Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRI.

In recent years Motion-Compensated MR reconstruction (MCMR) has emerged as a promising approach for cardiac MR (CMR) imaging reconstruction. MCMR estimates cardiac motion and incorporates this information in the reconstruction. However, two obstacles prevent the practical use of MCMR in clinical situations: First, inaccurate motion estimation often leads to inferior CMR reconstruction results. Second, the motion estimation frequently leads to a long processing time for the reconstruction. In this work, we propose a learning-based and unrolled MCMR framework that can perform precise and rapid CMR reconstruction. We achieve accurate reconstruction by developing a joint optimization between the motion estimation and reconstruction, in which a deep learning-based motion estimation framework is unrolled within an iterative optimization procedure. With progressive iterations, a mutually beneficial interaction can be established in which the reconstruction quality is improved with more accurate motion estimation. Further, we propose a groupwise motion estimation framework to speed up the MCMR process. A registration template based on the cardiac sequence average is introduced, while the motion estimation is conducted between the cardiac frames and the template. By applying this framework, cardiac sequence registration can be accomplished with linear time complexity. Experiments on 43 in-house acquired 2D CINE datasets indicate that the proposed unrolled MCMR framework can deliver artifacts-free motion estimation and high-quality CMR reconstruction even for imaging acceleration rates up to 20x. We compare our approach with state-of-the-art reconstruction methods and it outperforms them quantitatively and qualitatively in all adapted metrics across all acceleration rates.

Single-heartbeat cardiac cine imaging via jointly regularized nonrigid motion-corrected reconstruction.

The aim of the current study was to develop a novel approach for 2D breath-hold cardiac cine imaging from a single heartbeat, by combining cardiac motion-corrected reconstructions and nonrigidly aligned patch-based regularization. Conventional cardiac cine imaging is obtained via motion-resolved reconstructions of data acquired over multiple heartbeats. Here, we achieve single-heartbeat cine imaging by incorporating nonrigid cardiac motion correction into the reconstruction of each cardiac phase, in conjunction with a motion-aligned patch-based regularization. The proposed Motion-Corrected CINE (MC-CINE) incorporates all acquired data into the reconstruction of each (motion-corrected) cardiac phase, resulting in a better posed problem than motion-resolved approaches. MC-CINE was compared with iterative sensitivity encoding (itSENSE) and Extra-Dimensional Golden Angle Radial Sparse Parallel (XD-GRASP) in 14 healthy subjects in terms of image sharpness, reader scoring (range: 1-5) and reader ranking (range: 1-9) of image quality, and single-slice left ventricular assessment. MC-CINE was significantly superior to both itSENSE and XD-GRASP using 20 heartbeats, two heartbeats, and one heartbeat. Iterative SENSE, XD-GRASP, and MC-CINE achieved a sharpness of 74%, 74%, and 82% using 20 heartbeats, and 53%, 66%, and 82% with one heartbeat, respectively. The corresponding results for reader scoring were 4.0, 4.7, and 4.9 with 20 heartbeats, and 1.1, 3.0, and 3.9 with one heartbeat. The corresponding results for reader ranking were 5.3, 7.3, and 8.6 with 20 heartbeats, and 1.0, 3.2, and 5.4 with one heartbeat. MC-CINE using a single heartbeat presented nonsignificant differences in image quality to itSENSE with 20 heartbeats. MC-CINE and XD-GRASP at one heartbeat both presented a nonsignificant negative bias of less than 2% in ejection fraction relative to the reference itSENSE. It was concluded that the proposed MC-CINE significantly improves image quality relative to itSENSE and XD-GRASP, enabling 2D cine from a single heartbeat.

Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort.

BACKGROUND AND OBJECTIVE: Tracking of anatomical structures in time-resolved medical image data plays an important role for various tasks such as volume change estimation or treatment planning. State-of-the-art deep learning techniques for automated tracking, while providing accurate results, require large amounts of human-labeled training data making their wide-spread use time- and resource-intensive. Our contribution in this work is the implementation and adaption of a self-supervised learning (SSL) framework that addresses this bottleneck of training data generation. METHODS: To this end we adapted and implemented an SSL framework that allows for automated anatomical tracking without the necessity for human-labeled training data. We evaluated this method by comparison to conventional- and deep learning optical flow (OF)-based tracking methods. We applied all methods on three different time-resolved medical image datasets (abdominal MRI, cardiac MRI, and echocardiography) and assessed their accuracy regarding tracking of pre-defined anatomical structures within and across individuals. RESULTS: We found that SSL-based tracking as well as OF-based methods provide accurate results for simple, rigid and smooth motion patterns. However, regarding more complex motion, e.g. non-rigid or discontinuous motion patterns in the cardiac region, and for cross-subject anatomical matching, SSL-based tracking showed markedly superior performance. CONCLUSION: We conclude that automated tracking of anatomical structures on time-resolved medical image data with minimal human labeling effort is feasible using SSL and can provide superior results compared to conventional and deep learning OF-based methods.

Whole-heart non-rigid motion corrected coronary MRA with autofocus virtual 3D iNAV.

PURPOSE: Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction. METHODS: Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC. RESULTS: Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 ± 1% vs 52 ± 11%, LAD: 49 ± 8% vs 49 ± 7%; visible vessel length: RCA: 5.98 ± 1.37 cm vs 5.81 ± 1.62 cm, LAD: 5.95 ± 1.85 cm vs 4.83 ± 1.56 cm), however these improvements were not statistically significant. CONCLUSION: The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent.

Generalized low-rank nonrigid motion-corrected reconstruction for MR fingerprinting.

PURPOSE: Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF). METHODS: Generalized motion-corrected (MC) reconstructions have been developed for steady-state imaging. Here we extend this framework to enable nonrigid MC for transient imaging applications with varying contrast, such as MRF. This is achieved by integrating low-rank dictionary-based compression into the generalized MC model to reconstruct MC singular images, reducing motion artifacts in the resulting parametric maps. The proposed LRMC reconstruction was applied for cardiac motion correction in 2D myocardial MRF (T1 and T2 ) with extended cardiac acquisition window (~450 ms) and for respiratory MC in free-breathing 3D myocardial and 3D liver MRF. Experiments were performed in phantom and 22 healthy subjects. The proposed approach was compared with reference spin echo (phantom) and with 2D electrocardiogram-triggered/breath-hold MOLLI and T2 gradient-and-spin echo conventional maps (in vivo 2D and 3D myocardial MRF). RESULTS: Phantom results were in general agreement with reference spin-echo measurements, presenting relative errors of approximately 5.4% and 5.5% for T1 and short T2 (<100 ms), respectively. The proposed LRMC MRF reduced residual blurring artifacts with respect to no MC for cardiac or respiratory motion in all cases (2D and 3D myocardial, 3D abdominal). In 2D myocardial MRF, left-ventricle T1 values were 1150 ± 41 ms for LRMC MRF and 1010 ± 56 ms for MOLLI; T2 values were 43.8 ± 2.3 ms for LRMC MRF and 49.5 ± 4.5 ms for T2 gradient and spin echo. Corresponding measurements for 3D myocardial MRF were 1085 ± 30 ms and 1062 ± 29 ms for T1 , and 43.5 ± 1.9 ms and 51.7 ± 1.7 ms for T2 . For 3D liver, LRMC MRF measured liver T1 at 565 ± 44 ms and liver T2 at 35.4 ± 2.4 ms. CONCLUSION: The proposed LRMC reconstruction enabled generalized (nonrigid) MC for 2D and 3D MRF, both for cardiac and respiratory motion. The proposed approach reduced motion artifacts in the MRF maps with respect to no motion compensation and achieved good agreement with reference measurements.

Feasibility and Implementation of a Deep Learning MR Reconstruction for TSE Sequences in Musculoskeletal Imaging.

Magnetic Resonance Imaging (MRI) of the musculoskeletal system is one of the most common examinations in clinical routine. The application of Deep Learning (DL) reconstruction for MRI is increasingly gaining attention due to its potential to improve the image quality and reduce the acquisition time simultaneously. However, the technology has not yet been implemented in clinical routine for turbo spin echo (TSE) sequences in musculoskeletal imaging. The aim of this study was therefore to assess the technical feasibility and evaluate the image quality. Sixty examinations of knee, hip, ankle, shoulder, hand, and lumbar spine in healthy volunteers at 3 T were included in this prospective, internal-review-board-approved study. Conventional (TSES) and DL-based TSE sequences (TSEDL) were compared regarding image quality, anatomical structures, and diagnostic confidence. Overall image quality was rated to be excellent, with a significant improvement in edge sharpness and reduced noise compared to TSES (p < 0.001). No difference was found concerning the extent of artifacts, the delineation of anatomical structures, and the diagnostic confidence comparing TSES and TSEDL (p > 0.05). Therefore, DL image reconstruction for TSE sequences in MSK imaging is feasible, enabling a remarkable time saving (up to 75%), whilst maintaining excellent image quality and diagnostic confidence.

End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA.

PURPOSE: To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing whole-heart coronary MRA (CMRA). METHODS: A novel deep learning framework was developed consisting of a diffeomorphic registration network and a motion-informed model-based deep learning (MoDL) reconstruction network. The registration network receives as input highly undersampled (~22×) respiratory-resolved images and outputs 3D nonrigid respiratory motion fields between the images. The motion-informed MoDL performs MoCo reconstruction from undersampled data using the predicted motion fields. The whole deep learning framework, termed as MoCo-MoDL, was trained end-to-end in a supervised manner for simultaneous 3D nonrigid motion estimation and MoCo reconstruction. MoCo-MoDL was compared with a state-of-the-art nonrigid MoCo CMRA reconstruction technique in 15 retrospectively undersampled datasets and 9 prospectively undersampled acquisitions. RESULTS: The acquisition time for ninefold accelerated CMRA was ~2.5 min. The reconstruction time was ~22 s for the proposed MoCo-MoDL and ~35 min for the conventional approach. MoCo-MoDL achieved higher peak SNR (27.86 ± 3.00 vs. 26.71 ± 2.79; P < .05) and structural similarity (0.78 ± 0.06 vs. 0.75 ± 0.06; P < .05) than the conventional approach. Similar vessel length and visual image quality score were obtained with the 2 methods, whereas improved vessel sharpness was observed with MoCo-MoDL. CONCLUSION: An end-to-end deep learning approach was introduced for simultaneous nonrigid motion estimation and MoCo reconstruction of highly undersampled free-breathing whole-heart CMRA. The rapid free-breathing CMRA acquisition together with the fast reconstruction of the proposed approach promises easy integration into clinical workflow.

Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning.

Non-rigid motion-corrected reconstruction has been proposed to account for the complex motion of the heart in free-breathing 3D coronary magnetic resonance angiography (CMRA). This reconstruction framework requires efficient and accurate estimation of non-rigid motion fields from undersampled images at different respiratory positions (or bins). However, state-of-the-art registration methods can be time-consuming. This article presents a novel unsupervised deep learning-based strategy for fast estimation of inter-bin 3D non-rigid respiratory motion fields for motion-corrected free-breathing CMRA. The proposed 3D respiratory motion estimation network (RespME-net) is trained as a deep encoder-decoder network, taking pairs of 3D image patches extracted from CMRA volumes as input and outputting the motion field between image patches. Using image warping by the estimated motion field, a loss function that imposes image similarity and motion smoothness is adopted to enable training without ground truth motion field. RespME-net is trained patch-wise to circumvent the challenges of training a 3D network volume-wise which requires large amounts of GPU memory and 3D datasets. We perform 5-fold cross-validation with 45 CMRA datasets and demonstrate that RespME-net can predict 3D non-rigid motion fields with subpixel accuracy (0.44 ± 0.38 mm) within ~10 seconds, being ~20 times faster than a GPU-implemented state-of-the-art non-rigid registration method. Moreover, we perform non-rigid motion-compensated CMRA reconstruction for 9 additional patients. The proposed RespME-net has achieved similar motion-corrected CMRA image quality to the conventional registration method regarding coronary artery length and sharpness.

Fully Automated Segmentation and Shape Analysis of the Thoracic Aorta in Non-contrast-enhanced Magnetic Resonance Images of the German National Cohort Study.

PURPOSE: The purpose of this study was to develop and validate a deep learning-based framework for automated segmentation and vessel shape analysis on non-contrast-enhanced magnetic resonance (MR) data of the thoracic aorta within the German National Cohort (GNC) MR study. MATERIALS AND METHODS: One hundred data sets acquired in the GNC MR study were included (56 men, average age 53 y [22 to 72 y]). All participants had undergone non-contrast-enhanced MR imaging of the thoracic vessels. Automated vessel segmentation of the thoracic aorta was performed using a Convolutional Neural Network in a supervised setting with manually annotated data sets as the ground truth. Seventy data sets were used for training; 30 data sets were used for quantitative and qualitative evaluation. Automated shape analysis based on centerline extraction from segmentation masks was performed to derive a diameter profile of the vessel. For comparison, 2 radiologists measured vessel diameters manually. RESULTS: Overall, automated aortic segmentation was successful, providing good qualitative analyses with only minor irregularities in 29 of 30 data sets. One data set with severe MR artifacts led to inadequate automated segmentation results. The mean Dice score of automated vessel segmentation was 0.85. Automated aortic diameter measurements were similar to manual measurements (average difference -0.9 mm, limits of agreement: -5.4 to 3.9 mm), with minor deviations in the order of the interreader agreement between the 2 radiologists (average difference -0.5 mm, limits of agreement: -5.8 to 4.8 mm). CONCLUSION: Automated segmentation and shape analysis of the thoracic aorta is feasible with high accuracy on non-contrast-enhanced MR imaging using the proposed deep learning approach.

Respiratory motion-compensated high-resolution 3D whole-heart T1ρ mapping.

BACKGROUND: Cardiovascular magnetic resonance (CMR) T1ρ mapping can be used to detect ischemic or non-ischemic cardiomyopathy without the need of exogenous contrast agents. Current 2D myocardial T1ρ mapping requires multiple breath-holds and provides limited coverage. Respiratory gating by diaphragmatic navigation has recently been exploited to enable free-breathing 3D T1ρ mapping, which, however, has low acquisition efficiency and may result in unpredictable and long scan times. This study aims to develop a fast respiratory motion-compensated 3D whole-heart myocardial T1ρ mapping technique with high spatial resolution and predictable scan time. METHODS: The proposed electrocardiogram (ECG)-triggered T1ρ mapping sequence is performed under free-breathing using an undersampled variable-density 3D Cartesian sampling with spiral-like order. Preparation pulses with different T1ρ spin-lock times are employed to acquire multiple T1ρ-weighted images. A saturation prepulse is played at the start of each heartbeat to reset the magnetization before T1ρ preparation. Image navigators are employed to enable beat-to-beat 2D translational respiratory motion correction of the heart for each T1ρ-weighted dataset, after which, 3D translational registration is performed to align all T1ρ-weighted volumes. Undersampled reconstruction is performed using a multi-contrast 3D patch-based low-rank algorithm. The accuracy of the proposed technique was tested in phantoms and in vivo in 11 healthy subjects in comparison with 2D T1ρ mapping. The feasibility of the proposed technique was further investigated in 3 patients with suspected cardiovascular disease. Breath-hold late-gadolinium enhanced (LGE) images were acquired in patients as reference for scar detection. RESULTS: Phantoms results revealed that the proposed technique provided accurate T1ρ values over a wide range of simulated heart rates in comparison to a 2D T1ρ mapping reference. Homogeneous 3D T1ρ maps were obtained for healthy subjects, with septal T1ρ of 58.0 ± 4.1 ms which was comparable to 2D breath-hold measurements (57.6 ± 4.7 ms, P = 0.83). Myocardial scar was detected in 1 of the 3 patients, and increased T1ρ values (87.4 ± 5.7 ms) were observed in the infarcted region. CONCLUSIONS: An accelerated free-breathing 3D whole-heart T1ρ mapping technique was developed with high respiratory scan efficiency and near-isotropic spatial resolution (1.7 × 1.7 × 2 mm3) in a clinically feasible scan time of ~ 6 mins. Preliminary patient results suggest that the proposed technique may find applications in non-contrast myocardial tissue characterization.

Spatial-temporal perfusion patterns of the human liver assessed by pseudo-continuous arterial spin labeling MRI.

PURPOSE: To investigate the capabilities of a modern pseudo-continuous arterial spin labeling (PCASL) technique for non-invasive assessment of the temporal and spatial distribution of the liver perfusion in healthy volunteers on a clinical MR system at 3T. MATERIALS AND METHODS: A 2D-PCASL multi-slice echo planar imaging sequence was adapted to the specific conditions in liver: a) labeling by PCASL was optimized to the flow characteristics in the portal vein, b) background suppression was applied for reduction of motion related artifacts, c) post labeling delays (PLDs) were varied over a large range (0.7-3.5s) in order to get better insight in the temporal and spatial distribution of tagged blood in the liver, and d) a special timed-breathing protocol was used allowing for recording of 16 to 18 label-control image pairs and a reference M0 image for each of 4 to 6 slices within approx. 5min for one PLD. RESULTS: Measurements with multiple PLDs showed dominating perfusion signal in macroscopic blood vessels for PLDs up to 1.5 s, whereas pure liver parenchyma revealed maximum perfusion signal for a PLD of approx. 2 s, and detectable signal up to PLDs of 3.5 s. Data fitting to a perfusion model for liver provided a mean global perfusion of 153±15ml/100g/min and a mean transit time of 1938±332ms in liver parenchyma. Measurements with a single PLD of 2 s demonstrated that portal-venous and arterial perfusion components can be measured separately by two measurements with two different positions of the labeling plane (one for labeling of the global hepatopetal blood flow and one for selective labeling of the portal blood flow only). Relative contribution of blood from the hepatic artery to the global liver perfusion, the hepatic perfusion index (HPI), amounted to approx. 23%. CONCLUSION: Modern and adapted protocols for assessment of liver perfusion by PCASL have the potential to provide perfusion and blood transit time maps in reasonable acquisition time.

Acceleration of Magnetic Resonance Cholangiopancreatography Using Compressed Sensing at 1.5 and 3 T: A Clinical Feasibility Study.

OBJECTIVES: Magnetic resonance cholangiopancreatography (MRCP) is an established technique in routine magnetic resonance examination. By applying the compressed sensing (CS) acceleration technique to conventional MRCP sequences, scan time can be markedly reduced. With promising results at 3 T, there is a necessity to evaluate the performance at 1.5 T due to wide scanner availabilities. Aim of this study is to test the feasibility of accelerated 3-dimensional (3D) MRCP with extended sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) using CS in navigator triggering and in a single breath-hold in a clinical setting at 1.5 T and 3 T and compare it with a conventional navigator-triggered 3D SPACE-MRCP. MATERIALS AND METHODS: Phantom measurements were performed to adapt sequence parameters. Conventional 3D SPACE-MRCP in navigator triggering (STD_MRCP) as well as CS-accelerated 3D SPACE-MRCP acquired in navigator triggering and in a single breath-hold (CS_MRCP and CS_BH_MRCP) was performed in 66 patients undergoing clinically induced MRI of the pancreatobiliary system at 1.5 T and 3 T. Image quality evaluation was performed by 2 independent radiologists. Dedicated statistics were performed (P < 0.05 considered significant). RESULTS: In patient imaging, CS_MRCP was superior to STD_MRCP and CS_BH_MRCP in aspects of overall image quality at 1.5 T (P = 0.01; P < 0.001) and 3 T (P = 0.002; P = 0.013). Overall image quality in CS_BH_MRCP was inferior compared with STD_MRCP and CS_MRCP at 1.5 T. At 3 T, overall image quality in CS_BH_MRCP was superior to STD_MRCP (P = 0.001). Scan time was reduced by 25% to 46% covering 5% of k-space (CS_MRCP at 1.5 and 3 T) and 97% covering 3.6% of k-space (CS_BH_MRCP at 1.5 and 3 T). CONCLUSIONS: Compressed sensing-accelerated MRCP is feasible in clinical routine at 1.5 and 3 T offering major reduction of acquisition time. When applying a single breath-hold CS imaging, field strengths of 3 T are recommended.

Scan time reduction in diffusion-weighted imaging of the pancreas using a simultaneous multislice technique with different acceleration factors: How fast can we go?

OBJECTIVES: To investigate the feasibility of simultaneous multislice-accelerated diffusion-weighted imaging (sms-DWI) of the pancreas with different acceleration factors and its influence on image quality, acquisition time and apparent diffusion coefficients (ADCs) in comparison to conventional sequences. METHODS: DWI of the pancreas was performed at 1.5T in ten healthy volunteers and 20 patients with sms-accelerated echo-planar DWI using two different sms-acceleration factors of 2 and 3 (sms2/3-DWI). These DWI sequences were compared to conventional DWI (c-DWI) in terms of image quality parameters (5-point Likert scale) and ADC measurements. RESULTS: c-DWI and sms2-DWI offered equivalently high overall image quality (4 [1; 5]) with scan time reduction to one-third (c-DWI: 173 s, sms2-DWI: 56 s). Sms3-DWI showed significantly poorer overall image quality (3 [1; 5]; p < 0.0001). ADC values were significantly lower in sms3-DWI compared to c-DWI in the pancreatic body and tail (body: c-DWI 1.4 x 10-3 mm2/s, sms3-DWI 1.0 x 10-3 mm2/s, p = 0.028; tail: c-DWI 1.3 x 10-3 mm2/s and sms3-DWI 1.0 x 10-3 mm2/s, p = 0.014). CONCLUSIONS: Accelerated multislice DWI of the pancreas offers high image quality with a substantial reduction of acquisition time. Lower ADC values in multislice DWI should be considered in diagnostic reading. KEY POINTS: • Simultaneous multislice-accelerated diffusion-weighted imaging (sms-DWI) promises scan time minimisation. • Sms-DWI of the pancreas offers diagnostic image quality in volunteers and patients. • Sms-DWI with an acceleration factor of 2 offers high image quality. • Higher acceleration factors in sms-DWI do not provide sufficient diagnostic image quality. • ADC values may be lower in sms-DWI.

Feasibility of accelerated simultaneous multislice diffusion-weighted MRI of the prostate.

PURPOSE: To assess the feasibility of simultaneous multislice (SMS) single-shot echo-planar-imaging (EPI) for accelerated diffusion-weighted imaging (DWI) of the prostate. MATERIALS AND METHODS: For phantom measurements a dedicated DWI phantom with different sucrose concentrations was used. In addition, 10 volunteers and 16 patients with suspected prostate cancer were examined for in vivo measurements. All examinations were performed with a 3T magnetic resonance imaging (MRI) system. A prototype simultaneous multislice EPI sequence (DW-EPISMS ; acquisition time 3:14 min) was acquired and compared to a single-shot EPI sequence (DW-EPISS ; acquisition time 6:12 min) serving as a standard of reference. Different image quality parameters of EPISMS were assessed qualitatively (overall image quality, anatomic differentiability, lesion conspicuity, image noise, distortion; two independent readers; 5-point Likert-scale [5 = excellent]) and quantitatively (ADC-values by calculating interclass correlation [ICC] and Bland-Altman limits of agreement [LoA] as measures for reproducibility) and compared to DW-EPISS . RESULTS: DW-EPISMS allowed for a substantially reduced acquisition time as compared to DW-EPISS (˜50%). Bland-Altman plots revealed robust measurement repeatability for DW-EPISMS in the phantom study. Overall image quality did not significantly differ between DW-EPISMS and DW-EPISS (b1500 images P = 0.5; ADC maps P = 0.7). Only in b1500 DW images was subjective image noise rated significantly higher in DW-EPISS than in DW-EPISMS (P = 0.006). Quantitative analysis of ADC-values revealed not significant differences between DW-EPISMS and DW-EPISS (P = 0.7) and high measures for reproducibility ICC ≥0.96. CONCLUSION: Simultaneous multislice DWI is feasible for accelerated prostate MRI allowing for a substantially reduced examination time with similar image quality and ADC-values as compared to a standard of reference DWI sequence. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1507-1515.

Scan time minimization in hepatic diffusion-weighted imaging: evaluation of the simultaneous multislice acceleration technique with different acceleration factors and gradient preparation schemes.

OBJECTIVE: To evaluate simultaneous multislice (sms) accelerated diffusion-weighted imaging (DWI) of the liver in comparison to conventional sequences. MATERIALS AND METHODS: Ten volunteers underwent DWI of the liver at 1.5 T. Four different sms-accelerated sequences with monopolar and bipolar gradient preparation (MP, BP) and acceleration factors 2 and 3 (sms2-DWI, sms3-DWI) were compared to conventional DWI (c-DWI). Image quality criteria rated on a 5-point Likert scale (5 = excellent), image quality sum scores (maximum 120), and ADC were compared using Friedman test and Dunn-Bonferroni post hoc test. Bland-Altman plots were calculated for ADC comparison. p values <0.05 were considered significant. RESULTS: Sms2-DWI offered scan time minimization of 67 % without significant difference in image quality (sum score: sms2-DWI MP/BP: 97 ± 8/92 ± 9; c-DWI MP/BP: 99 ± 8/97 ± 8). Sms3-DWI offered slight additional scan time minimization with significantly inferior image quality (sum score: sms3-DWI MP/BP: 75 ± 14/69 ± 14; p < 0.001). MP preparation provided slightly higher image quality in sms-DWI without statistical significance. ADC in sms-DWI were significantly lower (sms2-DWI MP 1.01 × 10(-3) mm(2)/s; c-DWI MP 1.20 × 10(-3) mm(2)/s; p < 0.001). CONCLUSION: Sms2-DWI provides considerable scan time minimization without significant shortcomings in image quality. Sms3-DWI provides significantly inferior image quality without further scan time minimization. Potentially lower ADC in sms-DWI should be considered in clinical routine.

Simultaneous multislice diffusion-weighted MRI of the liver: Analysis of different breathing schemes in comparison to standard sequences.

PURPOSE: To systematically evaluate image characteristics of simultaneous-multislice (SMS)-accelerated diffusion-weighted imaging (DWI) of the liver using different breathing schemes in comparison to standard sequences. MATERIALS AND METHODS: DWI of the liver was performed in 10 healthy volunteers and 12 patients at 1.5T using an SMS-accelerated echo planar imaging sequence performed with respiratory-triggering and free breathing (SMS-RT, SMS-FB). Standard DWI sequences served as reference (STD-RT, STD-FB). Reduction of scan time by SMS-acceleration was measured. Image characteristics of SMS-DWI and STD-DWI with both breathing schemes were analyzed quantitatively (apparent diffusion coefficient [ADC], signal-to-noise ratio [SNR]) and qualitatively (5-point Likert scale, 5 = excellent). Qualitative and quantitative parameters were compared using Friedman test and Dunn-Bonferroni post-hoc method with P-values < 0.05 considered statistically significant. RESULTS: SMS-DWI provided diagnostic image quality in volunteers and patients both with RT and FB with a reduction of scan time of 70% (0:56 vs. 3:20 min in FB). Overall image quality did not significantly differ between FB and RT acquisition in both STD and SMS sequences (median STD-RT 5.0, STD-FB 4.5, SMS-RT: 4.75; SMS-FB: 4.5; P = 0.294). SNR in the right hepatic lobe was comparable between the four tested sequences. ADC values were significantly lower in SMS-DWI compared to STD-DWI irrespective of the breathing scheme (1.2 ± 0.2 × 10(-3) mm(2) /s vs. 1.0 ± 0.2 × 10(-3) mm(2) /s; P < 0.001). CONCLUSION: SMS-acceleration provides considerable scan time reduction for hepatic DWI with equivalent image quality compared to the STD technique both using RT and FB. Discrepancies in ADC between STD-DWI and SMS-DWI need to be considered when transferring the SMS technique to clinical routine reading. J. MAGN. RESON. IMAGING 2016;44:865-879.