Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study

PURPOSE

To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC).

METHODS

This single-center prospective study enrolled consecutive at-risk participants who underwent 3.0 T gadoxetate disodium-enhanced MRI. Conventional DWI was acquired using parallel imaging (PI) with SENSE (PI-DWI). In CS-DWI and DL-CS-DWI, CS but not PI with SENSE was used to accelerate the scan with 2.5 as the acceleration factor. Qualitative and quantitative image quality were independently assessed by two masked reviewers, and were compared using the Wilcoxon signed-rank test. The detection rates of clinically-relevant (LR-4/5/M based on the Liver Imaging Reporting and Data System v2018) liver lesions for each DWI sequence were independently evaluated by another two masked reviewers against their consensus assessments based on all available non-DWI sequences, and were compared by the McNemar test.

RESULTS

67 participants (median age, 58.0 years; 56 males) with 197 clinically-relevant liver lesions were enrolled. Among the three DWI sequences, DL-CS-DWI showed the best qualitative and quantitative image qualities (p range, <0.001-0.039). For clinically-relevant liver lesions, the detection rates (91.4%-93.4%) of DL-CS-DWI showed no difference with CS-DWI (87.3%-89.8%, p = 0.230-0.231) but were superior to PI-DWI (82.7%-85.8%, p = 0.015-0.025). For lesions located in the hepatic dome, DL-CS-DWI demonstrated the highest detection rates (94.8%-97.4% vs 76.9%-79.5% vs 64.1%-69.2%, p = 0.002-0.045) among the three DWI sequences.

CONCLUSION

In patients at high-risk for HCC, DL-CS-DWI improved image quality and detection for clinically-relevant liver lesions, especially for the hepatic dome.

Advanced MRI techniques in abdominal imaging

Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients' breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques.

Clinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient Settings

BACKGROUND AND PURPOSE

The use of MR imaging in emergency settings has been limited by availability, long scan times, and sensitivity to motion. This study assessed the diagnostic performance of an ultrafast brain MR imaging protocol for evaluation of acute intracranial pathology in the emergency department and inpatient settings.

MATERIALS AND METHODS

Sixty-six adult patients who underwent brain MR imaging in the emergency department and inpatient settings were included in the study. All patients underwent both the reference and the ultrafast brain MR protocols. Both brain MR imaging protocols consisted of T1-weighted, T2/T2*-weighted, FLAIR, and DWI sequences. The ultrafast MR images were reconstructed by using a machine-learning assisted framework. All images were reviewed by 2 blinded neuroradiologists.

RESULTS

The average acquisition time was 2.1 minutes for the ultrafast brain MR protocol and 10 minutes for the reference brain MR protocol. There was 98.5% agreement on the main clinical diagnosis between the 2 protocols. In head-to-head comparison, the reference protocol was preferred in terms of image noise and geometric distortion (P < .05 for both). The ultrafast ms-EPI protocol was preferred over the reference protocol in terms of reduced motion artifacts (P < .01). Overall diagnostic quality was not significantly different between the 2 protocols (P > .05).

CONCLUSIONS

The ultrafast brain MR imaging protocol provides high accuracy for evaluating acute pathology while only requiring a fraction of the scan time. Although there was greater image noise and geometric distortion on the ultrafast brain MR protocol images, there was significant reduction in motion artifacts with similar overall diagnostic quality between the 2 protocols.

Advanced Abdominal MRI Techniques and Problem-Solving Strategies

MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI. We aimed to present various advanced abdominal MRI techniques for reducing the scan time while maintaining image quality and free-breathing techniques for dynamic imaging and illustrate cases using the techniques mentioned above. A review of these advanced techniques can assist in the appropriate interpretation of sequences.

Utility of Echo Planar Imaging With Compressed Sensing-Sensitivity Encoding (EPICS) for the Evaluation of the Head and Neck Region

Purpose This study aimed to compare the image quality between echo planar imaging (EPI) with compressed sensing-sensitivity encoding (EPICS)-based diffusion-weighted imaging (DWI) and conventional parallel imaging (PI)-based DWI of the head and neck. Materials and methods Ten healthy volunteers participated in this study. EPICS-DWI was acquired based on an axial spin-echo EPI sequence with EPICS acceleration factors of 2, 3, and 4, respectively. Conventional PI-DWI was acquired using the same acceleration factors (i.e., 2, 3, and 4). Quantitative assessment was performed by measuring the signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) in a circular region of interest (ROI) on the parotid and submandibular glands. For qualitative evaluation, a three-point visual grading system was used to assess the (1) overall image quality and (2) degree of image distortion. Results In the quantitative assessment, the SNR of the parotid gland in EPICS-DWI was significantly higher than that of PI-DWI in acceleration factors of 3 and 4 (p<0.05). In a comparison of ADC values, significant differences were not observed between EPICS-DWI and PI-DWI. In the qualitative assessment, the overall image quality of EPICS-DWI was significantly higher than that of PI-DWI for acceleration factors 3 and 4 (p<0.05). The degree of image distortion was significantly larger in EPICS-DWI with an acceleration factor of 2 than that of 3 or 4 (p<0.01, respectively). Conclusion Under the appropriate parameter setting, EPICS-DWI demonstrated higher SNR and better overall image quality for head and neck imaging than PI-DWI, without increasing image distortion.

Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck

OBJECTIVES

To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.

RESULTS

Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001).

DISCUSSION

DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.

Diagnostic ability of diffusion-weighted imaging using echo planar imaging with compressed SENSE (EPICS) for differentiating hepatic hemangioma and liver metastasis

PURPOSE

To assess the diagnostic abilities of diffusion-weighted imaging (DWI) with parallel imaging (PI-DWI) and that with Compressed SENSE (EPICS-DWI) for differentiating hepatic hemangiomas (HHs) and liver metastases (LMs).

METHOD

This prospective study included 30 participants with HH and/or LM who underwent PI-DWI and EPICS-DWI. Two radiologists assessed the DWI images and assigned confidence scores for hepatic lesions conspicuity using 4-point scale. One of the radiologists additionally calculated the contrast-to-noise ratio (CNR) and measured ADC value of the hepatic lesions. The conspicuity, CNR, and ADC values were compared between the two sequences. A receiver operating characteristic (ROC) analysis was performed to assess the diagnostic abilities of the two sequences for differentiating HHs and LMs.

RESULTS

The conspicuity of LMs was better in EPICS-DWI than in PI-DWI (P < .05 in both radiologists). The CNR of LMs was higher in EPICS-DWI than in PI-DWI (P = .008). No difference was found in the CNR of HHs (P = .52), ADC values for HHs (P = .79), and LMs (P = .29) between the two sequences. To differentiate between HHs and LMs, the cutoff ADC values were 1.38 × 10-3 mm2/s in PI-DWI and 1.37 × 10-3 mm2/s in EPICS-DWI. The area under the ROC curve (P = .86), sensitivity (P > .99), and specificity (P > .99) did not vary.

CONCLUSIONS

The LMs were more visible in EPICS-DWI than in PI-DWI. However, the cutoff ADC values and diagnostic abilities for differentiating HHs and LMs were almost comparable between the two sequences.

Technical Advancements in Abdominal Diffusion-weighted Imaging

Since its first observation in the 18th century, the diffusion phenomenon has been actively studied by many researchers. Diffusion-weighted imaging (DWI) is a technique to probe the diffusion of water molecules and create a MR image with contrast based on the local diffusion properties. The DWI pixel intensity is modulated by the hindrance the diffusing water molecules experience. This hindrance is caused by structures in the tissue and reflects the state of the tissue. This characteristic makes DWI a unique and effective tool to gain more insight into the tissue's pathophysiological condition. In the past decades, DWI has made dramatic technical progress, leading to greater acceptance in clinical practice. In the abdominal region, however, acquiring DWI with good quality is challenging because of several reasons, such as large imaging volume, respiratory and other types of motion, and difficulty in achieving homogeneous fat suppression. In this review, we discuss technical advancements from the past decades that help mitigate these problems common in abdominal imaging. We describe the use of scan acceleration techniques such as parallel imaging and compressed sensing to reduce image distortion in echo planar imaging. Then we compare techniques developed to mitigate issues due to respiratory motion, such as free-breathing, respiratory-triggering, and navigator-based approaches. Commonly used fat suppression techniques are also introduced, and their effectiveness is discussed. Additionally, the influence of the abovementioned techniques on image quality is demonstrated. Finally, we discuss the current and future clinical applications of abdominal DWI, such as whole-body DWI, simultaneous multiple-slice excitation, intravoxel incoherent motion, and the use of artificial intelligence. Abdominal DWI has the potential to develop further in the future, thanks to scan acceleration and image quality improvement driven by technological advancements. The accumulation of clinical proof will further drive clinical acceptance.

Clinical Evaluation of Scout Accelerated Motion Estimation and Reduction Technique for 3D MR Imaging in the Inpatient and Emergency Department Settings

BACKGROUND AND PURPOSE

A scout accelerated motion estimation and reduction (SAMER) framework has been developed for efficient retrospective motion correction. The goal of this study was to perform an initial evaluation of SAMER in a series of clinical brain MR imaging examinations.

MATERIALS AND METHODS

Ninety-seven patients who underwent MR imaging in the inpatient and emergency department settings were included in the study. SAMER motion correction was retrospectively applied to an accelerated T1-weighted MPRAGE sequence that was included in brain MR imaging examinations performed with and without contrast. Two blinded neuroradiologists graded images with and without SAMER motion correction on a 5-tier motion severity scale (none = 1, minimal = 2, mild = 3, moderate = 4, severe = 5).

RESULTS

The median SAMER reconstruction time was 1 minute 47 seconds. SAMER motion correction significantly improved overall motion grades across all examinations (P < .005). Motion artifacts were reduced in 28% of cases, unchanged in 64% of cases, and increased in 8% of cases. SAMER improved motion grades in 100% of moderate motion cases and 75% of severe motion cases. Sixty-nine percent of nondiagnostic motion cases (grades 4 and 5) were considered diagnostic after SAMER motion correction. For cases with minimal or no motion, SAMER had negligible impact on the overall motion grade. For cases with mild, moderate, and severe motion, SAMER improved the motion grade by an average of 0.3 (SD, 0.5), 1.1 (SD, 0.3), and 1.1 (SD, 0.8) grades, respectively.

CONCLUSIONS

SAMER improved the diagnostic image quality of clinical brain MR imaging examinations with motion artifacts. The improvement was most pronounced for cases with moderate or severe motion.

Aliasing-free reduced field-of-view parallel imaging

PURPOSE

To reconstruct aliasing-free full field-of-view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods.

THEORY AND METHODS

The sensitivity encoding method (SENSE) was extended to the Soft-SENSE models supporting multiple-set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple-set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft-SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction.

RESULTS

Soft-SENSE using multiple-set full FOV CSM and PSF successfully reconstruct aliasing-free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high-resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem.

CONCLUSION

The proposed Soft-SENSE using full FOV CSM and PSF could reconstruct aliasing-free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction.

Technical note: Unexpected external markers artifact in 3D k-space based parallel imaging turbo spin-echo magnetic resonance imaging

PURPOSE

MRI for radiotherapy planning requires spatial referencing using immobilization devices and markers. Clinical images of a difficult-to-interpret artifact are presented, resembling a metastasis, which occurs when combining CAIPIRINHA k-space-based parallel imaging (PI), 3D distortion correction, and external markers.

METHODS

A 3D variable flip angle Turbo Spin Echo sequence was used on a 1.5 T and 3 T MRI using flexible and head and neck coils. Two types of markers were tested: Liquimark LM1 and Spee-D-Mark. A silicone oil phantom was used that represents low signal intensity, such as gray matter. 3D Fourier transforms were also used to show the issue's origin.

RESULTS

The markers can appear in an unexpected region of a patient, not in the same original or reconstructed slice nor in a rectilinear direction in a slice, especially when using CAIPIRINHA acceleration with 3D distortion correction. The probability of occurrence was respectively 13% and 80% for distances of <=2 mm and >2 mm between marker and patient, for example when using thermoplastic masks. Clinical cases are shown where this semi-randomly occurring artifact appears post contrast only, and thus can be interpreted as metastases. The artifact did not appear when using compressed sensing acceleration.

CONCLUSION

Markers used for radiotherapy MRI application can introduce additional artifacts that can be interpreted as metastases. However, other high signal intensity structures on the surface of a patient, such as the ear, can lead to an equivalent error.

Diffusion-weighted imaging of the abdomen using echo planar imaging with compressed SENSE: Feasibility, image quality, and ADC value evaluation

OBJECTIVE

To evaluate the feasibility, image quality, and apparent diffusion coefficient (ADC) values of diffusion-weighted imaging (DWI) using echo planar imaging (EPI) with Compressed SENSE (EPICS-DWI) of the abdomen and to compare them with conventional single-shot EPI with parallel imaging (PI) technique (PI-DWI).

MATERIALS AND METHODS

This prospective study included 46 participants with known or suspected upper abdominal diseases (19 men and 27 women, mean age, 68 years) who underwent MRI. DWI acquisition was performed using free-breathing two-dimensional fat-suppressed PI-DWI and EPICS-DWI with SENSE or compressed sensing (CS) factor, 3.0. Moreover, image noise and contour of liver and pancreas were qualitatively evaluated using a five-point scale. The mean ADC value and standard deviation (SD) of the liver, pancreas, and spleen were measured, and the coefficient of variation (CV) was calculated. Qualitative and quantitative parameters were compared between PI-DWI and EPICS-DWI using the Wilcoxon test.

RESULTS

The mean image quality scores for image noise and contour of liver and pancreas were higher in EPICS-DWI compared with PI-DWI (P < 0.0001). Moreover, the mean ADC values of the liver and pancreas were higher in EPICS-DWI compared with PI-DWI (P < 0.0001), but that of spleen was not significantly different. The mean SD and CV of the liver, pancreas, and spleen were lower in EPICS-DWI compared with PI-DWI (P < 0.0001-0.032).

CONCLUSION

EPICS-DWI could be feasible in MRI of the abdomen and significantly improve image quality compared with PI-DWI in aggressive setting. ADC value measurements were higher in EPICS-DWI compared with PI-DWI.

Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2_S), the novel T2 TSE sequence with DLR (T2_DLR) was applied in three planes. Overall, the acquisition time for T2_S resulted in 10:21 min versus 3:50 min for T2_DLR. The image evaluation was performed by two radiologists independently using a Likert scale ranging from 1-4 (4 best) applying the following criteria: noise levels, artifacts, overall image quality, diagnostic confidence, and lesion conspicuity. Additionally, T2 and PI-RADS scoring were performed. The mean patient age was 69 ± 9 years (range, 49-85 years). The noise levels and the extent of the artifacts were evaluated to be significantly improved in T2_DLR versus T2_S by both readers (p < 0.05). Overall image quality was also evaluated to be superior in T2_DLR versus T2_S in all three acquisition planes (p = 0.005-<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2_DLR with a median of 4 versus a median of 3 in T2_S (p = 0.001 and <0.001, respectively). T2-weighted TSE imaging of the prostate in three planes with an acquisition time reduction of more than 60% including DLR is feasible with a significant improvement of image quality.

Image Quality Improvement of Dynamic Contrast-Enhanced Gradient Echo Magnetic Resonance Imaging by Iterative Denoising and Edge Enhancement

OBJECTIVES

The aim of this study was to investigate the impact of a novel edge enhancement and iterative denoising algorithm in 1.5-T T1-weighted dynamic contrast-enhanced (DCE) gradient echo (GRE) magnetic resonance imaging of the abdomen on image quality, noise levels, diagnostic confidence, and lesion detectability.

MATERIALS AND METHODS

Fifty patients who underwent a clinically indicated magnetic resonance imaging with DCE imaging of the abdomen between June and August 2020 were included in this retrospective, monocentric, institutional review board-approved study. For DCE imaging, a series of 3 volume interpolated breath-hold examinations (VIBEs) was performed. The raw data of all DCE imaging studies were processed twice, once using standard reconstruction (DCES) and again using an edge enhancement and iterative denoising approach (DCEDE). All imaging studies were randomly reviewed by 2 radiologists independently regarding noise levels, arterial contrast, sharpness of vessels, overall image quality, and diagnostic confidence using a Likert scale ranging from 1 to 4, with 4 being the best. Furthermore, lesion detectability was evaluated using the same ranking system.

RESULTS

All 50 imaging studies were successfully reconstructed with both methods. Interreader agreement (Cohen κ) was substantial to perfect for both readers. Arterial contrast and sharpness of vessels were rated superior by both readers with a median of 4 in DCEDE versus a median of 3 in DCES (P < 0.001). Furthermore, noise levels as well as overall image quality were rated higher with a median of 4 in DCEDE compared with a median of 3 in DCES (P < 0.001). Lesion detectability was evaluated to be superior in DCEDE with a median of 4 versus DCES with a median of 3 (P < 0.001). Consequently, diagnostic confidence was also rated to be superior in DCEDE with a median of 4 versus DCES with a median of 3 (P < 0.001).

CONCLUSIONS

Iterative denoising and edge enhancement are feasible in DCE imaging of the abdomen providing superior arterial contrast, noise levels, and overall image quality. Furthermore, lesion detectability and diagnostic confidence were significantly improved using this novel reconstruction method. Further reduction of acquisition time might be possible via reduction of increased noise levels using this presented method.

Technical Improvements in Head and Neck MR Imaging: At the Cutting Edge

Head and neck MR imaging is technically challenging because of magnetic field inhomogeneity, respiratory and swallowing motion, and necessity of high-resolution imaging to trace key anatomic structures. These challenges have been answered by advances in MR imaging technology, including isovolumetric three-dimensional imaging, robust fat-water separation techniques, and novel deep learning-based reconstruction algorithms. New applications of MR imaging have been advanced and functional imaging has been improved. Improvements in acquisition and reconstruction technique facilitate novel applications of morphologic and functional imaging. This results in opportunities to improve diagnosis, staging, and treatment selection through application of advanced MR imaging techniques.

Non-Cartesian GRAPPA and coil combination using interleaved calibration data - application to concentric-ring MRSI of the human brain at 7T

PURPOSE

Proton MR spectroscopic imaging (MRSI) benefits from B₀ ≥ 7T and multichannel receive coils, promising substantial resolution improvements. However, MRSI acquisition with high spatial resolution requires efficient acceleration and coil combination. To speed up the already-fast sampling via concentric rings, we implemented additional, non-Cartesian, hybrid through-time/through-k-space (tt/tk)-generalized autocalibrating partially parallel acquisition (GRAPPA). A new multipurpose interleaved calibration scan (interleaved MUSICAL) acquires reference data for both coil combination and PI. This renders the reconstruction process (especially PI) less sensitive to instabilities.

METHODS

Six healthy volunteers were scanned at 7T. Three calibration datasets for coil combination and PI were recorded: a) iMUSICAL, b) static MUSICAL as prescan, c) moved MUSICAL as prescan with misaligned head position. The coil combination performance, including motion sensitivity, of iMUSICAL was compared to MUSICAL for single-slice free induction decay (FID)-MRSI. Through-time/through-k-space-GRAPPA with constant/variable-density undersampling was evaluated on the same data, comparing the three calibration datasets. Additionally, the proposed method was successfully applied to 3D whole-brain FID-MRSI.

RESULTS

Using iMUSICAL for coil combination yielded the highest signal-to-noise ratio (SNR) (+9%) and lowest Cramer-Rao lower bounds (CRLBs) (-6%) compared to both MUSICAL approaches, with similar metabolic map quality. Also, excellent mean g-factors of 1.07 and low residual lipid aliasing were obtained when using iMUSICAL as calibration data for two-fold, variable-density undersampling, while significantly degraded metabolic maps were obtained using the misaligned MUSICAL calibration data.

CONCLUSION

Through-time/through-k-space-GRAPPA can accelerate already time-efficient non-Cartesian spatial-spectral 2D/3D-MRSI encoding even further. Particularly promising results have been achieved using iMUSICAL as a robust, interleaved multipurpose calibration for MRSI reconstruction, without extra calibration prescan.

Language Mapping With fMRI: Current Standards and Reproducibility

Clinical use of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a relatively new phenomenon, with only about 3 decades of collective experience. Nevertheless, task-based BOLD fMRI has been widely accepted for presurgical planning, over traditional methods, which are invasive and at times perilous. Many studies have demonstrated the ability of BOLD fMRI to make substantial clinical impact with respect to surgical planning and preoperative risk assessment, especially to localize the eloquent motor and visual areas. Reproducibility and repeatability of language fMRI are important in the assessment of its clinical usefulness. There are national efforts currently underway to standardize language fMRI. The American Society of Functional Neuroradiology (ASFNR) has recently provided guidelines on fMRI paradigm algorithms for presurgical language assessment for language lateralization and localization. In this review article, we provide a comprehensive overview of current standards of language fMRI mapping and its reproducibility.

PEC-GRAPPA reconstruction of simultaneous multislice EPI with slice-dependent 2D Nyquist ghost correction

PURPOSE

To provide simultaneous multislice (SMS) EPI reconstruction with k-space implementation and robust Nyquist ghost correction.

METHODS

2D phase error correction SENSE (PEC-SENSE) was recently developed for Nyquist ghost correction in SMS EPI reconstruction for which virtual coil simultaneous autocalibration and k-space estimation (VC-SAKE) was used to remove slice-dependent Nyquist ghosts and intershot 2D phase variations in multi-shot EPI reference scan. However, masking coil sensitivity maps to exclude background region in PEC-SENSE and manually selecting slice-wise target ranks in VC-SAKE are cumbersome procedures in practice. To avoid masking, the concept of PEC-SENSE is extended to k-space implementation and termed as PEC-GRAPPA. Furthermore, a singular value shrinkage scheme is incorporated in VC-SAKE to circumvent the empirical slice-wise target rank selection. PEC-GRAPPA was evaluated and compared to PEC-SENSE with/without masking and 1D linear phase correction GRAPPA.

RESULTS

PEC-GRAPPA robustly reconstructed SMS EPI images from 7T phantom and human brain data, effectively removing the phase error-induced artifacts. The resulting residual artifact level and temporal SNR were comparable to those by PEC-SENSE with careful tuning. PEC-GRAPPA outperformed PEC-SENSE without masking and 1D linear phase correction GRAPPA.

CONCLUSION

Our proposed PEC-GRAPPA approach effectively removes the artifacts caused by Nyquist ghosts in SMS EPI without cumbersome tuning. This approach provides a robust and practical implementation of SMS EPI reconstruction in k-space with slice-dependent 2D Nyquist ghost correction.

Robust Self-Calibrating nCPMG Acquisition: Application to Body Diffusion-Weighted Imaging

This paper demonstrates a robust diffusion-weighted single-shot fast spin echo (SS-FSE) sequence in the presence of significant off-resonance, which includes a variable-density acquisition and a self-calibrated reconstruction as improvements. A non-Carr-Purcell-Meiboom-Gill (nCPMG) SS-FSE acquisition stabilizes both the main and parasitic echo families for each echo. This preserves both the in-phase and quadrature components of the magnetization throughout the echo train. However, nCPMG SS-FSE also promotes aliasing of the quadrature component, which complicates reconstruction. A new acquisition and reconstruction approach is presented here, where the field-of-view is effectively doubled, but a partial k-space and variable density sampling is used to improve scan efficiency. The technique is presented in phantom scans to validate SNR and robustness against rapidly varying object phase. In vivo healthy volunteer examples and the clinical cases are demonstrated in abdominal imaging. This new approach provides comparable SNR to previous nCPMG acquisition techniques as well as providing more uniform apparent diffusion coefficient maps in phantom scans. In vivo scans suggest that this method is more robust against motion than previous approaches. The proposed reconstruction is an improvement to the nCPMG sequence as it is auto-calibrating and is justified to accurately treat the signal model for the nCPMG SS-FSE sequence.

Diagnostic Performance of a 10-Minute Gadolinium-Enhanced Brain MRI Protocol Compared with the Standard Clinical Protocol for Detection of Intracranial Enhancing Lesions

BACKGROUND AND PURPOSE

The development of new MR imaging scanners with stronger gradients and improvement in coil technology, allied with emerging fast imaging techniques, has allowed a substantial reduction in MR imaging scan times. Our goal was to develop a 10-minute gadolinium-enhanced brain MR imaging protocol with accelerated sequences and to evaluate its diagnostic performance compared with the standard clinical protocol.

MATERIALS AND METHODS

Fifty-three patients referred for brain MR imaging with contrast were scanned with a 3T scanner. Each MR image consisted of 5 basic fast precontrast sequences plus standard and accelerated versions of the same postcontrast T1WI sequences. Two neuroradiologists assessed the image quality and the final diagnosis for each set of postcontrast sequences and compared their performances.

RESULTS

The acquisition time of the combined accelerated pre- and postcontrast sequences was 10 minutes and 15 seconds; and of the fast postcontrast sequences, 3 minutes and 36 seconds, 46% of the standard sequences. The 10-minute postcontrast axial T1WI had fewer image artifacts (P < .001) and better overall diagnostic quality (P < .001). Although the 10-minute MPRAGE sequence showed a tendency to have more artifacts than the standard sequence (P = .08), the overall diagnostic quality was similar (P = .66). Moreover, there was no statistically significant difference in the diagnostic performance between the protocols. The sensitivity, specificity, and accuracy values for the 10-minute protocol were 100.0%, 88.9%, and 98.1%.

CONCLUSIONS

The 10-minute brain MR imaging protocol with contrast is comparable in diagnostic performance with the standard protocol in an inpatient motion-prone population, with the additional benefits of reducing acquisition times and image artifacts.

Simultaneous Multislice-Based 5-Minute Lumbar Spine MRI Protocol: Initial Experience in a Clinical Setting

BACKGROUND AND PURPOSE

Previous studies have used parallel imaging (PI) techniques to decrease spine magnetic resonance imaging (MRI) protocol acquisition times. Recently developed MRI sequences allow even faster acquisitions. Our purpose was to develop a lumbar spine MRI protocol using PI with GRAPPA (generalized autocalibrating partially parallel acquisition) and a simultaneous multislice (SMS)-based sequence and to evaluate its diagnostic performance compared to a standard lumbar spine MRI protocol.

METHODS

Ten patients were scanned in a 3-Tesla scanner (MAGNETOM Skyra, Siemens Healthcare). Each patient was imaged using a standard protocol and an optimized fast protocol acquiring the same contrasts and planes. The fast protocol included sagittal T1, T2, and fat suppressed T2 sequences accelerated with GRAPPA and an SMS-based axial T2-weighted sequence using a high-density spine coil (Siemens MR, 30 channel spine). Two blinded neuroradiologists independently assessed image quality and diagnostic accuracy for clinically relevant imaging findings.

RESULTS

The fast protocol acquisition time was 5:28 minutes, compared with 16:30 minutes for the standard protocol. Both protocols had a similar performance for definition of anatomical structures, diagnostic quality, and identification of clinically relevant imaging findings. There were more artifacts in the SMS Turbo Spin Echo (P = .014) sequence without compromising diagnostic performance. Artifacts in the remaining non-SMS sequences were similar in both protocols (P > .180). The sensitivity, specificity, and accuracy of the 5-minute protocol were 92.3%, 100.0%, and 99.6%, respectively, for the clinically relevant findings (P = 1.0, interrater agreement .57).

CONCLUSIONS

A 5-minute SMS-based MRI protocol for lumbar spine imaging is feasible and can be achieved without significant impact in the overall diagnostic quality.

Cerebral lesions on 7 tesla MRI in patients with sickle cell anemia

BACKGROUND

Patients with sickle cell anemia (SCA) are at a high risk to develop cerebral damage. Most common are silent cerebral infarctions (SCIs), visible as white matter hyperintensities (WMHs) on MRI in a patient without neurological deficits. The etiology of SCIs remains largely unclear. In addition, patients are at an increased risk for overt stroke, which is associated with large vessel disease. This classification based on the presence or absence of neurological deficits may not be the most fitting for research purposes, as it does not match the different underlying pathology. A classification based on imaging findings may therefore be a more straightforward approach for research purposes. We explored the feasibility to identify imaging features of SCIs in young, asymptomatic patients with SCA using ultra high-field 7 Tesla (7T) MRI. 7T MRI has a high resolution, which offers a unique chance to investigate small subclinical brain lesions in detail. To explore the superiority of 7T in identifying imaging abnormalities, we compared our results with 3T MRI.

METHODS

Ten young, neurologically asymptomatic patients with SCA underwent 7T and 3T MRI; 10 healthy, age-matched controls underwent 7T MRI. We used existing neuroimaging standards to classify the brain lesions. We scored 7T and 3T scans separately, blinded for all other results.

RESULTS

Using 7T MRI, we identified more patients with intracerebral lesions (9/10 vs. 5/10), a higher total count of WMHs (203 vs. 190, p = 0.016) and more lacunes (5 vs. 4) compared to 3T MRI. Abnormalities seen on 7T, which could not be identified on 3T, were cortical hyperintensities (in 3/10) and a different aspect of irregular WMHs, closely associated with cortical hyperintensities in a patient with large vessel stenosis. In 7 controls, a total of 13 WMHs were present.

CONCLUSION

Using 7T MRI, we identified more intracerebral lesions compared to 3T, and found several abnormalities not visible on 3T. 7T MRI in SCA seems of particular interest to study the cortical involvement and the relation between WMHs and the cortex. We found some imaging features that are thought to be representative for small vessel disease, including WMHs, lacunes and prominent perivascular spaces; to understand whether small vessel disease plays a role in SCA requires further research.