Parallel MR imaging

DGEDDGAN: A dual-domain generator and edge-enhanced dual discriminator generative adversarial network for MRI reconstruction

Magnetic resonance imaging (MRI) as a critical clinical tool in medical imaging, requires a long scan time for producing high-quality MRI images. To accelerate the speed of MRI while reconstructing high-quality images with sharper edges and fewer aliases, a novel dual-domain generator and edge-enhancement dual discriminator generative adversarial network structure named DGEDDGAN for MRI reconstruction is proposed, in which one discriminator is responsible for holistic image reconstruction, whereas the other is adopted to enhance the edge preservation. A dual-domain U-Net structure that cascades the frequency domain and image domain is designed for the generator. The densely connected residual block is used to replace the traditional U-Net convolution block to improve the feature reuse capability while overcoming the gradient vanishing problem. The coordinate attention mechanism in each skip connection is employed to effectively reduce the loss of spatial information and enforce the feature selection capability. Extensive experiments on two publicly available datasets i.e., IXI dataset and CC-359, demonstrate that the proposed method can reconstruct the high-quality MRI images with more edge details and fewer artifacts, outperforming several state-of-the-art methods under various sampling rates and masks. The time of single-image reconstruction is below 13 ms, which meets the demand of faster processing.

Diffusion-prepared imaging with amplitude navigation for correction of motion-induced signal loss

PURPOSE

Diffusion-prepared imaging is a flexible alternative to conventional spin-echo diffusion-weighted EPI that allows selection of different imaging readouts and k-space traversals, and permits control of image contrast or image artifacts. We investigate a new signal model and reconstruction for diffusion-prepared imaging that addresses signal variations caused by motion-sensitizing diffusion gradients.

METHODS

A signal model, sampling theory, and reconstruction framework were developed assuming that motion-induced phases and the measured signals are random variables. The reconstruction incorporates real-valued amplitude weights estimated from low-resolution images into a linear system. A diffusion-prepared sequence was applied in phantom and in vivo acquisitions using different methods for managing phase errors from eddy currents or motion. This acquisition was performed with a high number of NEX and retrospectively undersampled to analyze errors in ADC estimation, and compared to spin-echo diffusion-weighted EPI, as well as conventional diffusion-prepared methods. The B1 sensitivity of the sequence was investigated using simulation and phantom experiments.

RESULTS

Images reconstructed using the proposed method had similar image structures when compared to conventional spin-echo diffusion-weighted EPI, and demonstrated improved SNR efficiency compared to previous diffusion-prepared approaches. ADC errors followed a trend consistent with the derived signal model, sampling theory, and expected B1 sensitivity. The sampling requirement was shown to depend on the magnitude of motion-induced phases, as well as phases from residual eddy currents.

CONCLUSION

Employing amplitude weights in the reconstruction of a diffusion-prepared sequence can improve SNR efficiency at the cost of a greater minimum sampling time and increased sensitivity to B1 inhomogeneity.

Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI

In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge lies in accurately estimating model parameters from the acquired data due to the inherently low signal-to-noise ratio (SNR) of the signal measurements and the complexity of solving the ill-posed inverse problem. Conventional model fitting approaches treat individual voxels as independent. However, the tissue microenvironment is typically homogeneous in a local environment, where neighboring voxels may contain correlated information. To harness the potential benefits of exploiting correlations among signals in adjacent voxels, this study introduces a novel approach to deep learning parameter estimation that effectively incorporates relevant spatial information. This is achieved by training neural networks on patches of synthetic data encompassing plausible combinations of direct correlations between neighboring voxels. We evaluated the approach on the intravoxel incoherent motion (IVIM) model in DWI. We explored the potential of several deep learning architectures to incorporate spatial information using self-supervised and supervised learning. We assessed performance quantitatively using novel fractal-noise-based synthetic data, which provide ground truths possessing spatial correlations. Additionally, we present results of the approach applied to in vivo DWI data consisting of twelve repetitions from a healthy volunteer. We demonstrate that supervised training on larger patch sizes using attention models leads to substantial performance improvements over both conventional voxelwise model fitting and convolution-based approaches.

Diffusion tensor imaging and diffusion kurtosis imaging of the pancreas - feasibility, robustness and protocol comparison in a healthy population

PURPOSE

This study aims to determine the feasibility, image quality, intra-subject repeatability and inter-reader variability of Diffusion tensor (DTI) and Diffusion kurtosis imaging (DKI) for pancreatic imaging using different protocols and report normative values in healthy individuals.

METHODS

Single-institution prospective study performed on healthy volunteers in a clinical 3T scanner, using two different protocols (6/16 diffusion directions). Acquisitions were repeated twice to assess intra-subject repeatability. To assess inter-reader variability, Mean diffusivity (MD), Axial diffusivity (AD), Radial diffusivity (RD), Apparent diffusion coefficient (ADC) and Mean kurtosis (MK) values were extracted from segmented pancreas by two radiologists. A Likert scale was used by both readers to assess subjective image quality.

RESULTS

Twelve healthy volunteers were recruited for each MRI protocol. The 6 diffusion directions protocol was shorter: 7 min vs. 14 min (corresponding to 4 min vs. 7.5 min for a DTI only reconstruction). No differences in image quality were found between protocols. Only MK maps showed implausible estimates, leading to the exclusion of median 16% and 17.7% pixels for the 6- and 16-direction protocols, respectively. Intra-subject repeatability was determined with negligible coefficients of repeatability for DTI; however, MK presented slightly higher values. Inter-reader agreement was excellent for all maps (ICC > 0.9).

CONCLUSIONS

DTI and DKI of the pancreas are feasible in clinical settings, with excellent inter-observer agreement and good image quality. Intra-subject repeatability is excellent for DTI, but some variability was observed with DKI. A 6-directions protocol may be preferred due to faster acquisition without quantitatively compromising estimates. MK inaccuracies prompt further research for improving artifact correction.

Accelerated Abdominal MRI: A Review of Current Methods and Applications

MRI is widely used for the diagnosis and management of various abdominal diseases involving organs such as the liver, pancreas, and kidneys. However, one major limitation of MRI is its relatively slow imaging speed compared to other modalities. In addition, respiratory motion poses a significant challenge in abdominal MRI, often requiring patients to hold their breath multiple times during an exam. This requirement can be particularly challenging for sick, elderly, and pediatric patients, who may have reduced breath-holding capacity. As a result, rapid imaging plays an important role in routine clinical abdominal MRI exams. Accelerated data acquisition not only reduces overall exam time but also shortens breath-hold durations, thereby improving patient comfort and compliance. Over the past decade, significant advancements in rapid MRI have led to the development of various accelerated imaging techniques for routine clinical use. These methods improve abdominal MRI by enhancing imaging speed, motion compensation, and overall image quality. Integrating these techniques into clinical practice also enables new applications that were previously challenging. This paper provides a concise yet comprehensive overview of rapid imaging techniques applicable to abdominal MRI and discusses their advantages, limitations, and potential clinical applications. By the end of this review, readers are expected to learn the latest advances in accelerated abdominal MRI and explore new frontiers in this evolving field. Evidence Level: N/A Technical Efficacy: Stage 5.

Rosette Spectroscopic Imaging for Whole-Brain Slab Metabolite Mapping at 7T: Acceleration Potential and Reproducibility

Whole-brain proton magnetic resonance spectroscopic imaging (1H-MRSI) is a non-invasive technique for assessing neurochemical distribution in the brain, offering valuable insights into brain functions and neural diseases. It greatly benefits from the improved SNR at ultrahigh field strengths (≥ 7T). However, 1H-MRSI still faces several challenges, such as long acquisition time and severe signal contamination from water and lipids. In this study, 2D and 3D short TR/TE 1H-FID-MRSI sequences using rosette trajectories were developed with nominal spatial resolutions of 4.48 × 4.48 mm2 and 4.48 × 4.48 × 4.50 mm3, respectively. Water signals were suppressed using an optimized Five-variable-Angle-gaussian-pulses-with-ShorT-total-duration (FAST) water suppression scheme of 76 ms, and lipid signals were removed using the L2 regularization method. Metabolic maps of major 1H metabolites were obtained in 5:40 min with 16 averages and 1 average for the 2D and 3D acquisitions, respectively. Excellent intra-session reproducibility was shown, with the coefficients of variance (CV) being lower than 6% for N-Acetyl-L-aspartic acid (NAA), Glutamate (Glu), total Choline (tCho), Creatine and Phosphocreatine (tCr), and Glycine and Myo-inositol (Gly + Ins). To explore the potential of further acceleration, compressed sensing was applied retrospectively to the 3D datasets. The structural similarity index (SSIM) remained above 0.85 and 0.8 until R = 2 and 3 for the metabolite maps of Glu, NAA, tCr, and tCho, indicating the possibility for further reduction of acquisition time to around 2 min.

Reducing the Energy Consumption of Magnetic Resonance Imaging and Computed Tomography Scanners: Integrating Ecodesign and Sustainable Operations

ABSTRACT

This review aims to provide valuable insights into how energy consumption in magnetic resonance imaging (MRI) and computed tomography (CT) scanners can be effectively monitored, managed, and reduced, thereby contributing to more sustainable medical imaging practices. Demand for advanced imaging technologies such as MRI and CT scanners continues to increase, and understanding the resultant impact on greenhouse gas emissions requires a thorough evaluation of their energy consumption. This review examines the energy monitoring and consumption characteristics of MRI and CT scanners, highlighting potential approaches for energy savings. An overview of MRI and CT principles, hardware components, and their associated energy consumption is provided. After addressing the technical aspects, the hardware and software requirements essential for accurate energy metering are detailed. Baseline measurements of energy consumption data are then provided as a foundation to understand current usage patterns and identify areas for improvement. Ongoing efforts to reduce energy consumption are categorized into 3 main strategies: operations, scanner design enhancements, and active scanning techniques, including accelerated MRI protocols. Ultimately, we emphasize that achieving sustainability in medical imaging requires collaboration across disciplines. By incorporating eco-friendly design in new imaging equipment, we can reduce the environmental impact, promote sustainability, and set a health care industry standard for a healthier planet.

Dynamic MRI with Locally Low-Rank Subspace Constraint: Towards 1-Second Temporal Resolution Aided by Deep Learning

MRI is the most effective method for screening high-risk breast cancer patients. While current exams primarily rely on the qualitative evaluation of morphological features before and after contrast administration and less on contrast kinetic information, the latest developments in acquisition protocols aim to combine both. However, balancing between spatial and temporal resolution poses a significant challenge in dynamic MRI. Here, we propose a radial MRI reconstruction framework for Dynamic Contrast Enhanced (DCE) imaging, which offers a joint solution to existing spatial and temporal MRI limitations. It leverages a locally low-rank (LLR) subspace model to represent spatially localized dynamics based on tissue information. Our framework demonstrated substantial improvement in CNR, noise reduction and enables a flexible temporal resolution, ranging from a few seconds to 1-second, aided by a neural network, resulting in images with reduced undersampling penalties. Finally, our reconstruction framework also shows potential benefits for head and neck, and brain MRI applications, making it a viable alternative for a range of DCE-MRI exams.

Self-supervised adversarial diffusion models for fast MRI reconstruction

BACKGROUND

Magnetic resonance imaging (MRI) offers excellent soft tissue contrast essential for diagnosis and treatment, but its long acquisition times can cause patient discomfort and motion artifacts.

PURPOSE

To propose a self-supervised deep learning-based compressed sensing MRI method named "Self-Supervised Adversarial Diffusion for MRI Accelerated Reconstruction (SSAD-MRI)" to accelerate data acquisition without requiring fully sampled datasets.

MATERIALS AND METHODS

We used the fastMRI multi-coil brain axialT 2 $\text{T}_{2}$ -weighted (T 2 $\text{T}_{2}$ -w) dataset from 1376 cases and single-coil brain quantitative magnetization prepared 2 rapid acquisition gradient echoesT 1 $\text{T}_{1}$ maps from 318 cases to train and test our model. Robustness against domain shift was evaluated using two out-of-distribution (OOD) datasets: multi-coil brain axial postcontrastT 1 $\text{T}_{1}$ -weighted (T 1 c $\text{T}_{1}\text{c}$ ) dataset from 50 cases and axial T1-weighted (T1-w) dataset from 50 patients. Data were retrospectively subsampled at acceleration ratesR ∈ { 2 × , 4 × , 8 × } $ R \in \lbrace 2\times, 4\times, 8\times \rbrace $ . SSAD-MRI partitions a random sampling pattern into two disjoint sets, ensuring data consistency during training. We compared our method with ReconFormer Transformer and SS-MRI, assessing performance using normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Statistical tests included one-way analysis of variance and multi-comparison Tukey's honesty significant difference (HSD) tests.

RESULTS

SSAD-MRI preserved fine structures and brain abnormalities visually better than comparative methods atR = 8 × $ R=8\times$ for both multi-coil and single-coil datasets. It achieved the lowest NMSE atR ∈ { 4 × , 8 × } $ R \in \lbrace 4\times, 8\times \rbrace $ , and the highest PSNR and SSIM values at all acceleration rates for the multi-coil dataset. Similar trends were observed for the single-coil dataset, though SSIM values were comparable to ReconFormer atR ∈ { 2 × , 8 × } $ R \in \lbrace 2\times, 8\times \rbrace $ . These results were further confirmed by the voxel-wise correlation scatter plots. OOD results showed significant (p≪ 10 - 5 $ \ll 10^{-5}$ ) improvements in undersampled image quality after reconstruction.

CONCLUSIONS

SSAD-MRI successfully reconstructs fully sampled images without utilizing them in the training step, potentially reducing imaging costs and enhancing image quality crucial for diagnosis and treatment.

Deep Learning Reconstruction Combined With Conventional Acceleration Improves Image Quality of 3 T Brain MRI and Does Not Impact Quantitative Diffusion Metrics

OBJECTIVES

Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resolve Boost (DRB)-based reconstruction techniques of a single-shot echo-planar imaging (ssEPI) diffusion-weighted imaging (DWI) on image quality features in cerebral 3 T brain MRI and compared it with a state-of-the-art DWI sequence.

MATERIALS AND METHODS

In this prospective study, 24 patients received a standard of care ssEPI DWI and 5 additional adapted ssEPI DWI sequences, 3 of those with DRB reconstruction. Qualitative analysis encompassed rating of image quality, noise, sharpness, and artifacts. Quantitative analysis compared apparent diffusion coefficient (ADC) values region-wise between the different DWI sequences. Intraclass correlations, paired sampled t test, Wilcoxon signed rank test, and weighted Cohen κ were used.

RESULTS

Compared with the reference standard, the acquisition time was significantly improved in accelerated DWI from 75 seconds up to 50% (39 seconds; P < 0.001). All tested DRB-reconstructed sequences showed significantly improved image quality, sharpness, and reduced noise (P < 0.001). Highest image quality was observed for the combination of conventional acceleration and DL reconstruction. In singular slices, more artifacts were observed for DRB-reconstructed sequences (P < 0.001). While in general high consistency was found between ADC values, increasing differences in ADC values were noted with increasing acceleration and application of DRB. Falsely pathological ADCs were rarely observed near frontal poles and optic chiasm attributable to susceptibility-related artifacts due to adjacent sinuses.

CONCLUSIONS

In this comparative study, we found that the combination of conventional acceleration and DRB reconstruction improves image quality and enables faster acquisition of ssEPI DWI. Nevertheless, a tradeoff between increased acceleration with risk of stronger artifacts and high-resolution with longer acquisition time needs to be considered, especially for application in cerebral MRI.

Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration

Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, strategies such as parallel imaging have been applied, which utilize multiple receiver coils to speed up the data acquisition process. Additionally, compressed sensing (CS) is a method that facilitates image reconstruction from sparse data, significantly reducing image acquisition time by minimizing the amount of data collection needed. Recently, deep learning (DL) has emerged as a powerful tool for improving MRI reconstruction. It has been integrated with parallel imaging and CS principles to achieve faster and more accurate MRI reconstructions. This review comprehensively examines DL-based techniques for MRI reconstruction. We categorize and discuss various DL-based methods, including end-to-end approaches, unrolled optimization, and federated learning, highlighting their potential benefits. Our systematic review highlights significant contributions and underscores the potential of DL in MRI reconstruction. Additionally, we summarize key results and trends in DL-based MRI reconstruction, including quantitative metrics, the dataset, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based MRI reconstruction in advancing medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based MRI reconstruction publications and public datasets-https://github.com/mosaf/Awesome-DL-based-CS-MRI.

Compressed SENSE acceleration factor influence on magnetic resonance image quality in patients with endometrial cancer

OBJECTIVES

To investigate the impact of compressed sensing - sensitivity encoding (CS-SENSE) acceleration factor on the diagnostic performance of magnetic resonance imaging (MRI) within standard female pelvis protocol in patients with endometrial cancer.

METHODS

T2-weighted turbo spin echo (TSE) sequence from standard female pelvic MRI protocol was chosen due to its long acquisition time and essential role in the evaluation of morphological characteristics of the female pelvic anatomical structures. Fully sampled reference scans and multiple prospectively 2x to 5x under-sampled CS-SENSE scans were acquired. Retrospectively, under-sampled scans were compared to fully sampled scans and visually assessed for image quality and diagnostic quality by two independent radiologists dedicated to urogenital imaging with different experience levels.

RESULTS

Images obtained with CS-SENSE accelerated acquisition were of diagnostically acceptable quality at up to 3x acceleration for T2 TSE in both axial and sagittal planes (with an acquisition time reduction of 64 %). Among all evaluated uterine structures, the junctional zone proved to be most sensitive to the influence of the acceleration factor. Statistical analysis showed statistically significant differences between image interpretation qualities between two radiologists of different experience levels (p < 0.05).

CONCLUSION

CS-SENSE accelerated T2 TSE sequence of the female pelvis shows image quality similar to that of conventional acquisitions with reduced acquisition time. CS-SENSE can moderately reduce scan time, providing many benefits without losing the image quality.

Acquisition Acceleration of Ultra-Low Field MRI With Parallel Imaging and Compressed Sensing in Microtesla Fields

OBJECTIVE

In recent years, ultra-low field (ULF) magnetic resonance imaging (MRI) has gained widespread attention due to its advantages, such as low cost, light weight, and portability. However, the low signal-to-noise ratio (SNR) leads to a long scan time. Herein, we study the acceleration performance of parallel imaging (PI) and compressed sensing (CS) in different k-space sampling strategies at 0.12 mT.

METHODS

This study employs phantoms to assess the efficiency of acceleration methods at ULF MRI, in which signals are detected by ultra-sensitive superconducting quantum interference devices (SQUIDs). We compare the performance of fast Fourier transform (FFT), generalized auto-calibrating partially parallel acquisitions (GRAPPA), and eigenvector-based SPIRiT (ESPIRiT) in Cartesian sampling, while also evaluating non-uniform FFT (NUFFT), GRAPPA operator gridding, and ESPIRiT in non-Cartesian sampling. We design a resolution phantom to investigate the effectiveness of these methods in maintaining image resolution.

RESULTS

In Cartesian sampling, GRAPPA and ESPIRiT jointly regularized by total variation and ℓ1-norm (TVJℓ1-ESPIRiT) methods reconstructed good-quality phantom images with an acceleration factor of R = 2. In contrast, TVJℓ1-ESPIRiT exhibited improved image quality and much less signal loss even for R = 4. In radial sampling, TVJℓ1-ESPIRiT reduced the acquisition time to 1.69 minutes at R = 4, with a respective improvement of 12.26 dB in peak SNR compared to NUFFT. The resolution phantom imaging showed that the reconstructions by PI and CS maintained the original resolution of 2 mm.

CONCLUSION AND SIGNIFICANCE

This study improves the practicality of ULF MRI at microtesla fields by implementing imaging acceleration with PI and CS in different k-space sampling.

The kinetic parameters of dynamic contrast-enhanced MRI with ultrafast imaging in breast cancer patients receiving neoadjuvant chemotherapy: Prediction of pathologic complete response and correlation with histologic microvessel density

Early prediction of pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients can help forecast prognosis and guide decisions on adjuvant therapy. This study aimed to determine whether the kinetic parameters of dynamic contrast-enhanced MRI (DCE-MRI) with ultrafast imaging can predict pCR following NAC in breast cancer patients and whether these parameters are correlated with histologic microvessel density (MVD). In this retrospective study, 61 breast cancer patients who underwent NAC and surgery between August 2020 and 2022 were analyzed. Ultrafast and conventional DCE-MRI features, along with pathologic results, were compared between the pCR and non-pCR groups. Regression analysis was conducted to identify predictive factors for pCR. Additionally, MRI kinetic parameters were correlated with histologic MVD. Of the 61 patients, 17 (27.9%) achieved pCR. The pCR group exhibited a larger delayed washout component (P = .002) and a smaller angiovolume (P = .02) compared to the non-pCR group; however, these factors lost significance when accounting for tumor size, lymph node status, and molecular subtypes. In a subgroup analysis based on molecular subtype, a low initial enhancement value (≤362.5%) and angiovolume (≤10.3 cc) predicted pCR in human epidermal growth factor receptor 2-enriched breast cancer, with an area under the curve of 0.833. The maximum slope on ultrafast MRI was higher in the high MVD group compared to the low MVD group (P = .049). Human epidermal growth factor receptor 2-enriched breast cancer with low vascularity on DCE-MRI is more likely to achieve pCR, although MRI kinetic parameters were not independent predictors of pCR in all breast cancer subtypes. The maximum slope on ultrafast MRI was the only kinetic parameter that correlated with histologic MVD. Larger studies focused on molecular subtypes are warranted.

Clinical Evaluation of 3D Motion-Correction Via Scout Accelerated Motion Estimation and Reduction Framework Versus Conventional T1-Weighted MRI at 1.5 T in Brain Imaging

OBJECTIVES

The aim of this study was to investigate the occurrence of motion artifacts and image quality of brain magnetic resonance imaging (MRI) T1-weighted imaging applying 3D motion correction via the Scout Accelerated Motion Estimation and Reduction (SAMER) framework compared with conventional T1-weighted imaging at 1.5 T.

MATERIALS AND METHODS

A preliminary study involving 14 healthy volunteers assessed the impact of the SAMER framework on induced motion during 3 T MRI scans. Participants performed 3 different motion patterns: (1) step up, (2) controlled breathing, and (3) free motion. The patient study included 82 patients who required clinically indicated MRI scans. 3D T1-weighted images (MPRAGE) were acquired at 1.5 T. The MRI data were reconstructed using either regular product reconstruction (non-Moco) or the 3D motion correction SAMER framework (SAMER Moco), resulting in 145 image sequences. For the preliminary and the patient study, 3 experienced radiologists evaluated the image data using a 5-point Likert scale, focusing on overall image quality, artifact presence, diagnostic confidence, delineation of pathology, and image sharpness. Interrater agreement was assessed using Gwet's AC2, and an exploratory analysis (non-Moco vs SAMER Moco) was performed.

RESULTS

Compared with non-Moco, the preliminary study demonstrated significant improvements across all imaging parameters and motion patterns with SAMER Moco (P < 0.001). Odds ratios favoring SAMER Moco were >999.999 for freedom of artifact and overall image quality (P < 0.0001). Excellent or good ratings for freedom of artifact were 52.4% with SAMER Moco, compared with 21.4% for non-Moco. Similarly, 66.7% of SAMER Moco images were rated excellent or good for overall image quality versus 21.4% for non-Moco. Multireader interrater agreement was excellent across all parameters.The patient study confirmed that SAMER Moco provided significantly superior image quality across all evaluated imaging parameters, particularly in the presence of motion (P < 0.001). Diagnostic confidence was rated as excellent or good in 95.1% of SAMER Moco cases, compared with 78.1% for non-Moco cases. Similarly, overall image quality was rated as excellent or good in 89.8% of SAMER Moco cases versus 65.9% for non-Moco cases. The odds ratios for diagnostic confidence and for overall image quality were 6.698 and 6.030, respectively, both favoring SAMER Moco (P < 0.0001). Multireader interrater agreement was excellent across all parameters.

CONCLUSIONS

The application of SAMER in T1-weighted imaging datasets is feasible in clinical routine and significantly increases image quality and diagnostic confidence in 1.5 T brain MRI by effectively reducing motion artifacts.

Audiovisual Breathing Guidance for Improved Image Quality and Scan Efficiency of T2- and Diffusion-Weighted Liver MRI

OBJECTIVES

Impaired image quality and long scan times frequently occur in respiratory-triggered sequences in liver magnetic resonance imaging (MRI). We evaluated the impact of an in-bore active breathing guidance (BG) application on image quality and scan time of respiratory-triggered T2-weighted (T2) and diffusion-weighted imaging (DWI) by comparing sequences with standard triggering (T2S and DWIS) and with BG (T2BG and DWIBG).

MATERIALS AND METHODS

In this prospective study, random patients with clinical indications for liver MRI underwent 3 T MRI with standard and BG acquisitions. The audiovisual BG application received the respiratory signal from the scanner, and animated breathing instructions were displayed using a mirror and screen behind the MRI bore. Prior to the DWIBG and T2BG acquisition, patients received a short video instruction about MRI with BG. Suitable parameters for desired breathing pattern for T2BG and DWIBG were set individually for each patient based on the patient's physical respiratory ability (ie, 4 seconds breathing followed by 4.5 seconds breath holding). Artifacts, sharpness, lesion conspicuity, and overall image quality were assessed using a Likert scale from 1 (nondiagnostic) to 5 (excellent). Scan time, apparent contrast-to-noise ratio, and apparent signal-to-noise ratio (aSNR) for all sequences were analyzed. Paired t test and Wilcoxon test were used for statistical analysis.

RESULTS

Thirty-two patients (mean age: 55 ± 13 years, 13 female) were included. T2BG showed less artifacts (4.5 ± 0.7 vs 4.1 ± 0.8, P < 0.001) and better sharpness, lesion conspicuity, and overall image quality (eg, overall image quality 4.6 ± 0.7 vs 4.4 ± 0.7, P = 0.004) compared with T2S. DWIBG demonstrated improved image quality in all categories compared with DWIS (eg, overall image quality 4.5 ± 0.5 vs 4.3 ± 0.5, P = 0.005) and less artifacts (4.1 ± 0.5 vs 3.8 ± 0.7, P = 0.007). Scan times of T2BG (286 ± 23 vs 345 ± 68 seconds, P < 0.001) and DWIBG (160 ± 4 vs 252 ± 70 seconds, P < 0.001) were reduced by 17% and 37%, respectively. aSNR and apparent contrast-to-noise ratio (eg, aSNR: 23.45 ± 11.31 [T2BG] vs 25.84 ± 10.76 [T2S]; P = 0.079) were similar for both sequences for both approaches.

CONCLUSIONS

Active BG for respiratory-triggered liver T2w and DWI sequences led to significant reduction of breathing artifacts, improved image quality, and shorter scan time compared with standard acquisitions.

Accelerated High-resolution T1- and T2-weighted Breast MRI with Deep Learning Super-resolution Reconstruction

RATIONALE AND OBJECTIVES

To assess the performance of an industry-developed deep learning (DL) algorithm to reconstruct low-resolution Cartesian T1-weighted dynamic contrast-enhanced (T1w) and T2-weighted turbo-spin-echo (T2w) sequences and compare them to standard sequences.

MATERIALS AND METHODS

Female patients with indications for breast MRI were included in this prospective study. The study protocol at 1.5 Tesla MRI included T1w and T2w. Both sequences were acquired in standard resolution (T1S and T2S) and in low-resolution with following DL reconstructions (T1DL and T2DL). For DL reconstruction, two convolutional networks were used: (1) Adaptive-CS-Net for denoising with compressed sensing, and (2) Precise-Image-Net for resolution upscaling of previously downscaled images. Overall image quality was assessed using 5-point-Likert scale (from 1=non-diagnostic to 5=excellent). Apparent signal-to-noise (aSNR) and contrast-to-noise (aCNR) ratios were calculated. Breast Imaging Reporting and Data System (BI-RADS) agreement between different sequence types was assessed.

RESULTS

A total of 47 patients were included (mean age, 58±11 years). Acquisition time for T1DL and T2DL were reduced by 51% (44 vs. 90 s per dynamic phase) and 46% (102 vs. 192 s), respectively. T1DL and T2DL showed higher overall image quality (e.g., 4 [IQR, 4-4] for T1S vs. 5 [IQR, 5-5] for T1DL, P<0.001). Both, T1DL and T2DL revealed higher aSNR and aCNR than T1S and T2S (e.g., aSNR: 32.35±10.23 for T2S vs. 27.88±6.86 for T2DL, P=0.014). Cohen k agreement by BI-RADS assessment was excellent (0.962, P<0.001).

CONCLUSION

DL for denoising and resolution upscaling reduces acquisition time and improves image quality for T1w and T2w breast MRI.

A roadmap towards standardized neuroimaging approaches for human thalamic nuclei

The thalamus has a key role in mediating cortical-subcortical interactions but is often neglected in neuroimaging studies, which mostly focus on changes in cortical structure and activity. One of the main reasons for the thalamus being overlooked is that the delineation of individual thalamic nuclei via neuroimaging remains controversial. Indeed, neuroimaging atlases vary substantially regarding which thalamic nuclei are included and how their delineations were established. Here, we review current and emerging methods for thalamic nuclei segmentation in neuroimaging data and consider the limitations of existing techniques in terms of their research and clinical applicability. We address these challenges by proposing a roadmap to improve thalamic nuclei segmentation in human neuroimaging and, in turn, harmonize research approaches and advance clinical applications. We believe that a collective effort is required to achieve this. We hope that this will ultimately lead to the thalamic nuclei being regarded as key brain regions in their own right and not (as often currently assumed) as simply a gateway between cortical and subcortical regions.

Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction

Rationale and Objectives: Diagnostic accuracy and therapeutic decision-making for IDH-mutant gliomas in tumor board reviews are based on MRI and multidisciplinary interactions.

Materials and Methods

This study explores the feasibility of deep learning-based reconstruction (DLR) in MRI for IDH-mutant gliomas. The research utilizes a multidisciplinary approach, engaging neuroradiologists, neurosurgeons, neuro-oncologists, and radiotherapists to evaluate qualitative aspects of DLR and conventional reconstructed (CR) sequences. Furthermore, quantitative image quality and tumor volumes according to Response Assessment in Neuro-Oncology (RANO) 2.0 standards were assessed.

Results

All DLR sequences consistently outperformed CR sequences (median of 4 for all) in qualitative image quality across all raters (p < 0.001 for all) and revealed higher SNR and CNR values (p < 0.001 for all). Preference for all DLR over CR was overwhelming, with ratings of 84 % from the neuroradiologist, 100 % from the neurosurgeon, 92 % from the neuro-oncologist, and 84 % from the radiation oncologist. The RANO 2.0 compliant measurements showed no significant difference between the CR and DRL sequences (p = 0.142).

Conclusion

This study demonstrates the clinical feasibility of DLR in MR imaging of IDH-mutant gliomas, with significant time savings of 29.6 % on average and non-inferior image quality to CR. DLR sequences received strong multidisciplinary preference, underscoring their potential for enhancing neuro-oncological decision-making and suitability for clinical implementation.

FEFA: Frequency Enhanced Multi-Modal MRI Reconstruction With Deep Feature Alignment

Integrating complementary information from multiple magnetic resonance imaging (MRI) modalities is often necessary to make accurate and reliable diagnostic decisions. However, the different acquisition speeds of these modalities mean that obtaining information can be time consuming and require significant effort. Reference-based MRI reconstruction aims to accelerate slower, under-sampled imaging modalities, such as T2-modality, by utilizing redundant information from faster, fully sampled modalities, such as T1-modality. Unfortunately, spatial misalignment between different modalities often negatively impacts the final results. To address this issue, we propose FEFA, which consists of cascading FEFA blocks. The FEFA block first aligns and fuses the two modalities at the feature level. The combined features are then filtered in the frequency domain to enhance the important features while simultaneously suppressing the less essential ones, thereby ensuring accurate reconstruction. Furthermore, we emphasize the advantages of combining the reconstruction results from multiple cascaded blocks, which also contributes to stabilizing the training process. Compared to existing registration-then-reconstruction and cross-attention-based approaches, our method is end-to-end trainable without requiring additional supervision, extensive parameters, or heavy computation. Experiments on the public fastMRI, IXI and in-house datasets demonstrate that our approach is effective across various under-sampling patterns and ratios.

Preliminary Exploration of T1ρ and T2 Mapping in Porcine Articular Cartilage Using Very-Low-Field Magnetic Resonance Imaging

OBJECTIVE

The high prevalence of osteoarthritis emphasizes the need for a cost-effective and accessible method for its early diagnosis. Recently, the portability and affordability of very-low-field (VLF) magnetic resonance imaging (MRI, 10-100 mT) have caused it to gain popularity. Nevertheless, there is insufficient evidence to quantify early degenerative changes in cartilage using VLF MRI. This study assessed the potential of T1ρ and T2 mapping for detecting degenerative changes in porcine cartilage specimens using a 50 mT MRI scanner.

METHODS

T2- and T1ρ-weighted images were acquired using a 50 mT MRI scanner with 2D spin-echo and triple-refocused T1ρ preparation sequences. MRI scans of porcine cartilage were also acquired using a 3 T MRI scanner for comparison. A mono-exponential algorithm was applied to fit a series of T2- and T1ρ-weighted images. T2 values for CuSO4·5H2O solutions measured via Carr-Purcell-Meiboom-Gill (CPMG) and spin-echo sequences were compared to verify the algorithm's reliability. The nonparametric Kruskal-Wallis statistical test was used to compare T2 and T1ρ values. Experimental repeatability was assessed using the root-mean-square of the coefficient of variation (rmsCV).

RESULTS

T2 values of the CuSO4·5H2O solutions obtained using the spin-echo sequence showed differences within 2.3% of those obtained using the CPMG sequence, indicating the algorithm's reliability. The T1ρ values for varying concentrations of agarose gel solutions were higher than the T2 values. Furthermore, 50 mT and 3 T MRI results showed that both the T1ρ and T2 values were significantly higher for porcine cartilage degraded for 6 h vs intact cartilage, with p-values of 0.006 and 0.01, respectively. Our experimental results showed good reproducibility (rmsCV < 8%).

CONCLUSION

We demonstrated the feasibility of quantitative cartilage imaging via T2 and T1ρ mapping at 50 mT MRI for the first time.

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.

Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features

OBJECTIVE

This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques.

METHODS

Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses.

RESULTS

According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues ( P < 0.001).

CONCLUSIONS

MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.

Application of deep learning reconstruction in abdominal magnetic resonance cholangiopancreatography for image quality improvement and acquisition time reduction

PURPOSE

To compare deep learning (DL)-based and conventional reconstruction through subjective and objective analysis and ascertain whether DL-based reconstruction improves the quality and acquisition speed of clinical abdominal magnetic resonance imaging (MRI).

METHODS

The 124 patients who underwent abdominal MRI between January and July 2021 were retrospectively studied. For each patient, two-dimensional axial T2-weighted single-shot fast spin-echo MRI images with or without fat saturation were reconstructed using DL-based and conventional methods. The subjective image quality scores and objective metrics, including signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images were analysed. An explorative analysis was performed to compare 20 patients' MRI images with site routine settings, high-resolution settings and high-speed settings. Paired t tests and Wilcoxon signed-rank tests were used for subjective and objective comparisons.

RESULTS

A total of 144 patients were evaluated (mean age, 62.2 ± 14.1 years; 83 men). The MRI images reconstructed using DL-based methods had higher SNRs and CNRs than did those reconstructed using conventional methods (all p < 0.01). The subjective scores of the images reconstructed using DL-based methods were higher than those of the images reconstructed using conventional methods (p < 0.01), with significantly lower variation (p < 0.01). Exploratory analysis revealed that the DL-based reconstructions with thin slice thickness and higher temporal resolution had the highest image quality and were associated with the shortest scan times.

CONCLUSIONS

DL-based reconstruction methods can be used to improve the quality with higher stability and accelerate the acquisition of abdominal MRI.

Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded," images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.

Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction

Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play (PnP) is a general framework that uses denoising algorithms as the prior or regularizer. Recent work showed that PnP methods with denoisers based on pretrained convolutional neural networks outperform other classical regularizers in CS MRI reconstruction. However, the numerical solvers for PnP can be slow for CS MRI reconstruction. This paper proposes a preconditioned PnPP 2 nP method to accelerate the convergence speed. Moreover, we provide proofs of the fixed-point convergence of theP 2 nP iterates. Numerical experiments on CS MRI reconstruction with non-Cartesian sampling trajectories illustrate the effectiveness and efficiency of theP 2 nP approach.

Enhanced image quality and lesion detection in FLAIR MRI of white matter hyperintensity through deep learning-based reconstruction

OBJECTIVE

To delve deeper into the study of degenerative diseases, it becomes imperative to investigate whether deep-learning reconstruction (DLR) can improve the evaluation of white matter hyperintensity (WMH) on 3.0T scanners, and compare its lesion detection capabilities with conventional reconstruction (CR).

METHODS

A total of 131 participants (mean age, 46 years ±17; 46 men) were included in the study. The images of these participants were evaluated by readers blinded to clinical data. Two readers independently assessed subjective image indicators on a 4-point scale. The severity of WMH was assessed by four raters using the Fazekas scale. To evaluate the relative detection capabilities of each method, we employed the Wilcoxon signed rank test to compare scores between the DLR and the CR group. Additionally, we assessed interrater reliability using weighted k statistics and intraclass correlation coefficient to test consistency among the raters.

RESULTS

In terms of subjective image scoring, the DLR group exhibited significantly better scores compared to the CR group (P < 0.001). Regarding the severity of WMH, the DL group demonstrated superior performance in detecting lesions. Majority readers agreed that the DL group provided clearer visualization of the lesions compared to the conventional group.

CONCLUSION

DLR exhibits notable advantages over CR, including subjective image quality, lesion detection sensitivity, and inter reader reliability.

Dynamic MRI interpolation in temporal direction using an unsupervised generative model

PURPOSE

Cardiac cine magnetic resonance imaging (MRI) is an important tool in assessing dynamic heart function. However, this technique requires long acquisition time and long breath holds, which presents difficulties. The aim of this study is to propose an unsupervised neural network framework that can perform cardiac cine interpolation in time, so that we can increase the temporal resolution of cardiac cine without increasing acquisition time.

METHODS

In this study, a subject-specific unsupervised generative neural network is designed to perform temporal interpolation for cardiac cine MRI. The network takes in a 2D latent vector in which each element corresponds to one cardiac phase in the cardiac cycle and then the network outputs the cardiac cine images which are acquired on the scanner. After the training of the generative network, we can interpolate the 2D latent vector and input the interpolated latent vector into the network and the network will output the frame-interpolated cine images. The results of the proposed cine interpolation neural network (CINN) framework are compared quantitatively and qualitatively with other state-of-the-art methods, the ground truth training cine frames, and the ground truth frames removed from the original acquisition. Signal-to-noise ratio (SNR), structural similarity index measures (SSIM), peak signal-to-noise ratio (PSNR), strain analysis, as well as the sharpness calculated using the Tenengrad algorithm were used for image quality assessment.

RESULTS

As shown quantitatively and qualitatively, the proposed framework learns the generative task well and hence performs the temporal interpolation task well. Furthermore, both quantitative and qualitative comparison studies show the effectiveness of the proposed framework in cardiac cine interpolation in time.

CONCLUSION

The proposed generative model can effectively learn the generative task and perform high quality cardiac cine interpolation in time.

Effect of MR head coil geometry on deep-learning-based MR image reconstruction

PURPOSE

To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method.

THEORY AND METHODS

Traditional and DL-based MR image reconstruction approaches operate in fundamentally different ways: Traditional methods solve a system of equations derived from the image data whereas DL methods use data/target pairs to learn a generalizable reconstruction model. Two sets of head coil profiles were evaluated: (1) 8-channel and (2) 32-channel geometries. A DL model was compared to conjugate gradient SENSE (CG-SENSE) and L1-wavelet compressed sensing (CS) through quantitative metrics and visual assessment as coil overlap was increased.

RESULTS

Results were generally consistent between experiments. As coil overlap increased, there was a significant (p < 0.001) decrease in performance in most cases for all methods. The decrease was most pronounced for CG-SENSE, and the DL models significantly outperformed (p < 0.001) their non-DL counterparts in all scenarios. CS showed improved robustness to coil overlap and signal-to-noise ratio (SNR) versus CG-SENSE, but had quantitatively and visually poorer reconstructions characterized by blurriness as compared to DL. DL showed virtually no change in performance across SNR and very small changes across coil overlap.

CONCLUSION

The DL image reconstruction method produced images that were robust to coil overlap and of higher quality than CG-SENSE and CS. This suggests that geometric coil design constraints can be relaxed when using DL reconstruction methods.

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.

Accelerated 3D whole-heart non-contrast-enhanced mDIXON coronary MR angiography using deep learning-constrained compressed sensing reconstruction

OBJECTIVES

To investigate the feasibility of a deep learning-constrained compressed sensing (DL-CS) method in non-contrast-enhanced modified DIXON (mDIXON) coronary magnetic resonance angiography (MRA) and compare its diagnostic accuracy using coronary CT angiography (CCTA) as a reference standard.

METHODS

Ninety-nine participants were prospectively recruited for this study. Thirty healthy subjects (age range: 20-65 years; 50% female) underwent three non-contrast mDIXON-based coronary MRA sequences including DL-CS, CS, and conventional sequences. The three groups were compared based on the scan time, subjective image quality score, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The remaining 69 patients suspected of coronary artery disease (CAD) (age range: 39-83 years; 51% female) underwent the DL-CS coronary MRA and its diagnostic performance was compared with that of CCTA.

RESULTS

The scan time for the DL-CS and CS sequences was notably shorter than that of the conventional sequence (9.6 ± 3.1 min vs 10.0 ± 3.4 min vs 13.0 ± 4.9 min; p < 0.001). The DL-CS sequence obtained the highest image quality score, mean SNR, and CNR compared to CS and conventional methods (all p < 0.001). Compared to CCTA, the accuracy, sensitivity, and specificity of DL-CS mDIXON coronary MRA per patient were 84.1%, 92.0%, and 79.5%; those per vessel were 90.3%, 82.6%, and 92.5%; and those per segment were 98.0%, 85.1%, and 98.0%, respectively.

CONCLUSION

The DL-CS mDIXON coronary MRA provided superior image quality and short scan time for visualizing coronary arteries in healthy individuals and demonstrated high diagnostic value compared to CCTA in CAD patients.

CRITICAL RELEVANCE STATEMENT

DL-CS resulted in improved image quality with an acceptable scan time, and demonstrated excellent diagnostic performance compared to CCTA, which could be an alternative to enhance the workflow of coronary MRA.

KEY POINTS

Current coronary MRA techniques are limited by scan time and the need for noise reduction. DL-CS reduced the scan time in coronary MR angiography. Deep learning achieved the highest image quality among the three methods. Deep learning-based coronary MR angiography demonstrated high performance compared to CT angiography.

Deep learning reconstructed T2-weighted Dixon imaging of the spine: Impact on acquisition time and image quality

PURPOSE

To assess the image quality and impact on acquisition time of a novel deep learning based T2 Dixon sequence (T2DL) of the spine.

METHODS

This prospective, single center study included n = 44 consecutive patients with a clinical indication for lumbar MRI at our university radiology department between September 2022 and March 2023. MRI examinations were performed on 1.5-T and 3-T scanners (MAGNETOM Aera and Vida; Siemens Healthineers, Erlangen, Germany) using dedicated spine coils. The MR study protocol consisted of our standard clinical protocol, including a T2 weighted standard Dixon sequence (T2std) and an additional T2DL acquisition. The latter used a conventional sampling pattern with a higher parallel acceleration factor. The individual contrasts acquired for Dixon water-fat separation were then reconstructed using a dedicated research application. After reconstruction of the contrast images from k-space data, a conventional water-fat separation was performed to provide derived water images. Two readers with 6 and 4 years of experience in interpreting MSK imaging, respectively, analyzed the images in a randomized fashion. Regarding overall image quality, banding artifacts, artifacts, sharpness, noise, and diagnostic confidence were analyzed using a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent image quality). Statistical analyses included the Wilcoxon signed-rank test and weighted Cohen's kappa statistics.

RESULTS

Forty-four patients (mean age 53 years (±18), male sex: 39 %) were prospectively included. Thirty-one examinations were performed on 1.5 T and 13 examinations on 3 T scanners. A sequence was successfully acquired in all patients. The total acquisition time of T2DL was 93 s at 1.5-T and 86 s at 3-T, compared to 235 s, and 257 s, respectively for T2std (reduction of acquisition time: 60.4 % at 1.5-T, and 66.5 % at 3-T; p < 0.01). Overall image quality was rated equal for both sequences (median T2DL: 5[3 -5], and median T2std: 5 [2 -5]; p = 0.57). T2DL showed significantly reduced noise levels compared to T2std (5 [4 -5] versus 4 [3 -4]; p < 0.001). In addition, sharpness was rated to be significantly higher in T2DL (5 [4 -5] versus 4 [3 -5]; p < 0.001). Although T2DL displayed significantly more banding artifacts (5 [2 -5] versus 5 [4 -5]; p < 0.001), no significant impact on readers diagnostic confidence between sequences was noted (T2std: 5 [2 -5], and T2DL: 5 [3 -5]; p = 0.61). Substantial inter-reader and intrareader agreement was observed for T2DL overall image quality (κ: 0.77, and κ: 0.8, respectively).

CONCLUSION

T2DL is feasible, yields an image quality comparable to the reference standard while substantially reducing the acquisition time.

DIRECTION: Deep cascaded reconstruction residual-based feature modulation network for fast MRI reconstruction

Deep cascaded networks have been extensively studied and applied to accelerate Magnetic Resonance Imaging (MRI) and have shown promising results. Most existing works employ a large cascading number for the sake of superior performances. However, due to the lack of proper guidance, the reconstruction performance can easily reach a plateau and even face degradation if simply increasing the cascading number. In this paper, we aim to boost the reconstruction performance from a novel perspective by proposing a parallel architecture called DIRECTION that fully exploits the guiding value of the reconstruction residual of each subnetwork. Specifically, we introduce a novel Reconstruction Residual-Based Feature Modulation Mechanism (RRFMM) which utilizes the reconstruction residual of the previous subnetwork to guide the next subnetwork at the feature level. To achieve this, a Residual Attention Modulation Block (RAMB) is proposed to generate attention maps using multi-scale residual features to modulate the image features of the corresponding scales. Equipped with this strategy, each subnetwork within the cascaded network possesses its unique optimization objective and emphasis rather than blindly updating its parameters. To further boost the performance, we introduce the Cross-Stage Feature Reuse Connection (CSFRC) and the Reconstruction Dense Connection (RDC), which can reduce information loss and enhance representative ability. We conduct sufficient experiments and evaluate our method on the fastMRI knee dataset using multiple subsampling masks. Comprehensive experimental results show that our method can markedly boost the performance of cascaded networks and significantly outperforms other compared state-of-the-art methods quantitatively and qualitatively.

Modern acceleration in musculoskeletal MRI: applications, implications, and challenges

Magnetic resonance imaging (MRI) is crucial for accurately diagnosing a wide spectrum of musculoskeletal conditions due to its superior soft tissue contrast resolution. However, the long acquisition times of traditional two-dimensional (2D) and three-dimensional (3D) fast and turbo spin-echo (TSE) pulse sequences can limit patient access and comfort. Recent technical advancements have introduced acceleration techniques that significantly reduce MRI times for musculoskeletal examinations. Key acceleration methods include parallel imaging (PI), simultaneous multi-slice acquisition (SMS), and compressed sensing (CS), enabling up to eightfold faster scans while maintaining image quality, resolution, and safety standards. These innovations now allow for 3- to 6-fold accelerated clinical musculoskeletal MRI exams, reducing scan times to 4 to 6 min for joints and spine imaging. Evolving deep learning-based image reconstruction promises even faster scans without compromising quality. Current research indicates that combining acceleration techniques, deep learning image reconstruction, and superresolution algorithms will eventually facilitate tenfold accelerated musculoskeletal MRI in routine clinical practice. Such rapid MRI protocols can drastically reduce scan times by 80-90% compared to conventional methods. Implementing these rapid imaging protocols does impact workflow, indirect costs, and workload for MRI technologists and radiologists, which requires careful management. However, the shift from conventional to accelerated, deep learning-based MRI enhances the value of musculoskeletal MRI by improving patient access and comfort and promoting sustainable imaging practices. This article offers a comprehensive overview of the technical aspects, benefits, and challenges of modern accelerated musculoskeletal MRI, guiding radiologists and researchers in this evolving field.

Enhancing quality and speed in database-free neural network reconstructions of undersampled MRI with SCAMPI

PURPOSE

We present SCAMPI (Sparsity Constrained Application of deep Magnetic resonance Priors for Image reconstruction), an untrained deep Neural Network for MRI reconstruction without previous training on datasets. It expands the Deep Image Prior approach with a multidomain, sparsity-enforcing loss function to achieve higher image quality at a faster convergence speed than previously reported methods.

METHODS

Two-dimensional MRI data from the FastMRI dataset with Cartesian undersampling in phase-encoding direction were reconstructed for different acceleration rates for single coil and multicoil data.

RESULTS

The performance of our architecture was compared to state-of-the-art Compressed Sensing methods and ConvDecoder, another untrained Neural Network for two-dimensional MRI reconstruction. SCAMPI outperforms these by better reducing undersampling artifacts and yielding lower error metrics in multicoil imaging. In comparison to ConvDecoder, the U-Net architecture combined with an elaborated loss-function allows for much faster convergence at higher image quality. SCAMPI can reconstruct multicoil data without explicit knowledge of coil sensitivity profiles. Moreover, it is a novel tool for reconstructing undersampled single coil k-space data.

CONCLUSION

Our approach avoids overfitting to dataset features, that can occur in Neural Networks trained on databases, because the network parameters are tuned only on the reconstruction data. It allows better results and faster reconstruction than the baseline untrained Neural Network approach.

Ultrafast Brain MRI at 3 T for MS: Evaluation of a 51-Second Deep Learning-Enhanced T2-EPI-FLAIR Sequence

In neuroimaging, there is no equivalent alternative to magnetic resonance imaging (MRI). However, image acquisitions are generally time-consuming, which may limit utilization in some cases, e.g., in patients who cannot remain motionless for long or suffer from claustrophobia, or in the event of extensive waiting times. For multiple sclerosis (MS) patients, MRI plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIRUF) that has targeted neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MRI protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin-echo (TSE)-FLAIR, and FLAIRUF. Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIRUF and axially reconstructed 3D-FLAIR (lesion size ≥3 mm × ≥2 mm) and no differences in sensitivity between FLAIRUF and TSE-FLAIR (lesion size ≥3 mm total). Lesion conspicuity in FLAIRUF was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in the central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIRUF. In conclusion, FLAIRUF could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality.

Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI

One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of images at different saturation frequency offsets. k-space under-sampling strategy is commonly used to accelerate MRI acquisition, while this could introduce artifacts and reduce signal-to-noise ratio (SNR). To accelerate CEST-MRI acquisition while maintaining suitable image quality, we proposed an attention-based multioffset deep learning reconstruction network (AMO-CEST) with a multiple radial k-space sampling strategy for CEST-MRI. The AMO-CEST also contains dilated convolution to enlarge the receptive field and data consistency module to preserve the sampled k-space data. We evaluated the proposed method on a mouse brain dataset containing 5760 CEST images acquired at a pre-clinical 3 T MRI scanner. Quantitative results demonstrated that AMO-CEST showed obvious improvement over zero-filling method with a PSNR enhancement of 11 dB, a SSIM enhancement of 0.15, and a NMSE decrease of [Formula: see text] in three acquisition orientations. Compared with other deep learning-based models, AMO-CEST showed visual and quantitative improvements in images from three different orientations. We also extracted molecular contrast maps, including the amide proton transfer (APT) and the relayed nuclear Overhauser enhancement (rNOE). The results demonstrated that the CEST contrast maps derived from the CEST images of AMO-CEST were comparable to those derived from the original high-resolution CEST images. The proposed AMO-CEST can efficiently reconstruct high-quality CEST images from under-sampled k-space data and thus has the potential to accelerate CEST-MRI acquisition.

Role of Magnetic Resonance Spectroscopy in Evaluating Choline Levels in Gallbladder Carcinoma: A Comprehensive Review

Gallbladder carcinoma (GBC) presents a significant clinical challenge due to its aggressive nature and often asymptomatic progression, resulting in late-stage diagnoses and a poor prognosis. Early detection and accurate staging are pivotal for improving patient outcomes, highlighting the critical role of advanced imaging techniques in oncological practice. Magnetic resonance spectroscopy (MRS) has emerged as a valuable non-invasive tool capable of assessing biochemical changes within tissues, including alterations in choline metabolism-a biomarker indicative of cell membrane turnover and proliferation. This review explores the application of MRS in evaluating choline levels in gallbladder carcinoma, synthesizing current literature to elucidate its potential in clinical settings. By analyzing studies investigating the correlation between choline levels detected via MRS and tumor characteristics, this review underscores MRS's role in enhancing diagnostic precision and guiding therapeutic decision-making. Moreover, it discusses the challenges and limitations associated with MRS in clinical practice alongside future research and technological advancement directions. Ultimately, integrating MRS into the diagnostic armamentarium for gallbladder carcinoma promises to improve early detection and treatment outcomes. This review provides insights into the evolving landscape of MRS in oncology, emphasizing its contribution to personalized medicine approaches aimed at optimizing patient care and management strategies for GBC.

Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction

OBJECTIVES

The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for the acceleration and undersampling of imaging data. Resulting reduced acquisition times would lead to greater machine utility and to greater cost-efficiency of machine operations.

MATERIALS AND METHODS

Our case shows images from magnetic resonance arthrography under traction of the right hip joint from a 30-year-old, otherwise healthy, male patient.

RESULTS

The undersampled image data when reconstructed by a deep learning tool can contain false-positive cartilage delamination and false-positive diffuse cartilage defects.

CONCLUSIONS

In the future, precision of this novel technology will have to be put to thorough testing. Bias of systems, in particular created by the choice of training data, will have to be part of those assessments.

Evaluation of deep-learning TSE images in clinical musculoskeletal imaging

In this study, we compared the fat-saturated (FS) and non-FS turbo spin echo (TSE) magnetic resonance imaging knee sequences reconstructed conventionally (conventional-TSE) against a deep learning-based reconstruction of accelerated TSE (DL-TSE) scans. A total of 232 conventional-TSE and DL-TSE image pairs were acquired for comparison. For each consenting patient, one of the clinically acquired conventional-TSE proton density-weighted sequences in the sagittal or coronal planes (FS and non-FS), or in the axial plane (non-FS), was repeated using a research DL-TSE sequence. The DL-TSE reconstruction resulted in an image resolution that increased by at least 45% and scan times that were up to 52% faster compared to the conventional TSE. All images were acquired on a MAGNETOM Vida 3T scanner (Siemens Healthineers AG, Erlangen, Germany). The reporting radiologists, blinded to the acquisition time, were requested to qualitatively compare the DL-TSE against the conventional-TSE reconstructions. Despite having a faster acquisition time, the DL-TSE was rated to depict smaller structures better for 139/232 (60%) cases, equivalent for 72/232 (31%) cases and worse for 21/232 (9%) cases compared to the conventional-TSE. Overall, the radiologists preferred the DL-TSE reconstruction in 124/232 (53%) cases and stated no preference, implying equivalence, for 65/232 (28%) cases. DL-TSE reconstructions enabled faster acquisition times while enhancing spatial resolution and preserving the image contrast. From these results, the DL-TSE provided added or comparable clinical value and utility in less time. DL-TSE offers the opportunity to further reduce the overall examination time and improve patient comfort with no loss in diagnostic accuracy.

Deep-learning-based super-resolution for accelerating chemical exchange saturation transfer MRI

Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high-resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high-resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep-learning-based super-resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR-CEST, to reduce the acquisition time for CEST MRI by reconstructing high-resolution images from fast low-resolution acquisitions. This is achieved by first pretraining the DLSR-CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasets to fine-tune the weights. Using the trained DLSR-CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal-to-noise ratio and structural similarity index measure metrics at all downsampling factors (2-8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR-CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z-spectrum information. Therefore, our DLSR-CEST demonstrated a robust reconstruction of high-resolution CEST source images from fast low-resolution acquisitions, thereby improving the spatial resolution and preserving most Z-spectrum information.

Imaging the Lung in ARDS: A Primer

Despite periodic changes in the clinical definition of ARDS, imaging of the lung remains a central component of its diagnostic identification. Several imaging modalities are available to the clinician to establish a diagnosis of the syndrome, monitor its clinical course, or assess the impact of treatment and management strategies. Each imaging modality provides unique insight into ARDS from structural and/or functional perspectives. This review will highlight several methods for lung imaging in ARDS, emphasizing basic operational and physical principles for the respiratory therapist. Advantages and disadvantages of each modality will be discussed in the context of their utility for clinical management and decision-making.

Growth-Associated Protein 43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.

Deep learning for accelerated and robust MRI reconstruction

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.

Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement

OBJECTIVE

This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM).

MATERIALS AND METHODS

In vivo brain images acquired with 3D-T1W from various MRI scanners were utilized. For model training, LR images were generated by downsampling the original 1 mm-2 mm isotropic resolution images. Pairs of LR and HR images were used for training 3D residual dense net (RDN). For model testing, actual scanned 2 mm isotropic resolution 3D-T1W images with one-minute scan time were used. Normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used for model evaluation. The evaluation also included brain volume measurement, with assessments of subcortical brain regions.

RESULTS

The results showed that DL-SR model improved the quality of LR images compared with cubic interpolation, as indicated by NRMSE (24.22% vs 30.13%), PSNR (26.19 vs 24.65), and SSIM (0.96 vs 0.95). For volumetric assessments, there were no significant differences between DL-SR and actual HR images (p > 0.05, Pearson's correlation > 0.90) at seven subcortical regions.

DISCUSSION

The combination of LR MRI and DL-SR enables addressing prolonged scan time in 3D MRI scans while providing sufficient image quality without affecting brain volume measurement.

Multi-average high-acceleration modified volumetric interpolated breath-hold examination (VIBE) for free-breathing multiphase contrast-enhanced liver MRI: a comparative study with breath-hold VIBE

BACKGROUND

Breath-hold volumetric interpolated breath-hold examination (BH-VIBE) of multiphase contrast-enhanced liver magnetic resonance imaging (MPCE-LMRI) requires good cooperative individuals to comply with multiple breath-holds.

PURPOSE

To develop a free-breathing modified VIBE (FB-mVIBE) as a substitute of BH-VIBE in MPCE-LMRI.

MATERIAL AND METHODS

We modified VIBE with a high acceleration factor (2 × 2) and four averages to produce the mVIBE scan. A total of 90 individuals (40 men; mean age = 54.6 ± 10.0 years) who had received MPCE-LMRI as part of a voluntary health check-up for oncology survey were enrolled. Each participant was scanned in four phases (pre-contrast, arterial phase, venous phase, and delay phase), and each phase had two sequential scans. To encounter the timing effect of contrast enhancement, three scan orders were designed: BH-VIBE and FB-mVIBE (group A, n = 30); BH-VIBE and FB-VIBE (group B, n = 30); and FB-mVIBE and BH-VIBE (group C, n = 30). The comparisons included the objective measurements and 25 visual-score by two abdominal radiologists independently.

RESULTS

Consistency between raters was observed for all three sequences (intraclass correlation coefficient [ICC] = 0.741-0.829). For rater 1, the mean scores of FB-mVIBE (23.67 ± 1.32) were equal to those of BH-VIBE (23.83 ± 1.98) in groups C and B (P = 0.852). The mean scores of FB-mVIBE (22.07 ± 3.02), but significantly higher than those of FB-VIBE (14.7 ± 3.41) in groups A and B (P <0.001). Similar scores were found for rater 2. The objective measurement of FB-mVIBE were equal to or higher than BH-VIBE and markedly superior to FB-VIBE.

CONCLUSION

FB-mVIBE is a practical alternative to BH-VIBE for individuals who cannot cooperate with multiple breath-holds for MPCE-LMRI.

Highly Accelerated Three-Dimensional Free-Breathing Cardiac T2 Mapping with Modified Low-Rank Modeling of Local K-Space Neighborhoods

Three-dimensional (3D) cardiac magnetic resonance T2 mapping has been proven beneficial for the diagnosis of myocardial edema. However, the time-consuming nature of cardiac quantitative imaging (e.g., T2 mapping) poses challenges in a clinical context, limiting its application mainly to 2D scenarios where fewer k-space samples are required. Efforts to develop efficient acceleration techniques in 3D cardiac T2 mapping have long been a focus of research. This study aims to develop and evaluate a highly accelerated 3D free-breathing cardiac T2 Mapping sequence with lOw-rank modeling of lOcal k-space neighboRhoods with parallel and parametric data rEdundancies (3D T2MOORE). Utilizing a customized electrocardiogram (ECG)-based tiny-golden angle stack-of-star sequence, three different T2-weighted fully sampled reference data sets are acquired for T2 estimation. These sets are retrospectively under-sampled at four increasing acceleration rates (R = 3.84, 4.80, 6.40, and 9.60) and then reconstructed based on 3D T2MOORE. T2 values are derived by fitting pixel-wise signals to an exponential decay curve. We conducted in vivo experiments to compare 3D T2MOORE with 3D fully sampled balanced steady-state procession T2 mapping (3D T2bSSFP) and a previously published approach, 3D fast gradient echo T2 mapping (3D T2FGRE). Retrospective analysis demonstrated that 3D T2MOORE with 4.8-fold acceleration rate yielded T2 accuracy comparable to 3D T2bSSFP (both in good agreement with previously reported T2 results) and outperformed 3D T2FGRE significantly (3D T2FGRE: 48.01 ± 6.68 ms; 3D T2bSSFP: 38.56±1.65 ms; 3D T2MOORE: 38.50±2.71 ms).

Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation

BACKGROUND

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR.

METHODS

Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis.

RESULTS

These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives.

CONCLUSIONS

Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study

BACKGROUND

We compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance of common degenerative pathologies.

METHODS

This prospective, single-center study included 31 patients (15 males and 16 females; aged 51 ± 16 years (mean ± standard deviation)) who underwent lumbar spine exams with both TSE-SD and TSE-DL acquisitions for degenerative spine diseases. Images were analyzed by two radiologists and assessed for qualitative image quality using a 4-point Likert scale, quantitative signal-to-noise ratio (SNR) of anatomic landmarks, and detection of common pathologies. Paired-sample t, Wilcoxon, and McNemar tests, unweighted/linearly weighted Cohen κ statistics, and intraclass correlation coefficients were used.

RESULTS

Scan time for TSE-DL and TSE-SD protocols was 2:55 and 5:17 min:s, respectively. The overall image quality was either significantly higher for TSE-DL or not significantly different between TSE-SD and TSE-DL. TSE-DL demonstrated higher SNR and subject noise scores than TSE-SD. For pathology detection, the interreader agreement was substantial to almost perfect for TSE-DL, with κ values ranging from 0.61 to 1.00; the interprotocol agreement was almost perfect for both readers, with κ values ranging from 0.84 to 1.00. There was no significant difference in the diagnostic confidence or detection rate of common pathologies between the two sequences (p ≥ 0.081).

CONCLUSIONS

TSE-DL allowed for a 45% reduction in scan time over TSE-SD in lumbar spine MRI without compromising the overall image quality and showed comparable detection performance of common pathologies in the evaluation of degenerative lumbar spine changes.

RELEVANCE STATEMENT

Deep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies.

KEY POINTS

• Lumbar spine MRI with deep learning reconstruction has broad application prospects. • Deep learning reconstruction of lumbar spine MRI saved 45% scan time without compromising overall image quality. • When compared with standard sequences, deep learning reconstruction showed similar detection performance of common degenerative lumbar spine pathologies.

Clinical feasibility of post-contrast accelerated 3D T1-Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) with iterative denoising for intracranial enhancing lesions: a retrospective study

BACKGROUND

Post-contrast T1-Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) is the preferred 3D T1 spin-echo sequence for evaluating brain metastases, regardless of the prolonged scan time.

PURPOSE

To evaluate the application of accelerated post-contrast T1-SPACE with iterative denoising (ID) for intracranial enhancing lesions in oncologic patients.

MATERIAL AND METHODS

For evaluation of intracranial lesions, 108 patients underwent standard and accelerated T1-SPACE during the same imaging session. Two neuroradiologists evaluated the overall image quality, artifacts, degree of enhancement, mean contrast-to-noise ratiolesion/parenchyma, and number of enhancing lesions for standard and accelerated T1-SPACE without ID.

RESULTS

Although there was a significant difference in the overall image quality and mean contrast-to-noise ratiolesion/parenchyma between standard and accelerated T1-SPACE without ID and accelerated SPACE with and without ID, there was no significant difference between standard and accelerated T1-SPACE with ID. Accelerated T1-SPACE showed more artifacts than standard T1-SPACE; however, accelerated T1-SPACE with ID showed significantly fewer artifacts than accelerated T1-SPACE without ID. Accelerated T1-SPACE without ID showed a significantly lower number of enhancing lesions than standard- and accelerated T1-SPACE with ID; however, there was no significant difference between standard and accelerated T1-SPACE with ID, regardless of lesion size.

CONCLUSION

Although accelerated T1-SPACE markedly decreased the scan time, it showed lower overall image quality and lesion detectability than the standard T1-SPACE. Application of ID to accelerated T1-SPACE resulted in comparable overall image quality and detection of enhancing lesions in brain parenchyma as standard T1-SPACE. Accelerated T1-SPACE with ID may be a promising replacement for standard T1-SPACE.