significantly improves intrapatient net forward flow agreement compared with low bandwidth (401 Hz/px) and a mid-phase TE as indicated by significantly decreased measurement biases and limits of agreement for the ascending aorta (1.8 ± 0.5 mL vs. 6.4 ± 2.8 mL, P = 0.01), main pulmonary artery (2.0 ± 0.9 mL vs. 11.9 ± 5.8 mL, P = 0.04), the left pulmonary artery (1.3 ± 0.9 mL vs. 5.4 ± 2.5 mL, P = 0.003), and all vessels (1.7 ± 0.8 mL vs. 7.2 ± 4.4 mL, P = 0.001).