Knee imaging: Rapid three-dimensional fast spin-echo using compressed sensing

[Accelerated musculoskeletal magnetic resonance imaging with deep learning-based image reconstruction at 0.55 T-3 T]

CLINICAL/METHODICAL ISSUE

Magnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times can pose practical barriers in clinical practice.

STANDARD RADIOLOGICAL METHODS

MRI is the established modality of choice in the diagnostic workup of injuries and diseases of the musculoskeletal system due to its high spatial resolution, excellent signal-to-noise ratio (SNR), and unparalleled soft tissue contrast.

METHODOLOGICAL INNOVATIONS

Continuous advances in hardware and software technology over the last few decades have enabled four-fold acceleration of 2D turbo-spin-echo (TSE) without compromising image quality or diagnostic performance. The recent clinical introduction of deep learning (DL)-based image reconstruction algorithms helps to minimize further the interdependency between SNR, spatial resolution and image acquisition time and allows the use of higher acceleration factors.

PERFORMANCE

The combined use of advanced acceleration techniques and DL-based image reconstruction holds enormous potential to maximize efficiency, patient comfort, access, and value of musculoskeletal MRI while maintaining excellent diagnostic accuracy.

ACHIEVEMENTS

Accelerated MRI with DL-based image reconstruction has rapidly found its way into clinical practice and proven to be of added value. Furthermore, recent investigations suggest that the potential of this technology does not yet appear to be fully harvested.

PRACTICAL RECOMMENDATIONS

Deep learning-reconstructed fast musculoskeletal MRI examinations can be reliably used for diagnostic work-up and follow-up of musculoskeletal pathologies in clinical practice.

Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers

BACKGROUND

To investigate the potential of combining compressed sensing (CS) and artificial intelligence (AI), in particular deep learning (DL), for accelerating three-dimensional (3D) magnetic resonance imaging (MRI) sequences of the knee.

METHODS

Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence with four different acceleration levels (10, 13, 15, and 17). All sequences were accelerated with CS and reconstructed using the conventional and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using seven criteria on a 5-point-Likert-scale (overall impression, artifacts, delineation of the anterior cruciate ligament, posterior cruciate ligament, menisci, cartilage, and bone). Using mixed models, all CS-AI sequences were compared to the clinical standard (sense sequence with an acceleration factor of 2) and CS sequences with the same acceleration factor.

RESULTS

3D sequences reconstructed with CS-AI achieved significantly better values for subjective image quality compared to sequences reconstructed with CS with the same acceleration factor (p ≤ 0.001). The images reconstructed with CS-AI showed that tenfold acceleration may be feasible without significant loss of quality when compared to the reference sequence (p ≥ 0.999).

CONCLUSIONS

For 3-T 3D-MRI of the knee, a DL-based algorithm allowed for additional acceleration of acquisition times compared to the conventional approach. This study, however, is limited by its small sample size and inclusion of only healthy volunteers, indicating the need for further research with a more diverse and larger sample.

TRIAL REGISTRATION

DRKS00024156.

RELEVANCE STATEMENT

Using a DL-based algorithm, 54% faster image acquisition (178 s versus 384 s) for 3D-sequences may be possible for 3-T MRI of the knee.

KEY POINTS

• Combination of compressed sensing and DL improved image quality and allows for significant acceleration of 3D knee MRI. • DL-based algorithm achieved better subjective image quality than conventional compressed sensing. • For 3D knee MRI at 3 T, 54% faster image acquisition may be possible.

Five-minute knee MRI: An AI-based super resolution reconstruction approach for compressed sensing. A validation study on healthy volunteers

PURPOSE

To investigate the potential of combining Compressed Sensing (CS) and a newly developed AI-based super resolution reconstruction prototype consisting of a series of convolutional neural networks (CNN) for a complete five-minute 2D knee MRI protocol.

METHODS

In this prospective study, 20 volunteers were examined using a 3T-MRI-scanner (Ingenia Elition X, Philips). Similar to clinical practice, the protocol consists of a fat-saturated 2D-proton-density-sequence in coronal, sagittal and transversal orientation as well as a sagittal T1-weighted sequence. The sequences were acquired with two different resolutions (standard and low resolution) and the raw data reconstructed with two different reconstruction algorithms: a conventional Compressed SENSE (CS) and a new CNN-based algorithm for denoising and subsequently to interpolate and therewith increase the sharpness of the image (CS-SuperRes). Subjective image quality was evaluated by two blinded radiologists reviewing 8 criteria on a 5-point Likert scale and signal-to-noise ratio calculated as an objective parameter.

RESULTS

The protocol reconstructed with CS-SuperRes received higher ratings than the time-equivalent CS reconstructions, statistically significant especially for low resolution acquisitions (e.g., overall image impression: 4.3 ± 0.4 vs. 3.4 ± 0.4, p < 0.05). CS-SuperRes reconstructions for the low resolution acquisition were comparable to traditional CS reconstructions with standard resolution for all parameters, achieving a scan time reduction from 11:01 min to 4:46 min (57 %) for the complete protocol (e.g. overall image impression: 4.3 ± 0.4 vs. 4.0 ± 0.5, p < 0.05).

CONCLUSION

The newly-developed AI-based reconstruction algorithm CS-SuperRes allows to reduce scan time by 57% while maintaining unchanged image quality compared to the conventional CS reconstruction.

An applicability study of rapid artificial intelligence-assisted compressed sensing (ACS) in anal fistula magnetic resonance imaging

Objective

To evaluate the applicability of artificial intelligence-assisted compressed sensing (ACS) to anal fistula magnetic resonance imaging (MRI).

Methods

51 patients were included in this study and underwent T2-weighted sequence of MRI examinations both with ACS and without ACS technology in a 3.0 T MR scanner. Subjective image quality scores, and objective image quality-related metrics including scanning time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were evaluated and statistically compared between the images collected with and without ACS.

Results

No significant difference in the subjective image quality of lesion conspicuity was observed between the two groups. However, ACS MRI decreased the acquisition time with regard to control group (74.00 s vs. 156.00 s). Besides, SNR of perianal and muscle in the ACS group was significantly higher than that of the control group (164.07 ± 33.35 vs 130.81 ± 29.10, p < 0.001; 109.87 ± 22.01 vs 87.61 ± 17.95, p < 0.001; respectively). The CNR was significantly higher in the ACS group than in the control group (54.02 ± 23.98 vs 43.20 ± 21.00; p < 0.001). Moreover, the accuracy rate of the ACS groups in evaluating the direction and internal opening of the fistula was 88.89 %, exactly the same as that of the control group.

Conclusion

We demonstrated the applicability of using ACS to accelerate MR of anal fistulas with improved SNR and CNR. Meanwhile, the accuracy rates of the ACS group and the control were equivalent in evaluating the direction and internal opening of the fistula, based on the results of surgical exploration.

Accelerated Musculoskeletal Magnetic Resonance Imaging

With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Imaging of early-stage osteoarthritis: the needs and challenges for diagnosis and classification

In an effort to boost the development of new management strategies for OA, there is currently a shift in focus towards the diagnosis and treatment of early-stage OA. It is important to distinguish diagnosis from classification of early-stage OA. Diagnosis takes place in clinical practice, whereas classification is a process to stratify participants with OA in clinical research. For both purposes, there is an important opportunity for imaging, especially with MRI. The needs and challenges differ for early-stage OA diagnosis versus classification. Although it fulfils the need of high sensitivity and specificity for making a correct diagnosis, implementation of MRI in clinical practice is challenged by long acquisition times and high costs. For classification in clinical research, more advanced MRI protocols can be applied, such as quantitative, contrast-enhanced, or hybrid techniques, as well as advanced image analysis methods including 3D morphometric assessments of joint tissues and artificial intelligence approaches. It is necessary to follow a step-wise and structured approach that comprises, technical validation, biological validation, clinical validation, qualification, and cost-effectiveness, before new imaging biomarkers can be implemented in clinical practice or clinical research.

Deep Learning MRI Reconstruction for Accelerating Turbo Spin Echo Hand and Wrist Imaging: A Comparison of Image Quality, Visualization of Anatomy, and Detection of Common Pathologies with Standard Imaging

RATIONALE AND OBJECTIVES

Magnetic resonance imaging (MRI) of the hand and wrist is a routine MRI examination and takes about 15-20 minutes, which can lead to problems resulting from the relatively long scan time, such as decreased image quality due to motion artifacts and lower patient throughput. The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the hand and wrist regarding image quality, visualization of anatomy, and diagnostic performance concerning common pathologies.

MATERIALS AND METHODS

Twenty-one patients (mean age: 43 ± 19 [19-85] years, 10 men, 11 female) were prospectively enrolled in this study between October 2020 and June 2021. Each participant underwent two MRI protocols: first, standard fully sampled TSE sequences reconstructed with a standard GRAPPA reconstruction (TSES) and second, prospectively undersampled TSE sequences using a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSEDL). Both protocols were acquired consecutively in one examination. Two experienced MSK-imaging radiologists qualitatively evaluated the images concerning image quality, noise, edge sharpness, artifacts, and diagnostic confidence, as well as the delineation of anatomical structures (triangular fibrocartilage complex, tendon of the extensor carpi ulnaris muscle, extrinsic and intrinsic ligaments, median nerve, cartilage) using a five-point Likert scale and assessed common pathologies. Wilcoxon signed-rank test and kappa statistics were performed to compare the sequences.

RESULTS

Overall image quality, artifacts, delineation of anatomical structures, and diagnostic confidence of TSEDL were rated to be comparable to TSES (p > 0.05). Additionally, TSEDL showed decreased image noise (4.90, median 5, IQR 5-5) compared to TSES (4.52, median 5, IQR 4-5, p < 0.05) and improved edge sharpness (TSEDL: 4.10, median 4, IQR 3.5-5; TSES: 3.57, median 4, IQR 3-4; p < 0.05). Inter- and intrareader agreement was substantial to almost perfect (κ = 0.632-1.000) for the detection of common pathologies. Time of acquisition could be reduced by more than 60% with the protocol using TSEDL.

CONCLUSION

Compared to TSES, TSEDL provided decreased noise and increased edge sharpness, equal image quality, delineation of anatomical structures, detection of pathologies, and diagnostic confidence. Therefore, TSEDL may be clinically relevant for hand and wrist imaging, as it reduces examination time by more than 60%, thus increasing patient comfort and patient throughput.

Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers

BACKGROUND

To investigate the potential of combining compressed sensing (CS) and deep learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) of the shoulder.

METHODS

Twenty healthy volunteers were examined using at 3-T scanner with a fat-saturated, coronal, 2D proton density-weighted sequence with four acceleration levels (2.3, 4, 6, and 8) and a 3D sequence with three acceleration levels (8, 10, and 13), all accelerated with CS and reconstructed using the conventional algorithm and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using 6 criteria on a 5-point Likert scale (overall impression, artifacts, and delineation of the subscapularis tendon, bone, acromioclavicular joint, and glenoid labrum). Objective image quality was measured by calculating signal-to-noise-ratio, contrast-to-noise-ratio, and a structural similarity index measure. All reconstructions were compared to the clinical standard (CS 2D acceleration factor 2.3; CS 3D acceleration factor 8). Additionally, subjective and objective image quality were compared between CS and CS-AI with the same acceleration levels.

RESULTS

Both 2D and 3D sequences reconstructed with CS-AI achieved on average significantly better subjective and objective image quality compared to sequences reconstructed with CS with the same acceleration factor (p ≤ 0.011). Comparing CS-AI to the reference sequences showed that 4-fold acceleration for 2D sequences and 13-fold acceleration for 3D sequences without significant loss of quality (p ≥ 0.058).

CONCLUSIONS

For MRI of the shoulder at 3 T, a DL-based algorithm allowed additional acceleration of acquisition times compared to the conventional approach.

RELEVANCE STATEMENT

The combination of deep-learning and compressed sensing hold the potential for further scan time reduction in 2D and 3D imaging of the shoulder while providing overall better objective and subjective image quality compared to the conventional approach.

TRIAL REGISTRATION

DRKS00024156.

KEY POINTS

• Combination of compressed sensing and deep learning improved image quality and allows for significant acceleration of shoulder MRI. • Deep learning-based algorithm achieved better subjective and objective image quality than conventional compressed sensing. • For shoulder MRI at 3 T, 40% faster image acquisition for 2D sequences and 38% faster image acquisition for 3D sequences may be possible.

Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network

OBJECTIVES

Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSEDL) for routine clinical hip MRI at 1.5 and 3 T in terms of feasibility, image quality, and diagnostic performance.

MATERIAL AND METHODS

In this prospective, monocentric study, TSEDL was implemented clinically and evaluated in 14 prospectively enrolled patients undergoing a clinically indicated hip MRI at 1.5 and 3T between October 2020 and May 2021. Each patient underwent two examinations: For the first exam, we used standard sequences with generalized autocalibrating partial parallel acquisition reconstruction (TSES). For the second exam, we implemented prospectively undersampled TSE sequences with DL reconstruction (TSEDL). Two radiologists assessed the TSEDL and TSES regarding image quality, artifacts, noise, edge sharpness, diagnostic confidence, and delineation of anatomical structures using an ordinal five-point Likert scale (1 = non-diagnostic; 2 = poor; 3 = moderate; 4 = good; 5 = excellent). Both sequences were compared regarding the detection of common pathologies of the hip. Comparative analyses were conducted to assess the differences between TSEDL and TSES.

RESULTS

Compared with TSES, TSEDL was rated to be significantly superior in terms of image quality (p ≤ 0.020) with significantly reduced noise (p ≤ 0.001) and significantly improved edge sharpness (p = 0.003). No difference was found between TSES and TSEDL concerning the extent of artifacts, diagnostic confidence, or the delineation of anatomical structures (p > 0.05). Example acquisition time reductions for the TSE sequences of 52% at 3 Tesla and 70% at 1.5 Tesla were achieved.

CONCLUSION

TSEDL of the hip is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared with TSES, reducing the acquisition time significantly.

Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction

OBJECTIVE

To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based reconstruction (DLR) methods for evaluation of shoulder.

MATERIALS AND METHODS

We included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics.

RESULTS

Ninety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600-0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691-1).

CONCLUSION

Accelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.

Conventional and Deep-Learning-Based Image Reconstructions of Undersampled K-Space Data of the Lumbar Spine Using Compressed Sensing in MRI: A Comparative Study on 20 Subjects

Compressed sensing accelerates magnetic resonance imaging (MRI) acquisition by undersampling of the k-space. Yet, excessive undersampling impairs image quality when using conventional reconstruction techniques. Deep-learning-based reconstruction methods might allow for stronger undersampling and thus faster MRI scans without loss of crucial image quality. We compared imaging approaches using parallel imaging (SENSE), a combination of parallel imaging and compressed sensing (COMPRESSED SENSE, CS), and a combination of CS and a deep-learning-based reconstruction (CS AI) on raw k-space data acquired at different undersampling factors. 3D T2-weighted images of the lumbar spine were obtained from 20 volunteers, including a 3D sequence (standard SENSE), as provided by the manufacturer, as well as accelerated 3D sequences (undersampling factors 4.5, 8, and 11) reconstructed with CS and CS AI. Subjective rating was performed using a 5-point Likert scale to evaluate anatomical structures and overall image impression. Objective rating was performed using apparent signal-to-noise and contrast-to-noise ratio (aSNR and aCNR) as well as root mean square error (RMSE) and structural-similarity index (SSIM). The CS AI 4.5 sequence was subjectively rated better than the standard in several categories and deep-learning-based reconstructions were subjectively rated better than conventional reconstructions in several categories for acceleration factors 8 and 11. In the objective rating, only aSNR of the bone showed a significant tendency towards better results of the deep-learning-based reconstructions. We conclude that CS in combination with deep-learning-based image reconstruction allows for stronger undersampling of k-space data without loss of image quality, and thus has potential for further scan time reduction.

Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study

PURPOSE

To evaluate whether deep learning reconstruction (DLR) accelerates the acquisition of 1.5-T magnetic resonance imaging (MRI) knee data without image deterioration.

MATERIALS AND METHODS

Twenty-one healthy volunteers underwent MRI of the right knee on a 1.5-T MRI scanner. Proton-density-weighted images with one or four numbers of signal averages (NSAs) were obtained via compressed sensing, and DLR was applied to the images with 1 NSA to obtain 1NSA-DLR images. The 1NSA-DLR and 4NSA images were compared objectively (by deriving the signal-to-noise ratios of the lateral and the medial menisci and the contrast-to-noise ratios of the lateral and the medial menisci and articular cartilages) and subjectively (in terms of the visibility of the anterior cruciate ligament, the medial collateral ligament, the medial and lateral menisci, and bone) and in terms of image noise, artifacts, and overall diagnostic acceptability. The paired t-test and Wilcoxon signed-rank test were used for statistical analyses.

RESULTS

The 1NSA-DLR images were obtained within 100 s. The signal-to-noise ratios (lateral: 3.27 ± 0.30 vs. 1.90 ± 0.13, medial: 2.71 ± 0.24 vs. 1.80 ± 0.15, both p < 0.001) and contrast-to-noise ratios (lateral: 2.61 ± 0.51 vs. 2.18 ± 0.58, medial 2.19 ± 0.32 vs. 1.97 ± 0.36, both p < 0.001) were significantly higher for 1NSA-DLR than 4NSA images. Subjectively, all anatomical structures (except bone) were significantly clearer on the 1NSA-DLR than on the 4NSA images. Also, in the former images, the noise was lower, and the overall diagnostic acceptability was higher.

CONCLUSION

Compared with the 4NSA images, the 1NSA-DLR images exhibited less noise, higher overall image quality, and allowed more precise visualization of the menisci and ligaments.

High-resolution dynamic susceptibility contrast perfusion imaging using higher-order temporal smoothness regularization

PURPOSE

To improve image quality and resolution of dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) by developing acquisition and reconstruction methods exploiting the temporal regularity property of DSC-PWI signal.

THEORY AND METHODS

A novel regularized reconstruction is proposed that recovers DSC-PWI series from interleaved segmented spiral k-space acquisition using higher order temporal smoothness (HOTS) properties of the DSC-PWI signal. The HOTS regularization is designed to tackle representational insufficiency of the standard first-order temporal regularizations for supporting higher accelerations. The higher accelerations allow for k-space coverage with shorter spiral interleaves resulting in improved acquisition point spread function, and acquisition of images at multiple TEs for more accurate DSC-PWI analysis.

RESULTS

The methods were evaluated in simulated and in-vivo studies. HOTS regularization provided increasingly more accurate models for DSC-PWI than the standard first-order methods with either quadratic or robust norms at the expense of increased noise. HOTS DSC-PWI optimized for noise and accuracy demonstrated significant advantages over both spiral DSC-PWI without temporal regularization and traditional echo-planar DSC-PWI, improving resolution and mitigating image artifacts associated with long readout, including blurring and geometric distortions. In context of multi-echo DSC-PWI, the novel methods allowed ∼4.3× decrease of voxel volume, providing 2× number of TEs compared to the previously published results.

CONCLUSIONS

Proposed HOTS reconstruction combined with dynamic spiral sampling represents a valid mechanism for improving image quality and resolution of DSC-PWI significantly beyond those available with established fast imaging techniques.

Emerging Technology in Musculoskeletal MRI and CT

This article provides a focused overview of emerging technology in musculoskeletal MRI and CT. These technological advances have primarily focused on decreasing examination times, obtaining higher quality images, providing more convenient and economical imaging alternatives, and improving patient safety through lower radiation doses. New MRI acceleration methods using deep learning and novel reconstruction algorithms can reduce scanning times while maintaining high image quality. New synthetic techniques are now available that provide multiple tissue contrasts from a limited amount of MRI and CT data. Modern low-field-strength MRI scanners can provide a more convenient and economical imaging alternative in clinical practice, while clinical 7.0-T scanners have the potential to maximize image quality. Three-dimensional MRI curved planar reformation and cinematic rendering can provide improved methods for image representation. Photon-counting detector CT can provide lower radiation doses, higher spatial resolution, greater tissue contrast, and reduced noise in comparison with currently used energy-integrating detector CT scanners. Technological advances have also been made in challenging areas of musculoskeletal imaging, including MR neurography, imaging around metal, and dual-energy CT. While the preliminary results of these emerging technologies have been encouraging, whether they result in higher diagnostic performance requires further investigation.

Imaging of Osteoarthritis of the Knee

Knee osteoarthritis is rising in prevalence, and more imaging studies are being requested to evaluate these patients. Although conventional radiographs of the knee are the most widely requested and available studies, other imaging modalities such as MRI, CT, and ultrasound may also be used. This article reviews commonly used imaging modalities, advantages and limitations of each, and their clinical applicability in diagnosing and monitoring knee osteoarthritis. New and advanced imaging techniques are also discussed as possible methods of early diagnosis and improved understanding of osteoarthritis pathophysiology.

3D CAIPIRINHA SPACE versus standard 2D TSE for routine knee MRI: a large-scale interchangeability study

OBJECTIVE

To perform a large-scale interchangeability study comparing 3D controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) TSE with standard 2D TSE for knee MRI.

METHODS

In this prospective study, 250 patients underwent 3 T knee MRI, including a multicontrast 3D CAIPIRINHA SPACE TSE (9:26 min) and a standard 2D TSE protocol (12:14 min). Thirty-three (13%) patients had previous anterior cruciate ligament and/or meniscus surgery. Two radiologists assessed MRIs for image quality and identified pathologies of menisci, ligaments, and cartilage by using a 4-point Likert scale according to the level of diagnostic confidence. Interchangeability of the protocols was tested under the same-reader scenario using a bootstrap percentile confidence interval. Interreader reliability and intermethod concordance were also evaluated.

RESULTS

Despite higher image quality and diagnostic confidence for standard 2D TSE compared to 3D CAIPIRINHA SPACE TSE, the protocols were found interchangeable for diagnosing knee abnormalities, except for patellar (6.8% difference; 95% CI: 4.0, 9.6) and trochlear (3.6% difference; 95% CI: 0.8, 6.6) cartilage defects. The interreader reliability was substantial to almost perfect for 2D and 3D MRI (range κ, 0.785-1 and κ, 0.725-0.964, respectively). Intermethod concordance was almost perfect for all diagnoses (range κ, 0.817-0.986).

CONCLUSION

Multicontrast 3D CAIPIRINHA SPACE TSE and standard 2D TSE protocols perform interchangeably for diagnosing knee abnormalities, except for patellofemoral cartilage defects. Despite the radiologist's preference for 2D TSE imaging, a pursuit towards time-saving 3D TSE knee MRI is justified for routine practice.

KEY POINTS

• Multicontrast 3D CAIPIRINHA SPACE and standard 2D TSE protocols perform interchangeably for diagnosing knee abnormalities, except for patellofemoral cartilage defects. • Radiologists are more confident in diagnosing knee abnormalities on 2D TSE than on 3D CAIPIRINHA SPACE TSE MRI. • Despite the radiologist's preference for 2D TSE, a pursuit towards accelerated 3D TSE knee MRI is justified for routine practice.

Accelerating Knee MRI: 3D Modulated Flip-Angle Technique in Refocused Imaging with an Extended Echo Train and Compressed Sensing

Purpose

The three-dimensional (3D) sequence of magnetic resonance imaging (MRI) plays a critical role in the imaging of musculoskeletal joints; however, its long acquisition time limits its clinical application. In such conditions, compressed sensing (CS) is introduced to accelerate MRI in clinical practice. We aimed to investigate the feasibility of an isotropic 3D variable-flip-angle fast spin echo (FSE) sequence with CS technique (CS-MATRIX) compared to conventional 2D sequences in knee imaging.

Methods

Images from different sequences of both the accelerated CS-MATRIX and the corresponding conventional acquisitions were prospectively analyzed and compared. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the structures within the knees were measured for quantitative analysis. The subjective image quality and diagnostic agreement were compared between CS-MATRIX and conventional 2D sequences. Quantitative and subjective image quality scores were statistically analyzed with the paired t-test and Wilcoxon signed-rank test, respectively. Diagnostic agreements of knee substructure were assessed using Cohen’s weighted kappa statistic.

Results

For quantitative analysis, images from the CS-MATRIX sequence showed a significantly higher SNR than T2-fs 2D sequences for visualizing cartilage, menisci, and ligaments, as well as a higher SNR than proton density (pd) 2D sequences for visualizing menisci and ligaments. There was no significant difference between CS-MATRIX and 2D T2-fs sequences in subjective image quality assessment. The diagnostic agreement was rated as moderate to very good between CS-MATRIX and 2D sequences.

Conclusion

This study demonstrates the feasibility and clinical potential of the CS-MATRIX sequence technique for detecting knee lesions The CS-MATRIX sequence allows for faster knee imaging than conventional 2D sequences, yielding similar image quality to 2D sequences.

The Value of 3 Tesla Field Strength for Musculoskeletal Magnetic Resonance Imaging

ABSTRACT

Musculoskeletal magnetic resonance imaging (MRI) is a careful negotiation between spatial, temporal, and contrast resolution, which builds the foundation for diagnostic performance and value. Many aspects of musculoskeletal MRI can improve the image quality and increase the acquisition speed; however, 3.0-T field strength has the highest impact within the current diagnostic range. In addition to the favorable attributes of 3.0-T field strength translating into high temporal, spatial, and contrast resolution, many 3.0-T MRI systems yield additional gains through high-performance gradients systems and radiofrequency pulse transmission technology, advanced multichannel receiver technology, and high-end surface coils. Compared with 1.5 T, 3.0-T MRI systems yield approximately 2-fold higher signal-to-noise ratios, enabling 4 times faster data acquisition or double the matrix size. Clinically, 3.0-T field strength translates into markedly higher scan efficiency, better image quality, more accurate visualization of small anatomic structures and abnormalities, and the ability to offer high-end applications, such as quantitative MRI and magnetic resonance neurography. Challenges of 3.0-T MRI include higher magnetic susceptibility, chemical shift, dielectric effects, and higher radiofrequency energy deposition, which can be managed successfully. The higher total cost of ownership of 3.0-T MRI systems can be offset by shorter musculoskeletal MRI examinations, higher-quality examinations, and utilization of advanced MRI techniques, which then can achieve higher gains and value than lower field systems. We provide a practice-focused review of the value of 3.0-T field strength for musculoskeletal MRI, practical solutions to challenges, and illustrations of a wide spectrum of gainful clinical applications.

3D MRI of Articular Cartilage

Osteoarthritis, characterized by the breakdown of articular cartilage and other joint structures, is one of the most prevalent and disabling chronic diseases in the United States. Magnetic resonance imaging is a commonly used imaging modality to evaluate patients with joint pain. Both two-dimensional fast spin-echo sequences (2D-FSE) and three-dimensional (3D) sequences are used in clinical practice to evaluate articular cartilage. The 3D sequences have many advantages compared with 2D-FSE sequences, such as their high in-plane spatial resolution, thin continuous slices that reduce the effects of partial volume averaging, and ability to create multiplanar reformat images following a single acquisition. This article reviews the different 3D imaging techniques available for evaluating cartilage morphology, illustrates the strengths and weaknesses of 3D approaches compared with 2D-FSE approaches for cartilage imaging, and summarizes the diagnostic performance of 2D-FSE and 3D sequences for detecting cartilage lesions within the knee and hip joints.

3D MRI of the Shoulder

Magnetic resonance imaging provides a comprehensive evaluation of the shoulder including the rotator cuff muscles and tendons, glenoid labrum, long head biceps tendon, and glenohumeral and acromioclavicular joint articulations. Most institutions use two-dimensional sequences acquired in all three imaging planes to accurately evaluate the many important structures of the shoulder. Recently, the addition of three-dimensional (3D) acquisitions with 3D reconstructions has become clinically feasible and helped improve our understanding of several important pathologic conditions, allowing us to provide added value for referring clinicians. This article briefly describes techniques used in 3D imaging of the shoulder and discusses applications of these techniques including measuring glenoid bone loss in anterior glenohumeral instability. We also review the literature on routine 3D imaging for the evaluation of common shoulder abnormalities as 3D imaging will likely become more common as imaging software continues to improve.

Using the Compressed Sensing Technique for Lumbar Vertebrae Imaging: Comparison with Conventional Parallel Imaging

OBJECTIVE

To compare conventional sensitivity encoding turbo spin-echo (SENSE-TSE) with compressed sensing plus SENSE turbo spin-echo (CS-TSE) in lumbar vertebrae magnetic resonance imaging (MRI).

METHODS

This retrospective study of lumbar vertebrae MRI included 600 patients; 300 patients received SENSE-TSE and 300 patients received CS-TSE. The SENSE acceleration factor was 1.4 for T1WI, 1.7 for T2WI, and 1.7 for PDWI. The CS total acceleration factor was 2.4, 3.6, 4.0, and 4.0 for T1WI, T2WI, PDWI sagittal, and T2WI transverse, respectively. The image quality of each MRI sequence was evaluated objectively by the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and subjectively on a five-point scale. Two radiologists independently reviewed the MRI sequences of the 300 patients receiving CS-TSE, and their diagnostic consistency was evaluated. The degree of intervertebral foraminal stenosis and nerve root compression was assessed using the T1WI sagittal and T2WI transverse images.

RESULTS

The scan time was reduced from 7 min 28 s to 4 min 26 s with CS-TSE. The median score of nerve root image quality was 5 (p > 0.05). The diagnostic consistency using CS-TSE images between the two radiologists was high for diagnosing lumbar diseases (κ > 0.75) and for evaluating the degree of lumbar foraminal stenosis and nerve root compression (κ = 0.882). No differences between SENSE-TSE and CS-TSE were observed for sensitivity, specificity, positive predictive value, or negative predictive value.

CONCLUSION

CS-TSE has the potential for diagnosing lumbar vertebrae and disc disorders.

A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging: Is Artificial Intelligence-Based Fat-Suppressed Imaging Feasible?

MATERIALS AND METHODS

This single-center study was approved by the institutional review board. Artificial intelligence-based FS MRI scans were created from non-FS images using a deep learning system with a modified convolutional neural network-based U-Net that used a training set of 25,920 images and validation set of 16,416 images. Three musculoskeletal radiologists reviewed 88 knee MR studies in 2 sessions, the original (proton density [PD] + FSPD) and the synthetic (PD + AFSMRI). Readers recorded AFSMRI quality (diagnostic/nondiagnostic) and the presence or absence of meniscal, ligament, and tendon tears; cartilage defects; and bone marrow abnormalities. Contrast-to-noise rate measurements were made among subcutaneous fat, fluid, bone marrow, cartilage, and muscle. The original MRI sequences were used as the reference standard to determine the diagnostic performance of AFSMRI (combined with the original PD sequence). This is a fully balanced study design, where all readers read all images the same number of times, which allowed the determination of the interchangeability of the original and synthetic protocols. Descriptive statistics, intermethod agreement, interobserver concordance, and interchangeability tests were applied. A P value less than 0.01 was considered statistically significant for the likelihood ratio testing, and P value less than 0.05 for all other statistical analyses.

RESULTS

Artificial intelligence-based FS MRI quality was rated as diagnostic (98.9% [87/88] to 100% [88/88], all readers). Diagnostic performance (sensitivity/specificity) of the synthetic protocol was high, for tears of the menisci (91% [71/78], 86% [84/98]), cruciate ligaments (92% [12/13], 98% [160/163]), collateral ligaments (80% [16/20], 100% [156/156]), and tendons (90% [9/10], 100% [166/166]). For cartilage defects and bone marrow abnormalities, the synthetic protocol offered an overall sensitivity/specificity of 77% (170/221)/93% (287/307) and 76% (95/125)/90% (443/491), respectively. Intermethod agreement ranged from moderate to substantial for almost all evaluated structures (menisci, cruciate ligaments, collateral ligaments, and bone marrow abnormalities). No significant difference was observed between methods for all structural abnormalities by all readers (P > 0.05), except for cartilage assessment. Interobserver agreement ranged from moderate to substantial for almost all evaluated structures. Original and synthetic protocols were interchangeable for the diagnosis of all evaluated structures. There was no significant difference for the common exact match proportions for all combinations (P > 0.01). The conspicuity of all tissues assessed through contrast-to-noise rate was higher on AFSMRI than on original FSPD images (P < 0.05).

CONCLUSIONS

Artificial intelligence-based FS MRI (3D AFSMRI) is feasible and offers a method for fast imaging, with similar detection rates for structural abnormalities of the knee, compared with original 3D MR sequences.

Super-Resolution Magnetic Resonance Imaging of the Knee Using 2-Dimensional Turbo Spin Echo Imaging

OBJECTIVES

The purpose of this study was to assess the technical feasibility of 3-dimensional (3D) super-resolution reconstruction (SRR) of 2D turbo spin echo (TSE) knee magnetic resonance imaging (MRI) and to compare its image quality with conventional 3D TSE sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) MRI.

MATERIALS AND METHODS

Super-resolution reconstruction 2D TSE MRI and 3D TSE SPACE images were acquired from a phantom and from the knee of 22 subjects (8 healthy volunteers and 14 patients) using a clinical 3-T scanner. For SRR, 7 anisotropic 2D TSE stacks (voxel size, 0.5 × 0.5 × 2.0 mm; scan time per stack, 1 minute 55 seconds; total scan time, 13 minutes 25 seconds) were acquired with the slice stack rotated around the phase-encoding axis. Super-resolution reconstruction was performed at an isotropic high-resolution grid with a voxel size of 0.5 × 0.5 × 0.5 mm. Direct isotropic 3D image acquisition was performed with the conventional SPACE sequence (voxel size, 0.5 × 0.5 × 0.5 mm; scan time, 12 minutes 42 seconds). For quantitative evaluation, perceptual blur metrics and edge response functions were obtained in the phantom image, and signal-to-noise and contrast-to-noise ratios were measured in the images from the healthy volunteers. Images were qualitatively evaluated by 2 independent radiologists in terms of overall image quality, edge blurring, anatomic visibility, and diagnostic confidence to assess normal and abnormal knee structures. Nonparametric statistical analysis was performed, and significance was defined for P values less than 0.05.

RESULTS

In the phantom, perceptual blur metrics and edge response functions demonstrated a clear improvement in spatial resolution for SRR compared with conventional 3D SPACE. In healthy subjects, signal-to-noise and contrast-to-noise ratios in clinically relevant structures were not significantly different between SRR and 3D SPACE. Super-resolution reconstruction provided better overall image quality and less edge blurring than conventional 3D SPACE, yet the perceived image contrast was better for 3D SPACE. Super-resolution reconstruction received significantly better visibility scores for the menisci, whereas the visibility of cartilage was significantly higher for 3D SPACE. Ligaments had high visibility on both SRR and 3D SPACE images. The diagnostic confidence for assessing menisci was significantly higher for SRR than for conventional 3D SPACE, whereas there were no significant differences between SRR and 3D SPACE for cartilage and ligaments. The interreader agreement for assessing menisci was substantial with 3D SPACE and almost perfect with SRR, and the agreement for assessing cartilage was almost perfect with 3D SPACE and moderate with SRR.

CONCLUSIONS

We demonstrate the technical feasibility of SRR for high-resolution isotropic knee MRI. Our SRR results show superior image quality in terms of edge blurring, but lower image contrast and fluid brightness when compared with conventional 3D SPACE acquisitions. Further contrast optimization and shortening of the acquisition time with state-of-the-art acceleration techniques are necessary for future clinical validation of SRR knee MRI.

Accelerated Isotropic Multiparametric Imaging by High Spatial Resolution 3D-QALAS With Compressed Sensing: A Phantom, Volunteer, and Patient Study

OBJECTIVES

The aims of this study were to develop an accelerated multiparametric magnetic resonance imaging method based on 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) combined with compressed sensing (CS) and to evaluate the effect of CS on the quantitative mapping, tissue segmentation, and quality of synthetic images.

MATERIALS AND METHODS

A magnetic resonance imaging system phantom, containing multiple compartments with standardized T1, T2, and proton density (PD) values; 10 healthy volunteers; and 12 patients with multiple sclerosis were scanned using the 3D-QALAS sequence with and without CS and conventional contrast-weighted imaging. The scan times of 3D-QALAS with and without CS were 5:56 and 11:11, respectively. For healthy volunteers, brain volumetry and myelin estimation were performed based on the measured T1, T2, and PD. For patients with multiple sclerosis, the mean T1, T2, PD, and the amount of myelin in plaques and contralateral normal-appearing white matter (NAWM) were measured. Simple linear regression analysis and Bland-Altman analysis were performed for each metric obtained from the datasets with and without CS. To compare overall image quality and structural delineations on synthetic and conventional contrast-weighted images, case-control randomized reading sessions were performed by 2 neuroradiologists in a blinded manner.

RESULTS

The linearity of both phantom and volunteer measurements in T1, T2, and PD values obtained with and without CS was very strong (R2 = 0.9901-1.000). The tissue segmentation obtained with and without CS also had high linearity (R2 = 0.987-0.999). The quantitative tissue values of the plaques and NAWM obtained with CS showed high linearity with those without CS (R2 = 0.967-1.000). There were no significant differences in overall image quality between synthetic contrast-weighted images obtained with and without CS (P = 0.17-0.99).

CONCLUSIONS

Multiparametric imaging of the whole brain based on 3D-QALAS can be accelerated using CS while preserving tissue quantitative values, tissue segmentation, and quality of synthetic images.

Speeding up the clinical routine: Compressed sensing for 2D imaging of lumbar spine disc herniation

PURPOSE

Increasing economic pressure and patient demands for comfort require an ever-increasing acceleration of scan times without compromising diagnostic certainty. This study tested the new acceleration technique Compressed SENSE (CS-SENSE) as well as different reconstruction methods for the lumbar spine.

METHODS

In this prospective study, 10 volunteers and 14 patients with lumbar disc herniation were scanned using a sagittal 2D T2 turbo spin echo (TSE) sequence applying different acceleration factors of SENSE and CS-SENSE. Gradient echo (GRE), autocalibration (CS-Auto) and TSE prescans were tested for reconstruction. Images were analysed by two readers regarding anatomical delineation, diagnostic certainty (for patients only) and image quality as well as objectively calculating the root mean square error (RMSE), structural similarity index (SSIM), SNR and CNR. The Friedman test and Chi-squared were used for ordinal, ANOVA for repeated measurements and Tukey Kramer test for continuous data. Cohen's kappawas calculated for interreader reliability.

RESULTS

CS-SENSE outperformed SENSE and CS-Auto regarding RMSE (e.g. CS-SENSE 1.5: 43.03 ± 11.64 versus SENSE 1.5: 80.41 ± 17.66; p = 0.0038) and SSIM as well as in the subjective rating for CS-SENSE 3 TSE. In the patient setting image quality was unchanged in all subjective criteria up to CS-SENSE 3 TSE (all p > 0.05) compared to standard T2 with 43 % less scan time while the GRE prescan only allowed a reduction of 32 %.

CONCLUSION

Combining a TSE prescan with CS-SENSE enables significant scan time reductions with unchanged ratings for lumbar spine disc herniation making this superior to the currently used SENSE acceleration or GRE reconstructions.

Five-Minute Five-Sequence Knee MRI Using Combined Simultaneous Multislice and Parallel Imaging Acceleration: Comparison with 10-Minute Parallel Imaging Knee MRI

Background Rapid knee MRI using combined simultaneous multislice (SMS) technique and parallel imaging (PI) acceleration can add value through reduced acquisition time but requires validation of clinical efficacy. Purpose To evaluate the performance of clinical fourfold SMS-PI-accelerated, 5-minute, five-sequence, multicontrast knee MRI protocols compared with standard twofold PI-accelerated, 10-minute knee MRI protocols. Materials and Methods Adults with painful knee conditions were prospectively enrolled from April 2018 to October 2019. Participants underwent fourfold SMS-PI-accelerated, 5-minute, turbo spin-echo (TSE) knee MRI and standard-of-care twofold PI-accelerated, 10-minute, TSE knee MRI at either 1.5 T or 3.0 T. Three radiologists independently evaluated the knee MRI studies for meniscal, tendinous, ligamentous, and osseocartilaginous injuries. Statistical analyses included k-based intermethod agreements and diagnostic performance testing. P < .05 was considered indicative of a statistically significant difference. Results A total of 252 adults were evaluated (mean age ± standard deviation, 47 years ± 17; 134 men). Among the participants, 104 (mean age, 42 years ± 18; 57 women) were in the 1.5-T arm and 148 (mean age, 46 years ± 17; 87 men) were in the 3.0-T arm. Twenty-nine participants (mean age, 38 years ± 12; 15 men) in the 1.5-T arm and 42 (mean age, 41 years ± 16; 24 men) in the 3.0-T arm underwent arthroscopy a mean of 45 days ± 31 and 45 days ± 22 after MRI, respectively. Intermethod agreements were good at 1.5 T (κ >0.71 [95% CI: 0.56, 0.83]) and very good at 3.0 T (κ >0.85 [95% CI: 0.69, 0.96]). The diagnostic performances of corresponding 5-minute and 10-minute MRI protocols were similar for 1.5 T, with areas under the receiver operating characteristic curve (AUCs) greater than 0.78 (95% CI: 0.71, 0.84) (P > .32), and 3.0 T, with AUCs greater than 0.83 (95% CI: 0.78, 0.88) (P > .32). Conclusion Comparisons of 5-minute five-sequence simultaneous multislice- and parallel imaging (PI)-accelerated and 10-minute five-sequence PI-accelerated turbo spin-echo MRI of the knee suggest similar performances at 1.5 and 3.0 T. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Subhas in this issue.

Fast 3D Isotropic Proton Density-Weighted Fat-Saturated MRI of the Knee at 1.5 T with Compressed Sensing: Comparison with Conventional Multiplanar 2D Sequences

PURPOSE

 Compressed sensing (CS) is a method to accelerate MRI acquisition by acquiring less data through undersampling of k-space. In this prospective study we aimed to evaluate whether a three-dimensional (3D) isotropic proton density-weighted fat saturated sequence (PDwFS) with CS can replace conventional multidirectional two-dimensional (2D) sequences at 1.5 Tesla.

MATERIALS AND METHODS

 20 patients (45.2 ± 20.2 years; 10 women) with suspected internal knee damage received a 3D PDwFS with CS acceleration factor 8 (acquisition time: 4:11 min) in addition to standard three-plane 2D PDwFS sequences (acquisition time: 4:05 min + 3:03 min + 4:46 min = 11:54 min) at 1.5 Tesla. Scores for homogeneity of fat saturation, image sharpness, and artifacts were rated by two board-certified radiologists on the basis of 5-point Likert scales. Based on these ratings, an overall image quality score was generated. Additionally, quantitative contrast ratios for the menisci (MEN), the anterior (ACL) and the posterior cruciate ligament (PCL) in comparison with the popliteus muscle were calculated.

RESULTS

 The overall image quality was rated superior in 3D PDwFS compared to 2D PDwFS sequences (14.45 ± 0.83 vs. 12.85 ± 0.99; p < 0.01), particularly due to fewer artifacts (4.65 ± 0.67 vs. 3.65 ± 0.49; p < 0.01) and a more homogeneous fat saturation (4.95 ± 0.22 vs. 4.55 ± 0.51; p < 0.01). Scores for image sharpness were comparable (4.80 ± 0.41 vs. 4.65 ± 0.49; p = 0.30). Quantitative contrast ratios for all measured structures were superior in 3D PDwFS (MEN: p < 0.05; ACL: p = 0.06; PCL: p = 0.33). In one case a meniscal tear was only diagnosed using multiplanar reformation of 3D PDwFS, but it would have been missed on standard multiplanar 2D sequences.

CONCLUSION

 An isotropic fat-saturated 3D PD sequence with CS enables fast and high-quality 3D imaging of the knee joint at 1.5 T and may replace conventional multiplanar 2D sequences. Besides faster image acquisition, the 3D sequence provides advantages in small structure imaging by multiplanar reformation.

KEY POINTS

  · 3D PDwFS with compressed sensing enables knee imaging that is three times faster compared to multiplanar 2D sequences. · 3D PDwFS with compressed sensing provides high-quality knee imaging at 1.5 T. · Isotropic 3D sequences provide advantages in small structure imaging by using multiplanar reformations.

CITATION FORMAT

· Endler CH, Faron A, Isaak A et al. Fast 3D Isotropic Proton Density-Weighted Fat-Saturated MRI of the Knee at 1.5 T with Compressed Sensing: Comparison with Conventional Multiplanar 2D Sequences. Fortschr Röntgenstr 2021; 193: 813 - 821.

Combination of compressed sensing and parallel imaging for T2-weighted imaging of the oral cavity in healthy volunteers: comparison with parallel imaging

OBJECTIVE

Compressed sensing (CS) and parallel imaging (PI) are magnetic resonance (MR) imaging acceleration techniques. Image quality of two-dimensional fast spin echo imaging of the oral cavity using CS or combined CS and PI has not been evaluated. The aim of this study was to compare the acquisition time and image quality between T2-weighted imaging (T2WI) with CS and PI (CSPI-T2WI) and T2WI with PI (PI-T2WI) of the oral cavity.

MATERIALS AND METHODS

Twenty healthy volunteers who underwent CSPI-T2WI and PI-T2WI of the oral cavity on a 3 T MR scanner were enrolled in the study. Contrast ratios of fat/muscle and bone/muscle on CSPI-T2WI and PI-T2WI were measured. Overall image quality, 4 kinds of artifacts, and visualization of 18 anatomical structures were independently evaluated by two radiologists with grading scales. The quantitative and qualitative measurements were compared between CSPI-T2WI and PI-T2WI by using the Wilcoxon signed-rank test.

RESULTS

Mean acquisition time of CSPI-T2WI and PI-T2WI was 72 s and 136 s, respectively (p < .001). CSPI-T2WI showed a significantly higher contrast ratio of fat/muscle than PI-T2WI (p < .01). There were no significant differences in the overall image quality, artifacts, and visualization of anatomical structures between CSPI-T2WI and PI-T2WI.

CONCLUSIONS

CSPI-T2WI of the oral cavity in healthy volunteers can provide a reduction in acquisition time without impaired image quality compared to PI-T2WI.

KEY POINTS

• The acquisition time of T2WI with the combined CS and PI provided a 47% reduction in acquisition time compared with T2WI with PI. • T2WI with the combined CS and PI did not show impaired image quality compared with T2WI with PI. • Combined CS and PI can be a useful technology to evaluate the oral cavity with high-speed acquisition.

[Diagnostic value of a 3D-SPACE-sequence with compressed sensing technology for the knee joint]

BACKROUND

SPACE (3D fast spin echo acquisition) sequences require long scan times for three-dimensional assessment of acute injury of the knee joint and are flawed due to geometric blurring. Their implementation into routine diagnostic imaging was not feasible until recently.

OBJECTIVES

By comparing conventional MRI (magnetic resonance imaging) sequences to 3D (three-dimensional) sequences, it was investigated whether the compressed sensing (CS) technique is inferior to the established 2D sequences with shorter examination times.

MATERIALS AND METHODS

A total of 109 patients (age range 18-50 years) with knee injury were examined by MRI between April 2017 and May 2018. The inter- and intraobserver concordance of two blinded readers were assessed. Consensus was achieved in case of discrepancies. Descriptive analyses of absolute and relative frequency and distribution were tested by Fisher's exact test concerning differences between CS-SPACE and standard proton density fat suppressed imaging.

RESULTS

Interoberserver concordance (IC) of conventional sequences before/after consensus amounted to 58.8/68.1% (medial meniscus, MM), 68.8/88.7% (lateral meniscus, LM) 88.9/97.2% (anterior cruciate ligament, ACL), 99/100% (posterior cruciate ligament, PCL), 88.9/97.2% (collateral ligament, CL) and chondral injury (CI) 1-2: 64.2%, CI-3: 77% and CI-4: 76%. The IC of CS-SPACE amounted before/after consensus of MM to 50.4/77%, LM 68.8/88%, ACL 89.9/94.5%, PCL 97.2/99.0%, CL 92.6/96.3%. IC of CI was evaluated without consensus and amounted to 65.1% (CI 1-2), 66% (CI 3) and 81.6% (CI 4).

CONCLUSIONS

Injuries of ACL, PCL and CL have excellent IC between 3D and 2D sequences. Excellent IC could be found in CI grade 3 and 4 when using 2D sequences and CI grade 4 utilizing CS-SPACE. Our results indicate that CS-SPACE is useful in diagnosing acute knee injuries.

Resolution-dependent influences of compressed sensing in quantitative T2 mapping of articular cartilage

This study evaluates the resolution-dependent influences of compressed sensing (CS) in MRI quantification of T2 mapping in articular cartilage with osteoarthritis (OA). T2-weighed 2D experiments of healthy and OA cartilage were fully sampled in k-space with five echo times at both 17.6 μm and 195.3 μm in-plane resolutions; termed as microscopic MRI (μMRI) and macroscopic MRI (mMRI) respectively. These fully sampled k-space data were under-sampled at various 2D CS accelerating factors (AF = 4-32). The under-sampled data were reconstructed individually into 2D images using nonlinear reconstruction, which were used to calculate the T2 maps. The bulk and zonal variations of T2 values in cartilage were evaluated at different AFs. The study finds that the T2 images at AFs up to 8 preserved major visual information and produced negligible artifacts for μMRI. The T2 values remained accurate for different sub-tissue zones at various AFs. The absolute difference between the CS (AF up to 32) and the Ground Truth (i.e., using 100% of the k-space data) of the mean T2 values through the whole tissue depth was higher in mMRI versus μMRI. For mMRI (where the resolution mimics the clinical MRI of human cartilage), the quantitative T2 mapping at AFs up to 4 showed negligible variations. This study demonstrates that both clinical MRI and μMRI can benefit from the use of CS in image acquisition, and μMRI benefits more from the use of CS by acquiring much less data, without losing significant accuracy in the quantification of T2 maps in osteoarthritic cartilage.

Evaluation of an accelerated 3D modulated flip-angle technique in refocused imaging with an extended echo-train sequence with compressed sensing for imaging of the knee: comparison with routine 2D MRI sequences

AIM

To accelerate the acquisition of high-resolution magnetic resonance imaging (MRI) by using the three-dimensional (3D) matrix sequence with compressed sensing and to compare it with conventional two-dimensional (2D) proton-density (PD) and fast spin-echo (FSE) sequences.

MATERIALS AND METHODS

3D matrix, 2D FSE, and PD sequences were acquired from 68 participants using 3 T magnetic resonance imaging (MRI). Two radiologists scored image quality independently on a four-point scale. The structural similarity index (SSIM), and signal- (SNRs) and contrast-to-noise ratios (CNRs) of different anatomical structures of the knee were assessed and compared between sequences using Wilcoxon signed-rank tests and Cohen's kappa.

RESULTS

The median acquisition time reduction was 44.5%. There was a substantial to perfect agreement for the rating between the 3D matrix FSE and 2D FSE or PD sequences when evaluating cartilage, subchondral bone, and ligaments (κ=0.783-872, p>0.05). The mean SSIM values between the 3D matrix FSE and 2D FSE, and between the 3D matrix PD and 2D PD sequences was 0.994 and 0.971, respectively, which are acceptable. No significant differences were found in SNR between the 3D matrix FSE and 2D FSE, and between the 3D matrix PD and 2D PD sequences, even though the SNR appeared to be higher on routine 2D sequences. The CNR of subchondral bone-meniscus, subchondral bone-joint fluid, and meniscus-joint fluid did not differentiate significantly between the 3D matrix sequence and routine 2D sequences.

CONCLUSIONS

3D matrix reduced the acquisition time in routine clinical knee MRI without the loss in image quality, SNR, and CNR.

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network

We have developed a deep learning-based approach to improve image quality of single-shot turbo spin-echo (SSTSE) images of female pelvis. We aimed to compare the deep learning-based single-shot turbo spin-echo (DL-SSTSE) images of female pelvis with turbo spin-echo (TSE) and conventional SSTSE images in terms of image quality.One hundred five and 21 subjects were used as training and test sets, respectively. We performed 6-fold cross validation. In the training process, low-quality images were generated from TSE images as input. TSE images were used as ground truth images. In the test process, the trained convolutional neural network was applied to SSTSE images. The output images were denoted as DL-SSTSE images. Apart from DL-SSTSE images, classical filtering methods were adopted to SSTSE images. Generated images were denoted as F-SSTSE images. Contrast ratio (CR) of gluteal fat and myometrium and signal-to-noise ratio (SNR) of gluteal fat were measured for all images. Two radiologists graded these images using a 5-point scale and evaluated the image quality with regard to overall image quality, contrast, noise, motion artifact, boundary sharpness of layers in the uterus, and the conspicuity of the ovaries. CRs, SNRs, and image quality scores were compared using the Steel-Dwass multiple comparison tests.CRs and SNRs were significantly higher in DL-SSTSE, F-SSTSE, and TSE images than in SSTSE images. Scores with regard to overall image quality, contrast, noise, and boundary sharpness of layers in the uterus were significantly higher on DL-SSTSE and TSE images than on SSTSE images. There were no significant differences in the CRs, SNRs, and respective scores between DL-SSTSE and TSE images. The score with regard to motion artifacts was significantly higher on DL-SSTSE, F-SSTSE, and SSTSE images than on TSE images. The score with regard to the conspicuity of ovaries was significantly higher on DL-SSTSE images than on F-SSTSE, SSTSE, and TSE images (P < .001).DL-SSTSE images showed higher image quality as compared with SSTSE images. In comparison with conventional TSE images, DL-SSTSE images had acceptable image quality while keeping the advantage of the motion artifact-robustness and acquisition time efficiency in SSTSE imaging.

Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis

Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.

Diagnostic interchangeability of deep convolutional neural networks reconstructed knee MR images: preliminary experience

Background

MRI acceleration using deep learning (DL) convolutional neural networks (CNNs) is a novel technique with great promise. Increasing the number of convolutional layers may allow for more accurate image reconstruction. Studies on evaluating the diagnostic interchangeability of DL reconstructed knee magnetic resonance (MR) images are scarce. The purpose of this study was to develop a deep CNN (DCNN) with an optimal number of layers for accelerating knee magnetic resonance imaging (MRI) acquisition by 6-fold and to test the diagnostic interchangeability and image quality of nonaccelerated images versus images reconstructed with a 15-layer DCNN or 3-layer CNN.

Methods

For the feasibility portion of this study, 10 patients were randomly selected from the Osteoarthritis Initiative (OAI) cohort. For the interchangeability portion of the study, 40 patients were randomly selected from the OAI cohort. Three readers assessed meniscal and anterior cruciate ligament (ACL) tears and cartilage defects using DCNN, CNN, and nonaccelerated images. Image quality was subjectively graded as nondiagnostic, poor, acceptable, or excellent. Interchangeability was tested by comparing the frequency of agreement when readers used both accelerated and nonaccelerated images to frequency of agreement when readers only used nonaccelerated images. A noninferiority margin of 0.10 was used to ensure type I error ≤5% and power ≥80%. A logistic regression model using generalized estimating equations was used to compare proportions; 95% confidence intervals (CIs) were constructed.

Results

DCNN and CNN images were interchangeable with nonaccelerated images for all structures, with excess disagreement values ranging from -2.5% [95% CI: (-6.1, 1.1)] to 3.0% [95% CI: (-0.1, 6.1)]. The quality of DCNN images was graded higher than that of CNN images but less than that of nonaccelerated images [excellent/acceptable quality: DCNN, 95% of cases (114/120); CNN, 60% (72/120); nonaccelerated, 97.5% (117/120)].

Conclusions

Six-fold accelerated knee images reconstructed with a DL technique are diagnostically interchangeable with nonaccelerated images and have acceptable image quality when using a 15-layer CNN.

Improving Quantitative Magnetic Resonance Imaging Using Deep Learning

Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parametric maps, allowing efficient and accurate T2 and T1ρ relaxometry analysis for monitoring and predicting MSK diseases. Deep learning methods have shown promising results for disease detection on quantitative MRI with diagnostic performance superior to conventional machine-learning methods for identifying knee osteoarthritis.

Combined signal averaging and compressed sensing: impact on quality of contrast-enhanced fat-suppressed 3D turbo field-echo imaging for pharyngolaryngeal squamous cell carcinoma

PURPOSE

To determine whether combined signal averaging and compressed sensing (CS averaging) improves the image quality of contrast-enhanced fat-suppressed T1-weighted three-dimensional turbo field-echo (FS T1W 3D-TFE) for evaluation of pharyngolaryngeal squamous cell carcinoma (PLSCC).

METHODS

This retrospective study included 27 patients with PLSCC. In all patients, contrast-enhanced FS T1W 3D-TFE imaging with CS averaging (number of excitations, 7) and that without CS averaging (number of excitations, 1) were obtained during the same acquisition time. Overall image quality, mucosal enhancement, vessel clarity, motion artifact, lesion conspicuity, and lesion edge sharpness were qualitatively evaluated using a 5-point scale. Images with and without CS averaging were compared using the Wilcoxon signed-rank test. Signal-to-noise ratio (SNR) of the lesion and the muscle structure were compared between the two imaging methods using a paired t-test.

RESULTS

Compared with the images without CS averaging, those with CS averaging showed significantly better overall image quality (p = 0.002), mucosal enhancement (p = 0.009), vessel clarity (p = 0.003), muscle edge clarity (p = 0.002), lesion conspicuity (p = 0.002), and lesion edge sharpness (p = 0.001); and less motion artifact (p < 0.001). The SNRs of the lesion and of the muscle structure were significantly higher for images with CS averaging than those without CS averaging (p < 0.001).

CONCLUSION

CS averaging improves the image quality of contrast-enhanced FS T1W 3D-TFE MR images for evaluation of PLSCC without requiring additional acquisition time.

Clinical utility of fat-suppressed 3-dimensional controlled aliasing in parallel imaging results in higher acceleration sampling perfection with application optimized contrast using different flip angle evolutions MRI of the knee in adults

OBJECTIVE

To compare htree-dimensional CAIPIRINHA SPACE and two-dimensional turbo spin echo (2D TSE) MRI in the diagnosis of knee pathology in symptomatic adult patients.

METHODS

From February to September in 2018, 120 patients who underwent a knee MRI using both 3D CAIPIRINHA SPACE and 2D TSE MRI were enrolled. The signal-to-noise ratios (SNRs) and contrast-to-noise ratio (CNR) of the 2D and 3D MRI were compared using a paired t-test. Two radiologists independently evaluated both 2D and 3D MRI images using scoring systems for the menisci, ligaments, and cartilage. Intermethod, inter- and intrareader agreements were determined using an intraclass correlation coefficient (ICC). The diagnostic performance of both methods was measured in 44 patients with arthroscopy.

RESULTS

The mean scan time of 3D CAIPIRINHA SPACE MRI (4' 43") was shorter than that of 2D TSE MRI (17' 27"). The mean SNR and CNR of 3D CAIPIRINHA SPACE was higher than those of 2D TSE MRI (mean difference, 3.97 of SNR and 1.58 of CNR; p < 0.001 and p = .038, respectively). Intermethod (ICC, 0.84-1.0) and inter-reader (ICC, 0.75-0.97), and intra-reader agreements (ICC, 0.87-1.0) were good or excellent. The diagnostic accuracy of 3D CAIPIRINHA SPACE sequence was equal for ligament (95.5%) and better for meniscal and cartilage evaluation (84.1% each), compared to 2D TSE MRI (79.5% each).

CONCLUSION

The fat-suppressed 3D CAIPIRINHA SPACE MRI maybe useful in clinical practice for the evaluation of the knee in place of the 2D conventional MRI protocol.

ADVANCES IN KNOWLEDGE

1. The 3D CAIPIRINHA SPACE MRI of the knee joint may be acceptable to be used in clinical practice showing comparable imaging quality compared to conventional 2D TSE MRI.2. Compared with arthroscopic findings as the gold-standard, the diagnostic performance of 3D CAIPIRINHA SPACE MRI was equal or better for knee joint evaluation than that of 2D TSE MRI, as well as with shorter scan time.

MRI Techniques to Decrease Imaging Times in Children

Long acquisition times can limit the use of MRI in pediatric patients, and the use of sedation or general anesthesia is frequently necessary to facilitate diagnostic examinations. The use of sedation or anesthesia has disadvantages including increased cost and imaging time and potential risks to the patient. Reductions in imaging time may decrease or eliminate the need for sedation or general anesthesia. Over the past decade, a number of imaging techniques that can decrease imaging time have become commercially available. These products have been used increasingly in clinical practice and include parallel imaging, simultaneous multisection imaging, radial k-space acquisition, compressed sensing MRI reconstruction, and automated protocol selection software. The underlying concepts, supporting data, current clinical applications, and available products for each of these strategies are reviewed in this article. In addition, emerging techniques that are still under investigation may provide further reductions in imaging time, including artificial intelligence-based reconstruction, gradient-controlled aliasing sampling and reconstruction, three-dimensional MR spectroscopy, and prospective motion correction. The preliminary results for these techniques are also discussed. ^©RSNA, 2020 See discussion on this article by Greer and Vasanawala.

Compressed Sensing MR Imaging (CS-MRI) of the Knee: Assessment of Quality, Inter-reader Agreement, and Acquisition Time

We compared 3 Tesla (3T) compressed sensing (CS)-MRI of different pulse sequences with various acceleration factors to standard fast spin-echo (FSE) sequences in terms of time, quality, and inter-reader agreement. Each sequence was qualitatively ranked and then qualitatively scored for blurring, artifact, low contrast detection, noise pattern, signal-to-noise ratio, and overall quality. The CS-MRI sequences demonstrated very good overall quality compared with routine FSE sequences with overall good inter-reader agreement.

Compressed sensing MRI of different organs: ready for clinical daily practice?

OBJECTIVES

The aim was to evaluate the image quality and sensitivity to artifacts of compressed sensing (CS) acceleration technique, applied to 3D or breath-hold sequences in different clinical applications from brain to knee.

METHODS

CS with an acceleration from 30 to 60% and conventional MRI sequences were performed in 10 different applications in 107 patients, leading to 120 comparisons. Readers were blinded to the technique for quantitative (contrast-to-noise ratio or functional measurements for cardiac cine) and qualitative (image quality, artifacts, diagnostic findings, and preference) image analyses.

RESULTS

No statistically significant difference in image quality or artifacts was found for each sequence except for the cardiac cine CS for one of both readers and for the wrist 3D proton density (PD)-weighted CS sequence which showed less motion artifacts due to the reduced acquisition time. The contrast-to-noise ratio was lower for the elbow CS sequence but not statistically different in all other applications. Diagnostic findings were similar between conventional and CS sequence for all the comparisons except for four cases where motion artifacts corrupted either the conventional or the CS sequence.

CONCLUSIONS

The evaluated CS sequences are ready to be used in clinical daily practice except for the elbow application which requires a lower acceleration. The CS factor should be tuned for each organ and sequence to obtain good image quality. It leads to 30% to 60% acceleration in the applications evaluated in this study which has a significant impact on clinical workflow.

KEY POINTS

• Clinical implementation of compressed sensing (CS) reduced scan times of at least 30% with only minor penalty in image quality and no change in diagnostic findings. • The CS acceleration factor has to be tuned separately for each organ and sequence to guarantee similar image quality than conventional acquisition. • At least 30% and up to 60% acceleration is feasible in specific sequences in clinical routine.

Targeted rapid knee MRI exam using T2 shuffling

BACKGROUND

MRI is commonly used to evaluate pediatric musculoskeletal pathologies, but same-day/near-term scheduling and short exams remain challenges.

PURPOSE

To investigate the feasibility of a targeted rapid pediatric knee MRI exam, with the goal of reducing cost and enabling same-day MRI access.

STUDY TYPE

A cost effectiveness study done prospectively.

SUBJECTS

Forty-seven pediatric patients.

FIELD STRENGTH/SEQUENCE

3T. The 10-minute protocol was based on T2 Shuffling, a four-dimensional acquisition and reconstruction of images with variable T2 contrast, and a T1 2D fast spin-echo (FSE) sequence. A distributed, compressed sensing-based reconstruction was implemented on a four-node high-performance compute cluster and integrated into the clinical workflow.

ASSESSMENT

In an Institutional Review Board-approved study with informed consent/assent, we implemented a targeted pediatric knee MRI exam for assessing pediatric knee pain. Pediatric patients were subselected for the exam based on insurance plan and clinical indication. Over a 2-year period, 47 subjects were recruited for the study and 49 MRIs were ordered. Date and time information was recorded for MRI referral, registration, and completion. Image quality was assessed from 0 (nondiagnostic) to 5 (outstanding) by two readers, and consensus was subsequently reached.

STATISTICAL TESTS

A Wilcoxon rank-sum test assessed the null hypothesis that the targeted exam times compared with conventional knee exam times were unchanged.

RESULTS

Of the 49 cases, 20 were completed on the same day as exam referral. Median time from registration to exam completion was 18.7 minutes. Median reconstruction time for T2 Shuffling was reduced from 18.9 minutes to 95 seconds using the distributed implementation. Technical fees charged for the targeted exam were one-third that of the routine clinical knee exam. No subject had to return for additional imaging.

DATA CONCLUSION

The targeted knee MRI exam is feasible and reduces the imaging time, cost, and barrier to same-day MRI access for pediatric patients.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.