Motion-induced phase-corrected homodyne reconstruction for partial Fourier single-shot diffusion-weighted echo planar imaging of the liver

Partial Fourier encoding is popular in single-shot (ss) diffusion-weighted (DW) echo planar imaging (EPI) because it enables a shorter echo time (TE) and, hence, improves the signal-to-noise-ratio. Motion during diffusion encoding causes k-space shifting and dispersion, which compromises the quality of the homodyne reconstruction. This work provides a comprehensive understanding of the artifacts in homodyne reconstruction of partial Fourier ss-DW-EPI data in the presence of motion-induced phase and proposes the motion-induced phase-corrected homodyne (mpc-hdyne) reconstruction method to ameliorate these artifacts. Simulations with different types of motion-induced phase were performed to provide an understanding of the potential artifacts that occur in the homodyne reconstruction of partial Fourier ss-DW-EPI data. To correct for the artifacts, the mpc-hdyne reconstruction is proposed. The algorithm recenters k-space, updates the partial Fourier factor according to detected global k-space shifts, and removes low-resolution nonlinear phase before the conventional homodyne reconstruction. The mpc-hdyne reconstruction is tested on both simulation and in vivo data. Motion-induced phase can cause signal overestimation, worm artifacts, and signal loss in partial Fourier ss-DW-EPI data with the conventional homodyne reconstruction. Simulation and in vivo data showed that the proposed mpc-hdyne reconstruction ameliorated artifacts, yielding higher quality DW images compared with conventional homodyne reconstruction. Based on the understanding of the artifacts in homodyne reconstruction of partial Fourier ss-DW-EPI data, the mpc-hdyne reconstruction was proposed and showed superior performance compared with the conventional homodyne reconstruction on both simulation and in vivo data.

High-resolution prostate diffusion MRI using eddy current-nulled convex optimized diffusion encoding and random matrix theory-based denoising

OBJECTIVE

To develop and evaluate a technique combining eddy current-nulled convex optimized diffusion encoding (ENCODE) with random matrix theory (RMT)-based denoising to accelerate and improve the apparent signal-to-noise ratio (aSNR) and apparent diffusion coefficient (ADC) mapping in high-resolution prostate diffusion-weighted MRI (DWI). MATERIALS AND METHODS: Eleven subjects with clinical suspicion of prostate cancer were scanned at 3T with high-resolution (HR) (in-plane: 1.0 × 1.0 mm2) ENCODE and standard-resolution (1.6 × 2.2 mm2) bipolar DWI sequences (both had 7 repetitions for averaging, acquisition time [TA] of 5 min 50 s). HR-ENCODE was retrospectively analyzed using three repetitions (accelerated effective TA of 2 min 30 s). The RMT-based denoising pipeline utilized complex DWI signals and Marchenko-Pastur distribution-based principal component analysis to remove additive Gaussian noise in images from multiple coils, b-values, diffusion encoding directions, and repetitions. HR-ENCODE with RMT-based denoising (HR-ENCODE-RMT) was compared with HR-ENCODE in terms of aSNR in prostate peripheral zone (PZ) and transition zone (TZ). Precision and accuracy of ADC were evaluated by the coefficient of variation (CoV) between repeated measurements and mean difference (MD) compared to the bipolar ADC reference, respectively. Differences were compared using two-sided Wilcoxon signed-rank tests (P < 0.05 considered significant).

RESULTS

HR-ENCODE-RMT yielded 62% and 56% higher median aSNR than HR-ENCODE (b = 800 s/mm2) in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT achieved 63% and 70% lower ADC-CoV than HR-ENCODE in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT ADC and bipolar ADC had low MD of 22.7 × 10-6 mm2/s in PZ and low MD of 90.5 × 10-6 mm2/s in TZ.

CONCLUSIONS

HR-ENCODE-RMT can shorten the acquisition time and improve the aSNR of high-resolution prostate DWI and achieve accurate and precise ADC measurements in the prostate.

Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging

PURPOSE

To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions.

METHODS

A controlled breathing DWI acquisition was first employed at 3 T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC.

RESULTS

Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF.

CONCLUSION

Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800 s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.

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.

Characterization and correction of diffusion gradient-induced eddy currents in second-order motion-compensated echo-planar and spiral cardiac DTI

PURPOSE

Very high gradient amplitudes played out over extended time intervals as required for second-order motion-compensated cardiac DTI may violate the assumption of a linear time-invariant gradient system model. The aim of this work was to characterize diffusion gradient-related system nonlinearity and propose a correction approach for echo-planar and spiral spin-echo motion-compensated cardiac DTI.

METHODS

Diffusion gradient-induced eddy currents of 9 diffusion directions were characterized at b values of 150 s/mm² and 450 s/mm² for a 1.5 Tesla system and used to correct phantom, ex vivo, and in vivo motion-compensated cardiac DTI data acquired with echo-planar and spiral trajectories. Predicted trajectories were calculated using gradient impulse response function and diffusion gradient strength- and direction-dependent zeroth- and first-order eddy current responses. A reconstruction method was implemented using the predicted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>k</mml:mi></mml:mrow> <mml:annotation>$$ k $$</mml:annotation></mml:semantics> </mml:math> -space trajectories to additionally include off-resonances and concomitant fields. Resulting images were compared to a reference reconstruction omitting diffusion gradient-induced eddy current correction.

RESULTS

Diffusion gradient-induced eddy currents exhibited nonlinear effects when scaling up the gradient amplitude and could not be described by a 3D basis alone. This indicates that a gradient impulse response function does not suffice to describe diffusion gradient-induced eddy currents. Zeroth- and first-order diffusion gradient-induced eddy current effects of up to -1.7 rad and -16 to +12 rad/m, respectively, were identified. Zeroth- and first-order diffusion gradient-induced eddy current correction yielded improved image quality upon image reconstruction.

CONCLUSION

The proposed approach offers correction of diffusion gradient-induced zeroth- and first-order eddy currents, reducing image distortions to promote improvements of second-order motion-compensated spin-echo cardiac DTI.

A joint linear reconstruction for multishot diffusion weighted non-Carr-Purcell-Meiboom-Gill fast spin echo with full signal

PURPOSE

Diffusion weighted Fast Spin Echo (DW-FSE) is a promising approach for distortionless DW imaging that is robust to system imperfections such as eddy currents and off-resonance. Due to non-Carr-Purcell-Meiboom-Gill (CPMG) magnetization, most DW-FSE sequences discard a large fraction of the signal (2 - 2 × $$ \sqrt{2}-2\times $$ ), reducing signal-to-noise ratio (SNR) efficiency compared to DW-EPI. The full FSE signal can be preserved by quadratically incrementing the transmit phase of the refocusing pulses, but this method of resolving non-CPMG magnetization has only been applied to single-shot DW-FSE due to challenges associated with image reconstruction. We present a joint linear reconstruction for multishot quadratic phase increment data that addresses these challenges and corrects ghosting from both shot-to-shot phase and intrashot signal oscillations. Multishot imaging reduces T2 blur and joint reconstruction of shots improves g-factor performance. A thorough analysis on the condition number of the proposed linear system is described.

METHODS

A joint multishot reconstruction is derived from the non-CPMG signal model. Multishot quadratic phase increment DW-FSE was tested in a standardized diffusion phantom and compared to single-shot DW-FSE and DW-EPI in vivo in the brain, cervical spine, and prostate. The pseudo multiple replica technique was applied to generate g-factor and SNR maps.

RESULTS

The proposed joint shot reconstruction eliminates ghosting from shot-to-shot phase and intrashot oscillations. g-factor performance is improved compared to previously proposed reconstructions, permitting efficient multishot imaging. apparent diffusion coefficient estimates in phantom experiments and in vivo are comparable to those obtained with conventional methods.

CONCLUSION

Multi-shot non-CPMG DW-FSE data with full signal can be jointly reconstructed using a linear model.

MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

PURPOSE

Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time.

METHODS

We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps.

RESULTS

T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts.

CONCLUSION

We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.

Flow-compensated diffusion encoding in MRI for improved liver metastasis detection

Magnetic resonance (MR) diffusion-weighted imaging (DWI) is often used to detect focal liver lesions (FLLs), though DWI image quality can be limited in the left liver lobe owing to the pulsatile motion of the nearby heart. Flow-compensated (FloCo) diffusion encoding has been shown to reduce this pulsation artifact. The purpose of this prospective study was to intra-individually compare DWI of the liver acquired with conventional monopolar and FloCo diffusion encoding for assessing metastatic FLLs in non-cirrhotic patients. Forty patients with known or suspected multiple metastatic FLLs were included and measured at 1.5 T field strength with a conventional (monopolar) and a FloCo diffusion encoding EPI sequence (single refocused; b-values, 50 and 800 s/mm²). Two board-certified radiologists analyzed the DWI images independently. They issued Likert-scale ratings (1 = worst, 5 = best) for pulsation artifact severity and counted the difference of lesions visible at b = 800 s/mm² separately for small and large FLLs (i.e., < 1 cm or > 1 cm) and separately for left and right liver lobe. Differences between the two diffusion encodings were assessed with the Wilcoxon signed-rank test. Both readers found a reduction in pulsation artifact in the liver with FloCo encoding (p < 0.001 for both liver lobes). More small lesions were detected with FloCo diffusion encoding in both liver lobes (left lobe: six and seven additional lesions by readers 1 and 2, respectively; right lobe: five and seven additional lesions for readers 1 and 2, respectively). Both readers found one additional large lesion in the left liver lobe. Thus, flow-compensated diffusion encoding appears more effective than monopolar diffusion encoding for the detection of liver metastases.

Tractography methods and findings in brain tumors and traumatic brain injury

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

Quantitative diffusion MRI of the abdomen and pelvis

Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.

Cross-term-compensated gradient waveform design for tensor-valued diffusion MRI

Diffusion MRI uses magnetic field gradients to sensitize the signal to the random motion of spins. In addition to the prescribed gradient waveforms, background field gradients contribute to the diffusion weighting and thereby cause an error in the measured signal and consequent parameterization. The most prominent contribution to the error comes from so-called 'cross-terms.' In this work we present a novel gradient waveform design that enables diffusion encoding that cancels such cross-terms and yields a more accurate measurement. This is achieved by numerical optimization that maximizes encoding efficiency with a simultaneous constraint on the 'cross-term sensitivity' (c = 0). We found that the optimized cross-term-compensated waveforms were superior to previous cross-term-compensated designs for a wide range of waveform types that yield linear, planar, and spherical b-tensor encoding. The efficacy of the proposed design was also demonstrated in practical experiments using a clinical MRI system. The sensitivity to cross-terms was evaluated in a water phantom with a folded surface which provoked strong internal field gradients. In every comparison, the cross-term-compensated waveforms were robust to the effects of background gradients, whereas conventional designs were not. We also propose a method to measure background gradients from diffusion-weighted data, and show that cross-term-compensated waveforms produce parameters that are markedly less dependent on the background compared to non-compensated designs. Finally, we also used simulations to show that the proposed cross-term compensation was robust to background gradients in the interval 0 to 3 mT/m, whereas non-compensated designs were impacted in terms of a severe signal and parameter bias. In conclusion, we have proposed and demonstrated a waveform design that yields efficient cross-term compensation and facilitates accurate diffusion MRI in the presence of static background gradients regardless of their amplitude and direction. The optimization framework is compatible with arbitrary spin-echo sequence timing and RF events, b-tensor shapes, suppression of concomitant gradient effects and motion encoding, and is shared in open source.

Gradient waveform design for tensor-valued encoding in diffusion MRI

Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.

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.

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.

Prospective motion correction for diffusion weighted EPI of the brain using an optical markerless tracker

PURPOSE

To enable motion-robust diffusion weighted imaging of the brain using well-established imaging techniques.

METHODS

An optical markerless tracking system was used to estimate and correct for rigid body motion of the head in real time during scanning. The imaging coordinate system was updated before each excitation pulse in a single-shot EPI sequence accelerated by GRAPPA with motion-robust calibration. Full Fourier imaging was used to reduce effects of motion during diffusion encoding. Subjects were imaged while performing prescribed motion patterns, each repeated with prospective motion correction on and off.

RESULTS

Prospective motion correction with dynamic ghost correction enabled high quality DWI in the presence of fast and continuous motion within a 10° range. Images acquired without motion were not degraded by the prospective correction. Calculated diffusion tensors tolerated the motion well, but ADC values were slightly increased.

CONCLUSIONS

Prospective correction by markerless optical tracking minimizes patient interaction and appears to be well suited for EPI-based DWI of patient groups unable to remain still including those who are not compliant with markers.

Alternative approach of hepatocellular carcinoma surveillance: abbreviated MRI

This review focuses on emerging abbreviated magnetic resonance imaging (AMRI) surveillance of patients with chronic liver disease for hepatocellular carcinoma (HCC). This surveillance strategy has been proposed as a high-sensitivity alternative to ultrasound for identification of patients with early-stage HCC, particularly in patients with cirrhosis or obesity, in whom sonographic visualization of small tumors may be compromised. Three general AMRI approaches have been developed and studied in the literature - non-contrast AMRI, dynamic contrast-enhanced AMRI, and hepatobiliary phase contrast-enhanced AMRI - each comprising a small number of selected sequences specifically tailored for HCC detection. The rationale, general technique, advantages and disadvantages, and diagnostic performance of each AMRI approach is explained. Additionally, current gaps in knowledge and future directions are discussed. Based on emerging evidence, we cautiously recommend the use of AMRI for HCC surveillance in situations where ultrasound is compromised.

Extracting diffusion tensor fractional anisotropy and mean diffusivity from 3-direction DWI scans using deep learning

PURPOSE

To develop and evaluate machine-learning methods that reconstruct fractional anisotropy (FA) values and mean diffusivities (MD) from 3-direction diffusion MRI (dMRI) acquisitions.

METHODS

Two machine-learning models were implemented to map undersampled dMRI signals with high-quality FA and MD maps that were reconstructed from fully sampled DTI scans. The first model was a previously described multilayer perceptron (MLP), which maps signals and FA/MD values from a single voxel. The second was a convolutional neural network U-Net model, which maps dMRI slices to full FA/MD maps. Each method was trained on dMRI brain scans (N = 46), and reconstruction accuracies were compared with conventional linear-least-squares (LLS) reconstructions.

RESULTS

In an independent testing cohort (N = 20), 3-direction U-Net reconstructions had significantly lower absolute FA error than both 3-direction MLP (U-Net_3-dir : 0.06 ± 0.01 vs. MLP_3-dir : 0.08 ± 0.01, P < 1 × 10^-5 ) and 6-direction LLS (LLS_6-dir : 0.09 ± 0.03, P = 1 × 10^-5 ). The MD errors were not significantly different among 3-direction MLP (0.06 ± 0.01 × 10^-3 mm² /s), 3-direction U-Net (0.06 ± 0.01 × 10^-3 mm² /s), and 6-direction LLS (0.07 ± 0.02 × 10^-3 mm² /s, P > .1).

CONCLUSION

The proposed U-Net model reconstructed FA from 3-direction dMRI scans with improved accuracy compared with both a previously described MLP approach and LLS fitting from 6-direction scans. The MD reconstruction accuracies did not differ significantly between reconstructions.

Diffusion tensor magnetic resonance imaging of the postoperative spine with metallic implants

There has been a growing need to understand the mechanism of development of acute spinal cord injury (SCI) and to optimize treatment. The paramagnetic nature of metallic implants has hampered the application of diffusion tensor imaging (DTI) in postsurgical SCI monitoring. We describe here a successful implementation of spinal DTI in postsurgical SCI patients. Data were acquired using a single-shot turbo-spin-echo sequence, where an extra gradient is applied before the refocusing pulse train to eliminate contributions from the non-Carr-Purcell-Meiboom-Gill components following a diffusion preparation block where a single-spin echo scheme is deployed. The DTI images were acquired in axial orientation with a 2 x 2 x 4 mm³ resolution and a total of 18 slices. Diffusion gradients were applied in six directions with b values of 0 and 600 seconds/mm² . The whole scan took ~10 minutes. The sequence was compared with SENSE-DW-EPI and ZOOM-DW-EPI on a phantom, eight patients with either anterior or posterior titanium alloy implants, and a pork loin with a similar implant. The protocol resulted in dramatically reduced geometric distortions compared with routine imaging sequences, however, the SNR efficiency was compromised. The spinal cord signal displacement was 0.68±1.00 mm (mean±SD, n = 8) for the proposed protocol, and 5.14±3.07 and 2.82±1.60 mm for the SENSE-DW-EPI and ZOOM-DW-EPI sequences, respectively. Fiber tracking was achieved in the presence of implants, which in one case was accompanied by central spinal cord caviation. Mathematical analysis concluded that the proposed protocol would be generally applicable in the spinal cord when the titanium alloy implant is ~15 mm away (<0.5 kHz B₀ field drift). The protocol described is capable of DTI in postsurgery SCI patients with metallic implants at sufficient resolution and SNR.

Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding

BACKGROUND

Prostate diffusion-weighted imaging (DWI) using monopolar encoding is sensitive to eddy-current-induced distortion artifacts. Twice-refocused bipolar encoding suppresses eddy current artifacts, but increases echo time (TE), leading to lower signal-to-noise ratio (SNR). Optimization of the diffusion encoding might improve prostate DWI.

PURPOSE

To evaluate eddy current nulled convex optimized diffusion encoding (ENCODE) for prostate DWI with minimal TE.

STUDY TYPE

Prospective cohort study.

POPULATION

A diffusion phantom, an ex vivo prostate specimen, 10 healthy male subjects (27 ± 3 years old), and five prostate cancer patients (62 ± 7 years old).

FIELD STRENGTH/SEQUENCE

3T; single-shot spin-echo echoplanar DWI.

ASSESSMENT

Eddy-current artifacts, TE, SNR, apparent diffusion coefficient (ADC), and image quality scores from three independent readers were compared between monopolar, bipolar, and ENCODE prostate DWI for standard-resolution (1.6 × 1.6 mm² , partial Fourier factor [pF] = 6/8) and higher-resolution protocols (1.6 × 1.6 mm² , pF = off; 1.0 × 1.0 mm² , pF = 6/8).

STATISTICAL TESTING

SNR and ADC differences between techniques were tested with Kruskal-Wallis and Wilcoxon signed-rank tests (P < 0.05 considered significant).

RESULTS

Eddy current suppression with ENCODE was comparable to bipolar encoding (mean coefficient of variation across three diffusion directions of 9.4% and 9%). For a standard-resolution protocol, ENCODE achieved similar TE as monopolar and reduced TE by 14 msec compared to bipolar, resulting in 27% and 29% higher mean SNR in prostate transition zone (TZ) and peripheral zone (PZ) (P < 0.05) compared to bipolar, respectively. For higher-resolution protocols, ENCODE achieved the shortest TE (67 msec), with 17-21% and 58-70% higher mean SNR compared to monopolar (TE = 77 msec) and bipolar (TE = 102 msec) in PZ and TZ (P < 0.05). No significant differences were found in mean TZ (P = 0.91) and PZ ADC (P = 0.94) between the three techniques. ENCODE achieved similar or higher image quality scores than bipolar DWI in patients, with mean intraclass correlation coefficient of 0.77 for overall quality between three independent readers.

DATA CONCLUSION

ENCODE minimizes TE (improves SNR) and reduces eddy-current distortion for prostate DWI compared to monopolar and bipolar encoding.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1526-1539.

Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding

PURPOSE

Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients.

METHODS

The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain.

RESULTS

Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m)2 ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory.

CONCLUSION

Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.

Distortion correction for high-resolution single-shot EPI DTI using a modified field-mapping method

PURPOSE

The widely used single-shot EPI (SS-EPI) diffusion tensor imaging (DTI) suffers from strong image distortion due to B₀ inhomogeneity, especially for high-resolution imaging. Traditional methods such as the field-mapping method and the top-up method have various deficiencies in high-resolution SS-EPI DTI distortion correction. This study aims to propose a robust distortion correction approach, which combines the advantages of traditional methods and overcomes their deficiencies, for high-resolution SS-EPI DTI.

METHODS

The proposed correction method is based on the echo planar spectroscopic imaging field-mapping followed by an intensity correction procedure. To evaluate the efficacy of distortion correction, the proposed method was compared with the conventional field-mapping method and the top-up method, using a newly developed quantitative evaluation framework. The correction results were also compared with multi-shot EPI DTI to investigate whether the proposed method can provide high-resolution SS-EPI DTI with high geometric fidelity and high time efficiency.

RESULTS

The results show that accurate field-mapping and intensity correction are critical to distortion correction in high-resolution SS-EPI DTI. The proposed method can provide more precise field maps and better correction results than the other two methods (p < 0.0001), and the corrected images show higher geometric fidelity than those from MS-EPI DTI.

CONCLUSION

An effective method is proposed to reduce image distortion in high-resolution SS-EPI DTI. It is practical to achieve high-resolution DTI with high time efficiency and high structure accuracy using this method.

Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems

Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.

Motion-robust and blood-suppressed M1-optimized diffusion MR imaging of the liver

PURPOSE

To develop motion-robust, blood-suppressed diffusion-weighted imaging (DWI) of the liver with optimized diffusion encoding waveforms and evaluate the accuracy and reproducibility of quantitative apparent diffusion coefficient (ADC) measurements.

METHODS

A novel approach for the design of diffusion weighting waveforms, termed M1-optimized diffusion imaging (MODI), is proposed. MODI includes an echo time-optimized motion-robust diffusion weighting gradient waveform design, with a small nonzero first-moment motion sensitivity (M1) value to enable blood signal suppression. Experiments were performed in eight healthy volunteers and five patient volunteers. In each case, DW images and ADC maps were compared between acquisitions using standard monopolar waveforms, motion moment-nulled (M1-nulled and M1-M2-nulled) waveforms, and the proposed MODI approach.

RESULTS

Healthy volunteer experiments using MODI showed no significant ADC bias in the left lobe relative to the right lobe (p < .05) demonstrating robustness to cardiac motion, and no significant ADC bias with respect to monopolar-based ADC measured in the right lobe (p < .05), demonstrating blood signal suppression. In contrast, monopolar-based ADC showed significant bias in the left lobe relative to the right lobe (p < .01) due to its sensitivity to motion, and both M1-nulled and M1-M2-nulled-based ADC showed significant bias (p < .01) due to the lack of blood suppression. Preliminary patient results also suggest MODI may enable improved visualization and quantitative assessment of lesions throughout the entire liver.

CONCLUSIONS

This novel method for diffusion gradient waveform design enables DWI of the liver with high robustness to motion and suppression of blood signals, overcoming the limitations of conventional monopolar waveforms and moment-nulled waveforms, respectively.

Eddy current nulled constrained optimization of isotropic diffusion encoding gradient waveforms

PURPOSE

Isotropic diffusion encoding efficiently encodes additional microstructural information in combination with conventional linear diffusion encoding. However, the gradient-intensive isotropic diffusion waveforms generate significant eddy currents, which cause image distortions. The purpose of this study is to present a method for designing isotropic diffusion encoding waveforms with intrinsic eddy current nulling.

METHODS

Eddy current nulled gradient waveforms were designed using a constrained optimization waveform for a 3T GE Premier MRI system. Encoding waveforms were designed for a variety of eddy current null times and sequence timings to evaluate the achievable b-value. Waveforms were also tested with both eddy current nulling and concomitant field compensation. Distortion reduction was tested in both phantoms and the in vivo human brain.

RESULTS

The feasibility of isotropic diffusion encoding with intrinsic correction of eddy current distortion and signal bias from concomitant fields was demonstrated. The constrained optimization algorithm produced gradient waveforms with the specified eddy current null times. The reduction in the achievable diffusion weighting was dependent on the number of eddy current null times. A reduction in the eddy current-induced image distortions was observed in both phantoms and in vivo human subjects.

CONCLUSION

The proposed framework allows the design of isotropic diffusion-encoding sequences with reduced image distortion.

Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks

PURPOSE

The purpose of this study was to develop a neural network that accurately performs diffusion tensor imaging (DTI) reconstruction from highly accelerated scans.

MATERIALS AND METHODS

This retrospective study was conducted using data acquired between 2013 and 2018 and was approved by the local institutional review board. DTI acquired in healthy volunteers (N = 10) was used to train a neural network, DiffNet, to reconstruct fractional anisotropy (FA) and mean diffusivity (MD) maps from small subsets of acquired DTI data with between 3 and 20 diffusion-encoding directions. FA and MD maps were then reconstructed in volunteers and in patients with glioblastoma multiforme (GBM, N = 12) using both DiffNet and conventional reconstructions. Accuracy and precision were quantified in volunteer scans and compared between reconstructions. The accuracy of tumor delineation was compared between reconstructed patient data by evaluating agreement between DTI-derived tumor volumes and volumes defined by contrast-enhanced T1-weighted MRI. Comparisons were performed using areas under the receiver operating characteristic curves (AUC).

RESULTS

DiffNet FA reconstructions were more accurate and precise compared with conventional reconstructions for all acceleration factors. DiffNet permitted reconstruction with only three diffusion-encoding directions with significantly lower bias than the conventional method using six directions (0.01 ± 0.01 vs 0.06 ± 0.01, P < 0.001). While MD-based tumor delineation was not substantially different with DiffNet (AUC range: 0.888-0.902), DiffNet FA had higher AUC than conventional reconstructions for fixed scan time and achieved similar performance with shorter scans (conventional, six directions: AUC = 0.926, DiffNet, three directions: AUC = 0.920).

CONCLUSION

DiffNet improved DTI reconstruction accuracy, precision, and tumor delineation performance in GBM while permitting reconstruction from only three diffusion-encoding directions.&!#6.

Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling

PURPOSE

To present a novel Optimized Diffusion-weighting Gradient waveform Design (ODGD) method for the design of minimum echo time (TE), bulk motion-compensated, and concomitant gradient (CG)-nulling waveforms for diffusion MRI.

METHODS

ODGD motion-compensated waveforms were designed for various moment-nullings M_n (n = 0, 1, 2), for a range of b-values, and spatial resolutions, both without (ODGD-M_n ) and with CG-nulling (ODGD-M_n -CG). Phantom and in-vivo (brain and liver) experiments were conducted with various ODGD waveforms to compare motion robustness, signal-to-noise ratio (SNR), and apparent diffusion coefficient (ADC) maps with state-of-the-art waveforms.

RESULTS

ODGD-M_n and ODGD-M_n -CG waveforms reduced the TE of state-of-the-art waveforms. This TE reduction resulted in significantly higher SNR (P < 0.05) in both phantom and in-vivo experiments. ODGD-M₁ improved the SNR of BIPOLAR (42.8 ± 5.3 vs. 32.9 ± 3.3) in the brain, and ODGD-M₂ the SNR of motion-compensated (MOCO) and Convex Optimized Diffusion Encoding-M₂ (CODE-M₂ ) (12.3 ± 3.6 vs. 9.7 ± 2.9 and 10.2 ± 3.4, respectively) in the liver. Further, ODGD-M₂ also showed excellent motion robustness in the liver. ODGD-M_n -CG waveforms reduced the CG-related dephasing effects of non CG-nulling waveforms in phantom and in-vivo experiments, resulting in accurate ADC maps.

CONCLUSIONS

ODGD waveforms enable motion-robust diffusion MRI with reduced TEs, increased SNR, and reduced ADC bias compared to state-of-the-art waveforms in theoretical results, simulations, phantoms and in-vivo experiments.