Motion artifact reduction of diffusion-weighted MRI of the liver: use of velocity-compensated diffusion gradients combined with tetrahedral gradients

Reproducibility of liver ADC measurements using first moment optimized diffusion imaging

PURPOSE

Cardiac-induced liver motion can bias liver ADC measurements and compromise reproducibility. The purpose of this work was to enable motion-robust DWI on multiple MR scanners and assess reproducibility of the resulting liver ADC measurements.

METHODS

First moment-optimized diffusion imaging (MODI) was implemented on three MR scanners with various gradient performances and field strengths. MODI-DWI and conventional Stejskal-Tanner monopolar (MONO) DWI were acquired in eight (N = 8) healthy volunteers on each scanner, and DWI repetitions were combined using three different averaging methods. For each combination of scanner, acquisition, and averaging method, ADC measurements from each liver segment were collected. Systematic differences in ADC values between scanners and methods were assessed with linear mixed effects modeling, and reproducibility was quantified via reproducibility coefficients.

RESULTS

MODI reduced left-right liver lobe ADC bias from 0.43 × 10-3 mm2/s (MONO) to 0.19 × 10-3 mm2/s (MODI) when simple (unweighted) repetition averaging was used. The bias was reduced from 0.23 × 10-3 mm2/s to 0.06 × 10-3 mm2/s using weighted averaging, and 0.14 × 10-3 mm2/s to 0.01 × 10-3 mm2/s using squared weighted averaging. There was no significant difference in ADC measurements between field strengths or scanner gradient performance. MODI improved reproducibility coefficients compared to MONO: 0.84 × 10-3 mm2/s vs. 0.63 × 10-3 mm2/s (MODI vs. MONO) for simple averaging, 0.66 × 10-3 mm2/s vs. 0.50 × 10-3 mm2/s for weighted averaging, and 0.61 × 10-3 mm2/s vs. 0.47 × 10-3 mm2/s for squared weighted averaging.

CONCLUSION

The feasibility of motion-robust liver DWI using MODI was demonstrated on multiple MR scanners. MODI improved interlobar agreement and reproducibility of ADC measurements in a healthy cohort.

Relevance of lesion size in navigator-triggered and free-breathing diffusion-weighted liver MRI

OBJECTIVES

The purpose of this study was to investigate the relevance of focal liver lesions (FLL) size for lesion detection comparing navigator triggering (TRIG) to free breathing (FB) liver Diffusion-weighted magnetic resonance imaging (DWI).

MATERIALS AND METHOD

Patients with known or suspected FLL were prospectively (registry number 276_19 B) included from October to December 2019 in this study, out of which 32 had liver lesions. Echo planar spin-echo DWI data both with TRIG and FB were with approximately constant acquisition times acquired at 1.5 T. Lesions were segmented in the b = 800 s/mm² images in both the TRIG and FB images. The lesion size, location (liver segment), liver lesion visibility, as well as contrast-to-noise ratio (CNR) were recorded. The CNR was assessed with the Wilcoxon-Mann-Whitney test and the number of visible lesions with the Fisher test.

RESULTS

Data from 43 patients (22 female) were analyzed. The mean patient age was 58 ± 14 years. A total of 885 FLL (Ntotal) were segmented. Among these, 811 lesions (Nboth) were detected with TRIG and FB, 65 lesions exclusively with TRIG (NTRIG_Only), and nine exclusively in FB (NFB_Only). The largest additional lesion in TRIG/FB had a diameter of 10.4 mm/7.6 mm. The number of additional lesions detected with TRIG decreased with size. Among all lesions ≤ 4.7 mm, the relative number of additional lesions was 15.6%. Additional lesions were found in all liver segments with TRIG. In the left liver lobe, the relative proportion was 9.2%, and in the right liver lobe 5.4%. CNR and visibility were significantly higher in TRIG than in FB (p < 0.001). In relation to size, the difference is significant in terms of visibility and CNR for lesion diameters ≤ 8 mm.

CONCLUSION

Respiration triggering can improve the detection of small liver lesions with diameters up to approx. 1 cm in the whole liver.

KEY POINTS

Question Can respiration triggering (TRIG) improve the detection of small FLL compared to FB diffusion-weighted imaging? Findings Among 885 segmented FLL, TRIG was superior to FB for lesions smaller than 8 mm and had improved CNR and visibility. Clinical relevance Diffusion-weighted magnetic resonance imaging is used for the detection of focal liver lesions and image quality is influenced by breathing motion. Navigator triggering becomes more important for smaller lesions, and seems recommendable for the detection of small focal liver lesions.

Phase stabilization with motion compensated diffusion weighted imaging

PURPOSE

Diffusion encoding gradient waveforms can impart intra-voxel and inter-voxel dephasing owing to bulk motion, limiting achievable signal-to-noise and complicating multishot acquisitions. In this study, we characterize improvements in phase consistency via gradient moment nulling of diffusion encoding waveforms.

METHODS

Healthy volunteers received neuro (N = 10 $$ N=10 $$ ) and cardiac (N = 10 $$ N=10 $$ ) MRI. Three gradient moment nulling levels were evaluated: compensation for position (M 0 $$ {M}_0 $$ ), position + velocity (M 1 $$ {M}_1 $$ ), and position + velocity + acceleration (M 1 + M 2 $$ {M}_1+{M}_2 $$ ). Three experiments were completed: (Exp-1) Fixed Trigger Delay Neuro DWI; (Exp-2) Mixed Trigger Delay Neuro DWI; and (Exp-3) Fixed Trigger Delay Cardiac DWI. Significant differences (p < 0 . 05 $$ p<0.05 $$ ) of the temporal phase SD between repeated acquisitions and the spatial phase gradient across a given image were assessed.

RESULTS

M 0 $$ {M}_0 $$ moment nulling was a reference for all measures. In Exp-1, temporal phase SD forG z $$ {G}_z $$ diffusion encoding was significantly reduced withM 1 $$ {M}_1 $$ (35% of t-tests) andM 1 + M 2 $$ {M}_1+{M}_2 $$ (68% of t-tests). The spatial phase gradient was reduced in 23% of t-tests forM 1 $$ {M}_1 $$ and 2% of cases forM 1 + M 2 $$ {M}_1+{M}_2 $$ . In Exp-2, temporal phase SD significantly decreased withM 1 + M 2 $$ {M}_1+{M}_2 $$ gradient moment nulling only forG z $$ {G}_z $$ (83% of t-tests), but spatial phase gradient significantly decreased with onlyM 1 $$ {M}_1 $$ (50% of t-tests). In Exp-3,M 1 + M 2 $$ {M}_1+{M}_2 $$ gradient moment nulling significantly reduced temporal phase SD and spatial phase gradients (100% of t-tests), resulting in less signal attenuation and more accurate ADCs.

CONCLUSION

We characterized gradient moment nulling phase consistency for DWI. Using M1 for neuroimaging and M1 + M2 for cardiac imaging minimized temporal phase SDs and spatial phase gradients.

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.

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.

Motion-compensated diffusion encoding in multi-shot human brain acquisitions: Insights using high-performance gradients

PURPOSE

To evaluate the utility of up to second-order motion-compensated diffusion encoding in multi-shot human brain acquisitions.

METHODS

Experiments were performed with high-performance gradients using three forms of diffusion encoding motion-compensated through different orders: conventional zeroth-order-compensated pulsed gradients (PG), first-order-compensated gradients (MC1), and second-order-compensated gradients (MC2). Single-shot acquisitions were conducted to correlate the order of motion compensation with resultant phase variability. Then, multi-shot acquisitions were performed at varying interleaving factors. Multi-shot images were reconstructed using three levels of shot-to-shot phase correction: no correction, channel-wise phase correction based on FID navigation, and correction based on explicit phase mapping (MUSE).

RESULTS

In single-shot acquisitions, MC2 diffusion encoding most effectively suppressed phase variability and sensitivity to brain pulsation, yielding residual variations of about 10° and of low spatial order. Consequently, multi-shot MC2 images were largely satisfactory without phase correction and consistently improved with the navigator correction, which yielded repeatable high-quality images; contrarily, PG and MC1 images were inadequately corrected using the navigator approach. With respect to MUSE reconstructions, the MC2 navigator-corrected images were in close agreement for a standard interleaving factor and considerably more reliable for higher interleaving factors, for which MUSE images were corrupted. Finally, owing to the advanced gradient hardware, the relative SNR penalty of motion-compensated diffusion sensitization was substantially more tolerable than that faced previously.

CONCLUSION

Second-order motion-compensated diffusion encoding mitigates and simplifies shot-to-shot phase variability in the human brain, rendering the multi-shot acquisition strategy an effective means to circumvent limitations of retrospective phase correction methods.

Precision of liver and pancreas apparent diffusion coefficients using motion-compensated gradient waveforms in DWI

BACKGROUND

Diffusion weighted imaging (DWI) with optimized motion-compensated gradient waveforms reduces signal dropouts in the liver and pancreas caused by cardiovascular-associated motion, however its precision is unknown. We hypothesized that DWI with motion-compensated DW gradient waveforms would improve apparent diffusion coefficient (ADC)-repeatability and inter-reader reproducibility compared to conventional DWI in these organs.

METHODS

In this IRB-approved, prospective, single center study, subjects recruited between October 2019 and March 2020 were scanned twice on a 3 T scanner, with repositioning between test and retest. Each scan included two respiratory-triggered DWI series with comparable acquisition time: 1) conventional (monopolar) 2) motion- compensated diffusion gradients. Three readers measured ADC values. One-way ANOVA, Bland-Altman analysis were used for statistical analysis.

RESULTS

Eight healthy participants (4 male/4 female), with a mean age of 29 ± 4 years, underwent the liver and pancreas MRI protocol. Four patients with liver metastases (2 male/2 female) with a mean age of 58 ± 5 years underwent the liver MRI protocol. In healthy participants, motion-compensated DWI outperformed conventional DWI with mean repeatability coefficient of 0.14 × 10-3 (CI:0.12-0.17) vs. 0.31 × 10-3 (CI:0.27-0.37) mm2/s for liver, and 0.11 × 10-3 (CI:0.08-0.15) vs. 0.34 × 10-3 (CI:0.27-0.49) mm2/s for pancreas; and with mean reproducibility coefficient of 0.20 × 10-3 (CI:0.18-0.23) vs. 0.51 × 10-3 (CI:0.46-0.58) mm2/s for liver, and 0.16 × 10-3 (CI:0.13-0.20) vs. 0.42 × 10-3 (CI:0.34-0.52) mm2/s for pancreas. In patients, improved repeatability was observed for motion-compensated DWI in comparison to conventional with repeatability coefficient of 0.51 × 10- 3 mm2/s (CI:0.35-0.89) vs. 0.70 × 10-3 mm2/s (CI:0.49-1.20).

CONCLUSION

Motion-compensated DWI enhances the precision of ADC measurements in the liver and pancreas compared to conventional DWI.

Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver

PURPOSE

This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm in order to enhance the image quality of diffusion-weighted liver images and, in particular, to reduce the pulsation-induced signal loss occurring predominantly in the left liver lobe.

METHODS

Data from 40 patients with liver lesions were used. For the conventional approach, the best-suited out of five examined algorithms was chosen. For the deep learning approach, a U-Net was trained. Instead of learning "gold-standard" target images, the network was trained to optimize four image features (lesion CNR, vessel darkness, data consistency, and pulsation artifact reduction), which could be assessed quantitatively using manually drawn ROIs. A quality score was calculated from these four features. As an additional quality assessment, three radiologists rated different features of the resulting images.

RESULTS

The conventional approach could substantially increase the lesion CNR and reduce the pulsation-induced signal loss. However, the vessel darkness was reduced. The deep learning approach increased the lesion CNR and reduced the signal loss to a slightly lower extent, but it could additionally increase the vessel darkness. According to the image quality score, the quality of the deep-learning images was higher than that of the images obtained using the conventional approach. The radiologist ratings were mostly consistent with the quantitative scores, but the overall quality ratings differed among the readers.

CONCLUSION

Unlike the conventional algorithm, the deep-learning algorithm increased the vessel darkness. Therefore, it may be a viable alternative to conventional algorithms.

Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs

PURPOSE

The study aims to develop easy-to-implement concomitant field-compensated gradient waveforms with varying velocity-weighting (M1) and acceleration-weighting (M2) levels and to evaluate their efficacy in correcting signal dropouts and preserving the black-blood state in liver diffusion-weighted imaging. Additionally, we seek to determine an optimal degree of compensation that minimizes signal dropouts while maintaining blood signal suppression.

METHODS

Numerically optimized gradient waveforms were adapted using a novel method that allows for the simultaneous tuning of M1- and M2-weighting by changing only one timing variable. Seven healthy volunteers underwent diffusion-weighted magnetic resonance imaging (DWI) with five diffusion encoding schemes (monopolar, velocity-compensated (M1 = 0), acceleration-compensated (M1 = M2 = 0), 84%-M1-M2-compensated, 67%-M1-M2-compensated) at b-values of 50 and 800 s/mm2 at a constant echo time of 70 ms. Signal dropout correction and apparent diffusion coefficients (ADCs) were quantified using regions of interest in the left and right liver lobe. The blood appearance was evaluated using two five-point Likert scales.

RESULTS

Signal dropout was more pronounced in the left lobe (19%-42% less signal than in the right lobe with monopolar scheme) and best corrected by acceleration-compensation (8%-10% less signal than in the right lobe). The black-blood state was best with monopolar encodings and decreased significantly (p < 0.001) with velocity- and/or acceleration-compensation. The partially M1-M2-compensated encoding schemes could restore the black-blood state again. Strongest ADC bias occurred for monopolar encodings (difference between left/right lobe of 0.41 μm2/ms for monopolar vs. < 0.12 μm2/ms for the other encodings).

CONCLUSION

All of the diffusion encodings used in this study demonstrated suitability for routine DWI application. The results indicate that a perfect value for the level of M1-M2-compensation does not exist. However, among the examined encodings, the 84%-M1-M2-compensated encodings provided a suitable tradeoff.

Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver

PURPOSE

To evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques.

METHODS

DW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference.

RESULTS

In the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes ( p = 0 . 0078 $$ p=0.0078 $$ , reproducibility coefficients (RPC) =  470 × 1 0 - 6 $$ 470\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for monopolar-msEPI), while M1-optimized waveforms showed more reproducible ADC values ( p = 0 . 29 $$ p=0.29 $$ , RPC = 220 × 1 0 - 6 $$ \mathrm{RPC}=220\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for M1-optimized-msEPI). In phantom and healthy volunteer studies, motion-robust acquisitions with msEPI showed significantly reduced image distortion ( p < 0 . 001 $$ p<0.001 $$ ) compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1-optimized waveforms with msEPI.

CONCLUSION

Synergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

Diffusion Weighted Imaging of the Abdomen and Pelvis: Recent Technical Advances and Clinical Applications

Diffusion weighted imaging (DWI) serves as one of the most important functional magnetic resonance imaging techniques in abdominal and pelvic imaging. It is designed to reflect the diffusion of water molecules and is particularly sensitive to the malignancies. Yet, the limitations of image distortion and artifacts in single-shot DWI may hamper its widespread use in clinical practice. With recent technical advances in DWI, such as simultaneous multi-slice excitation, computed or reduced field-of-view techniques, as well as advanced shimming methods, it is possible to achieve shorter acquisition time, better image quality, and higher robustness in abdominopelvic DWI. This review discussed the recent advances of each DWI approach, and highlighted its future perspectives in abdominal and pelvic imaging, hoping to familiarize physicians and radiologists with the technical improvements in this field and provide future research directions.

Motion compensated renal diffusion weighted imaging

PURPOSE

To assess the effect of respiratory motion and cardiac driven pulsation in renal DWI and to examine asymmetrical velocity-compensated diffusion encoding waveforms for robust ADC mapping in the kidneys.

METHODS

The standard monopolar Stejskal-Tanner pulsed gradient spin echo (pgse) and the asymmetric bipolar velocity-compensated (asym-vc) diffusion encoding waveforms were used for coronal renal DWI at 3T. The robustness of the ADC quantification in the kidneys was tested with the aforementioned waveforms in respiratory-triggered and breath-held cardiac-triggered scans at different trigger delays in 10 healthy subjects.

RESULTS

The pgse waveform showed higher ADC values in the right kidney at short trigger delays in comparison to longer trigger delays in the respiratory triggered scans when the diffusion gradient was applied in the feet-head (FH) direction. The coefficient of variation over all respiratory trigger delays, averaged over all subjects was 0.15 for the pgse waveform in the right kidney when diffusion was measured in the FH direction; the corresponding coefficient of variation for the asym-vc waveform was 0.06. The effect of cardiac driven pulsation was found to be small in comparison to the effect of respiratory motion.

CONCLUSION

Short trigger delays in respiratory-triggered scans can cause higher ADC values in comparison to longer trigger delays in renal DWI, especially in the right kidney when diffusion is measured in the FH direction. The asym-vc waveform can reduce ADC variation due to respiratory motion in respiratory-triggered scans at the cost of reduced SNR compared to the pgse waveform.

Reduction of the cardiac pulsation artifact and improvement of lesion conspicuity in flow‐compensated diffusion images in the liver—A quantitative evaluation of postprocessing algorithms

PURPOSE

To enhance image quality of flow-compensated diffusion-weighted liver MRI data by increasing the lesion conspicuity and reducing the cardiac pulsation artifact using postprocessing algorithms.

METHODS

Diffusion-weighted image data of 40 patients with liver lesions had been acquired at 1.5 T. These data were postprocessed with 5 different algorithms (weighted averaging, p-mean, percentile, outlier exclusion, and exception set). Four image properties of the postprocessed data were evaluated for optimizing the algorithm parameters. These properties were the lesion to tissue contrast-to-noise ratio (CNR), the reduction of the cardiac pulsation artifact, the data consistency, and the vessel darkness. They were combined into a total quality score ( Q total , $$ {Q}_{\mathrm{total}}, $$ set to 1 for the trace-weighted reference image), which was used to rate the image quality objectively.

RESULTS

The weighted averaging algorithm performed best according to the total quality score ( Q total = 1.111 ± 0.067 $$ {Q}_{\mathrm{total}}=1.111\pm 0.067 $$ ). The further ranking was outlier exclusion algorithm ( Q total = 1.086 ± 0.061 $$ {Q}_{\mathrm{total}}=1.086\pm 0.061 $$ ), p-mean algorithm ( Q total = 1.045 ± 0.049 $$ {Q}_{\mathrm{total}}=1.045\pm 0.049 $$ ), percentile algorithm ( Q total = 1.012 ± 0.049 $$ {Q}_{\mathrm{total}}=1.012\pm 0.049 $$ ), and exception set algorithm ( Q total = 0.957 ± 0.027 $$ {Q}_{\mathrm{total}}=0.957\pm 0.027 $$ ). All optimized algorithms except for the exception set algorithm corrected the pulsation artifact and increased the lesion CNR. Changes in Q total $$ {Q}_{\mathrm{total}} $$ were significant for all optimized algorithms except for the percentile algorithm. Liver ADC was significantly reduced (except for the exception set algorithm), particularly in the left lobe.

CONCLUSION

Postprocessing algorithms should be used for flow-compensated liver DWI. The proposed weighted averaging algorithm seems to be suited best to increase the image quality of artifact-corrupted flow-compensated diffusion-weighted liver data.

Deep Learning-Guided Weighted Averaging for Signal Dropout Compensation in DWI of the Liver

PURPOSE

To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion.

METHODS

Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed that aims to suppress the contribution of image regions affected by signal dropouts. Corresponding weight maps were estimated by a sliding-window algorithm, which analyzed signal deviations from a patch-wise reference. In order to ensure the computation of a robust reference, repetitions were filtered by a classifier that was trained to detect images corrupted by signal dropouts. The proposed method, named Deep Learning-guided Adaptive Weighted Averaging (DLAWA), was evaluated in terms of dropout suppression capability, bias reduction in the ADC, and noise characteristics.

RESULTS

In the case of uniform averaging, motion-related dropouts caused signal attenuation and ADC overestimation in parts of the liver, with the left lobe being affected particularly. Both effects could be substantially mitigated by DLAWA while preventing global penalties with respect to SNR due to local signal suppression. Performing evaluations on patient data, the capability to recover lesions concealed by signal dropouts was demonstrated as well. Further, DLAWA allowed for transparent control of the trade-off between SNR and signal dropout suppression by means of a few hyperparameters.

CONCLUSION

This work presents an effective and flexible method for the local compensation of signal dropouts resulting from motion and pulsation. Because DLAWA follows a retrospective approach, no changes to the acquisition are required.

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.

Gradient nonlinearity correction in liver DWI using motion-compensated diffusion encoding waveforms

OBJECTIVE : To experimentally characterize the effectiveness of a gradient nonlinearity correction method in removing ADC bias for different motion-compensated diffusion encoding waveforms.

METHODS

The diffusion encoding waveforms used were the standard monopolar Stejskal-Tanner pulsed gradient spin echo (pgse) waveform, the symmetric bipolar velocity-compensated waveform (sym-vc), the asymmetric bipolar velocity-compensated waveform (asym-vc) and the asymmetric bipolar partial velocity-compensated waveform (asym-pvc). The effectiveness of the gradient nonlinearity correction method using the spherical harmonic expansion of the gradient coil field was tested with the aforementioned waveforms in a phantom and in four healthy subjects.

RESULTS

The gradient nonlinearity correction method reduced the ADC bias in the phantom experiments for all used waveforms. The range of the ADC values over a distance of ± 67.2 mm from isocenter reduced from 1.29 × 10^-4 to 0.32 × 10^-4 mm²/s for pgse, 1.04 × 10^-4 to 0.22 × 10^-4 mm²/s for sym-vc, 1.22 × 10^-4 to 0.24 × 10^-4 mm²/s for asym-vc and 1.07 × 10^-4 to 0.11 × 10^-4 mm²/s for asym-pvc. The in vivo results showed that ADC overestimation due to motion or bright vessels can be increased even further by the gradient nonlinearity correction.

CONCLUSION

The investigated gradient nonlinearity correction method can be used effectively with various motion-compensated diffusion encoding waveforms. In coronal liver DWI, ADC errors caused by motion and residual vessel signal can be increased even further by the gradient nonlinearity correction.

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.

Characterization and correction of cardiovascular motion artifacts in diffusion-weighted imaging of the pancreas

PURPOSE

To assess the effects of cardiovascular-induced motion on conventional DWI of the pancreas and to evaluate motion-robust DWI methods in a motion phantom and healthy volunteers.

METHODS

3T DWI was acquired using standard monopolar and motion-compensated gradient waveforms, including in an anatomically accurate pancreas phantom with controllable compressive motion and healthy volunteers (n = 8, 10). In volunteers, highly controlled single-slice DWI using breath-holding and cardiac gating and whole-pancreas respiratory-triggered DWI were acquired. For each acquisition, the ADC variability across volunteers, as well as ADC differences across parts of the pancreas were evaluated.

RESULTS

In motion phantom scans, conventional DWI led to biased ADC, whereas motion-compensated waveforms produced consistent ADC. In the breath-held, cardiac-triggered study, conventional DWI led to heterogeneous DW signals and highly variable ADC across the pancreas, whereas motion-compensated DWI avoided these artifacts. In the respiratory-triggered study, conventional DWI produced heterogeneous ADC across the pancreas (head: 1756 ± 173 × 10^-6 mm² /s; body: 1530 ± 338 × 10^-6 mm² /s; tail: 1388 ± 267 × 10^-6 mm² /s), with ADCs in the head significantly higher than in the tail (P < .05). Motion-compensated ADC had lower variability across volunteers (head: 1277 ± 102 × 10^-6 mm² /s; body: 1204 ± 169 × 10^-6 mm² /s; tail: 1235 ± 178 × 10^-6 mm² /s), with no significant difference (P ≥ .19) across the pancreas.

CONCLUSION

Cardiovascular motion introduces artifacts and ADC bias in pancreas DWI, which are addressed by motion-robust DWI.

Effective bowel motion reduction in mouse abdominal MRI using hyoscine butylbromide

PURPOSE

Bowel motion is a significant source of artifacts in mouse abdominal MRI. Fasting and administration of hyoscine butylbromide (BUSC) have been proposed for bowel motion reduction but with inconsistent results and limited efficacy assessments. Here, we evaluate these regimes for mouse abdominal MRI at high field.

METHODS

Thirty-two adult C57BL/6J mice were imaged on a 9.4T scanner with a FLASH sequence, acquired over 90 min with ~19 s temporal resolution. During MRI acquisition, 8 mice were injected with a low-dose and 8 mice with a high-dose bolus of BUSC (0.5 and 5 mg/kg, respectively). Eight mice were food deprived for 4.5-6.5 hours before MRI and another group of eight mice was injected with saline during MRI acquisition. Two expert readers reviewed the images and classified bowel motion, and quantitative voxel-wise analyses were performed for identification of moving regions. After defining the most effective protocol, high-resolution T₂ -weighted and diffusion-weighted images were acquired from 4 mice.

RESULTS

High-dose BUSC was the most effective protocol for bowel motion reduction, for up to 45 min. Fasting and saline protocols were not effective in suppressing bowel motion. High-resolution abdominal MRI clearly demonstrated improved image quality and ADC quantification with the high-dose BUSC protocol.

CONCLUSION

Our data show that BUSC administration is advantageous for abdominal MRI in the mouse. Specifically, it endows significant bowel motion reduction, with relatively short onset timings after injection (~8.5 min) and relatively long duration of the effect (~45 min). These features improve the quality of high-resolution images of the mouse abdomen.

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.

Motion-compensated gradient waveforms for tensor-valued diffusion encoding by constrained numerical optimization

PURPOSE

Diffusion-weighted MRI is sensitive to incoherent tissue motion, which may confound the measured signal and subsequent analysis. We propose a "motion-compensated" gradient waveform design for tensor-valued diffusion encoding that negates the effects bulk motion and incoherent motion in the ballistic regime.

METHODS

Motion compensation was achieved by constraining the magnitude of gradient waveform moment vectors. The constraint was incorporated into a numerical optimization framework, along with existing constraints that account for b-tensor shape, hardware restrictions, and concomitant field gradients. We evaluated the efficacy of encoding and motion compensation in simulations, and we demonstrated the approach by linear and planar b-tensor encoding in a healthy heart in vivo.

RESULTS

The optimization framework produced asymmetric motion-compensated waveforms that yielded b-tensors of arbitrary shape with improved efficiency compared with previous designs for tensor-valued encoding, and equivalent efficiency to previous designs for linear (conventional) encoding. Technical feasibility was demonstrated in the heart in vivo, showing vastly improved data quality when using motion compensation. The optimization framework is available online in open source.

CONCLUSION

Our gradient waveform design is both more flexible and efficient than previous methods, facilitating tensor-valued diffusion encoding in tissues in which motion would otherwise confound the signal. The proposed design exploits asymmetric encoding times, a single refocusing pulse or multiple refocusing pulses, and integrates compensation for concomitant gradient effects throughout the imaging volume.

On the dependence of the cardiac motion artifact on the breathing cycle in liver diffusion-weighted imaging

Purpose

The purpose of this study was to investigate whether the cardiac motion artifact that regularly appears in diffusion-weighted imaging of the left liver lobe might be reduced by acquiring images in inspiration, when the coupling between heart and liver might be minimal.

Materials and methods

43 patients with known or suspected focal liver lesions were examined at 1.5 T with breath hold acquisition, once in inspiration and once in expiration. Data were acquired with a diffusion-weighted echo planar imaging sequence and two b-values (b50 = 50 s/mm² and b800 = 800 s/mm²). The severity of the cardiac motion artifact in the left liver lobe was rated by two experienced radiologists for both b-values with a 5 point Likert scale. Additionally, the normalized signal S(b800)/S(b50) in the left liver lobe was computed. The Wilcoxon signed-rank test was used comparing the scores of the two readers obtained in inspiration and expiration, and to compare the normalized signal in inspiration and expiration.

Results

The normalized signal in inspiration was slightly higher than in expiration (0.349±0.077 vs 0.336±0.058), which would indicate a slight reduction of the cardiac motion artifact, but this difference was not significant (p = 0.24). In the qualitative evaluation, the readers did not observe a significant difference for b50 (reader 1: p = 0.61; reader 2: p = 0.18). For b800, reader 1 observed a significant difference of small effect size favouring expiration (p = 0.03 with a difference of mean Likert scores of 0.27), while reader 2 observed no significant difference (p = 0.62).

Conclusion

Acquiring the data in inspiration does not lead to a markedly reduced cardiac motion artifact in diffusion-weighted imaging of the left liver lobe and is in this regard not to be preferred over acquiring the data in expiration.

A mixed waveform protocol for reduction of the cardiac motion artifact in black-blood diffusion-weighted imaging of the liver

OBJECTIVE

Diffusion-weighted imaging (DWI) in the liver suffers from signal loss due to the cardiac motion artifact, especially in the left liver lobe. The purpose of this work was to improve the image quality of liver DWI in terms of cardiac motion artifact reduction and achievement of black-blood images in low b-value images.

MATERIAL AND METHODS

Ten healthy volunteers (age 20-31 years) underwent MRI examinations at 1.5 T with a prototype DWI sequence provided by the vendor. Two diffusion encodings (i.e. waveforms), monopolar and flow-compensated, and the b-values 0, 20, 50, 100, 150, 600 and 800 s/mm² were used. Two Likert scales describing the severity of the pulsation artifact and the quality of the black-blood state were defined and evaluated by two experienced radiologists. Regions of interest (ROIs) were manually drawn in the right and left liver lobe in each slice and combined to a volume of interest (VOI). The mean and coefficient of variation were calculated for each normalized VOI-averaged signal to assess the severity of the cardiac motion artifact. The ADC was calculated using two b-values once for the monopolar data and once with mixed data, using the monopolar data for the small and the flow-compensated data for the high b-value. A Wilcoxon rank sum test was used to compare the Likert scores obtained for monopolar and flow-compensated data.

RESULTS

At b-values from 20 to 150 s/mm², unlike the flow-compensated diffusion encoding, the monopolar encoding yielded black blood in all images with a negligible signal loss due to the cardiac motion artifact. At the b-values 600 and 800 s/mm², the flow-compensated encoding resulted in a significantly reduced cardiac motion artifact, especially in the left liver lobe, and in a black-blood state. The ADC calculated with monopolar data was significantly higher in the left than in the right liver lobe.

CONCLUSION

It is recommendable to use the following mixed waveform protocol: Monopolar diffusion encodings at small b-values and flow-compensated diffusion encodings at high b-values.

Optimization of b-value schemes for estimation of the diffusion coefficient and the perfusion fraction with segmented intravoxel incoherent motion model fitting

PURPOSE

Intravoxel incoherent motion (IVIM) modeling for estimation of the diffusion coefficient (D) and perfusion fraction (f) is increasingly popular, but no consensus on standard protocols exists. This study provides a framework for optimization of b-value schemes for reduced estimation uncertainty of D and f from segmented model fitting.

THEORY

Analytical expressions for uncertainties of D and f from segmented model fitting were derived as Cramer-Rao lower bounds (CRLBs).

METHODS

Optimized b-value schemes were obtained for 3 to 12 acquisitions and in the limit of infinitely many acquisitions through constrained minimization of the CRLBs, with b-values constrained to be 0 or 200 to 800 s/mm² . The optimized b-value scheme with eight acquisitions was compared with b-values linearly distributed in the allowed range using simulations and in vivo liver data from seven healthy volunteers.

RESULTS

All optimized b-value schemes contained exactly three unique b-values regardless of the total number of acquisitions (0, 200, and 800 s/mm² ) with repeated acquisitions distributed approximately as 1:2:2. Compared with linearly distributed b-values, the variability of estimates of D and f was reduced by approximately 30% as seen both in simulations and in repeated in vivo measurements.

CONCLUSION

The uncertainty of IVIM D and f estimates can be reduced by the use of optimized b-value schemes.

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.

Comparison of Navigator Triggering Reduced Field of View and Large Field of View Diffusion-Weighted Imaging of the Pancreas

RATIONALE AND OBJECTIVES

The purpose of this study is to compare image quality, presence and grade of artifacts, signal-to-noise ratio, and apparent diffusion coefficient (ADC) values in pancreatic tissue between high-resolution navigator-triggered (NT) restricted field of view (rFOV) FOCUS single-shot (SS) echo-planar imaging (EPI) diffusion-weighted imaging (DWI) and NT large FOV SS-EPI DWI.

MATERIALS AND METHODS

Magnetic resonance imaging examinations were performed with GE 3-T systems using a 32-channel body array coil. Seventeen consecutive patients were imaged. A 5-point scale semiquantitative grading system was used to evaluate image quality and general artifacts. Signal-to-noise ratio and ADC were measured in the head, body, and tail of the pancreas. Statistical analysis was performed using Student t test and Wilcoxon signed rank test, with differences considered significant for P value less than 0.05.

RESULTS

More artifacts were present on large FOV compared with rFOV FOCUS SS-EPI DW images (P < 0.01). Restricted field of view image quality was subjectively better (P < 0.01). No difference in the signal-to-noise ratio was demonstrated between the 2 image datasets. Apparent diffusion coefficient values were significantly lower (P < 0.01) when calculated from rFOV images than large FOV images.

CONCLUSIONS

Our results demonstrate better image quality and reduced artifacts in rFOV images compared with large FOV DWI. Measurements from ADC maps derived from rFOV DWI show significantly lower ADC values when compared with ADC maps derived from large FOV DWI.

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.

The influence of diffusion gradient direction on diffusion-weighted imaging of breast mass-like lesions at 3.0T

Background It has been challenging to achieve ideal breast diffusion-weighted imaging (DWI). The optimization of diffusion gradient direction is of great importance. Purpose To evaluate the effect of diffusion gradient direction on the apparent diffusion coefficient (ADC) values of breast mass-like lesions and the visual grades of image quality, lesion visibility, and sharpness of breast contour at 3.0T. Material and Methods Sixty consecutive patients with mass-like lesions were enrolled in this study. In addition to typical breast magnetic resonance imaging (MRI) protocols, the breasts were scanned with conventional orthogonal DWI (c-DWI), tetrahedral DWI (t-DWI), and 3in1 DWI (3in1-DWI) sequences. The DW images were observed and visually graded by two radiologists independently. For ADC measurement, one radiographer manually selected the region of interest (ROI). Results For both readers, t-DWI had better image quality and sharpness of breast contour than c-DWI. Regarding lesion visibility, no significant differences were observed among three sequences. The mean ADC values were 1.462 × 10^-3, 1.490 × 10^-3, and 1.446 × 10^-3 mm² s^-1 for c-DWI, t-DWI, and 3in1-DWI, respectively. The ADC values extracted from both t-DWI and 3in1-DWI were not statistically different compared with those from c-DWI. In all DWI sequences, the ADC of malignant lesions was significantly reduced compared with benign lesions. Conclusion DWI with tetrahedral or 3in1 diffusion gradients is a more useful technique in clinical breast MRI than c-DWI because the image quality and sharpness of breast contour are improved. ADC is comparable to c-DWI.

Convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted MRI

PURPOSE

To evaluate convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted imaging (DWI).

METHODS

Diffusion-encoding gradient waveforms were designed for a range of b-values and spatial resolutions with and without motion compensation using the CODE framework. CODE, first moment (M₁ ) nulled CODE-M₁ , and first and second moment (M₂ ) nulled CODE-M₁ M₂ were used to acquire neuro, liver, and cardiac ADC maps in healthy subjects (n=10) that were compared respectively to monopolar (MONO), BIPOLAR (M₁  = 0), and motion-compensated (MOCO, M₁  + M₂  = 0) diffusion encoding.

RESULTS

CODE significantly improved the SNR of neuro ADC maps compared with MONO (19.5 ± 2.5 versus 14.5 ± 1.9). CODE-M₁ liver ADCs were significantly lower (1.3 ± 0.1 versus 1.8 ± 0.3 × 10^-3 mm² /s, ie, less motion corrupted) and more spatially uniform (6% versus 55% ROI difference) than MONO and had higher SNR than BIPOLAR (SNR = 14.9 ± 5.3 versus 8.0 ± 3.1). CODE-M₁ M₂ cardiac ADCs were significantly lower than MONO (1.9 ± 0.6 versus 3.8 ± 0.3 x10^-3 mm² /s) throughout the cardiac cycle and had higher SNR than MOCO at systole (9.1 ± 3.9 versus 7.0 ± 2.6) while reporting similar ADCs (1.5 ± 0.2 versus 1.4 ± 0.6 × 10^-3 mm² /s).

CONCLUSIONS

CODE significantly improved SNR for ADC mapping in the brain, liver and heart, and significantly improved DWI bulk motion robustness in the liver and heart. Magn Reson Med 77:717-729, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver

An area of rapid advancement in abdominal MRI is diffusion-weighted imaging (DWI). By measuring diffusion properties of water molecules, DWI is capable of non-invasively probing tissue properties and physiology at cellular and macromolecular level. The integration of DWI as part of abdominal MRI exam allows better lesion characterization and therefore more accurate initial diagnosis and treatment monitoring. One of the most technical challenging, but also most useful abdominal DWI applications is in liver and therefore requires special attention and careful optimization. In this article, the latest technical developments of DWI and its liver applications are reviewed with the explanations of the technical principles, recommendations of the imaging parameters, and examples of clinical applications. More advanced DWI techniques, including Intra-Voxel Incoherent Motion (IVIM) diffusion imaging, anomalous diffusion imaging, and Diffusion Kurtosis Imaging (DKI) are discussed.

Diffusion-Weighted MR Imaging of Hepatocellular Carcinoma: Current Value in Clinical Evaluation of Tumor Response to Locoregional Treatment

The established size-based image biomarkers for tumor burden measurement continue to be applied to solid tumors, as size measurement can easily be used in clinical practice. However, in the setting of novel targeted therapies and liver-directed locoregional treatments for hepatocellular carcinoma (HCC), simple tumor anatomic changes can be less informative and usually appear later than biologic changes. Functional magnetic resonance (MR) imaging has the potential to be a promising technique for assessment of HCC response to therapy. Diffusion-weighted MR imaging is now widely used as a standard imaging modality to evaluate the liver. This review discusses the current clinical value of diffusion-weighted MR imaging in the evaluation of tumor response after nonsurgical locoregional treatment of HCC.

Applicable apparent diffusion coefficient of an orthotopic mouse model of gastric cancer by improved clinical MRI diffusion weighted imaging

In vivo imaging studies in animal models are hindered by variables that contribute to poor image quality and measurement reliability. As such we sought to improve the diffusion coefficient (ADC) of an orthotopic mouse model of gastric cancer in diffusion-weighted images (DWI) using alginate moulding and Ultrasonic coupling medium. BGC-823 human gastric cancer cells were subcutaneously injected into the abdomen of nude mice and 1 mm(3) primary tumour was orthotopically transplanted. Alginate and coupling medium were applied to the mice and MRI (T2 and DWI) was performed for 6 weeks. Regions of interest (ROI) were drawn and liver and tumour ADC were evaluated. Using alginate moulding, the mean quality total score of DW imaging was 8.53; however, in control animals this value was 5.20 (p < 0.001). The coefficient of variation of ADC of liver in experimental and control groups were 0.071 and 0.270 (p < 0.001), respectively, suggesting this method may be helpful for DWI studies of important human diseases such as gastric cancer.

Diffusion-weighted imaging of the liver: techniques and applications

Diffusion-weighted imaging (DWI) is a technique that assesses the cellularity, tortuosity of the extracellular/extravascular space, and cell membrane density based on differences in water proton mobility in tissues. The strength of the diffusion weighting is reflected by the b value. DWI using several b values enables the quantification of the apparent diffusion coefficient. DWI is increasingly used in liver imaging for multiple reasons: it can add useful qualitative and quantitative information to conventional imaging sequences; it is acquired relatively quickly; it is easily incorporated into existing clinical protocols; and it is a noncontrast technique.

Characterization and correction of eddy-current artifacts in unipolar and bipolar diffusion sequences using magnetic field monitoring

Diffusion tensor imaging (DTI) of moving organs is gaining increasing attention but robust performance requires sequence modifications and dedicated correction methods to account for system imperfections. In this study, eddy currents in the "unipolar" Stejskal-Tanner and the velocity-compensated "bipolar" spin-echo diffusion sequences were investigated and corrected for using a magnetic field monitoring approach in combination with higher-order image reconstruction. From the field-camera measurements, increased levels of second-order eddy currents were quantified in the unipolar sequence relative to the bipolar diffusion sequence while zeroth and linear orders were found to be similar between both sequences. Second-order image reconstruction based on field-monitoring data resulted in reduced spatial misalignment artifacts and residual displacements of less than 0.43 mm and 0.29 mm (in the unipolar and bipolar sequences, respectively) after second-order eddy-current correction. Results demonstrate the need for second-order correction in unipolar encoding schemes but also show that bipolar sequences benefit from second-order reconstruction to correct for incomplete intrinsic cancellation of eddy-currents.