3D Cartesian MRI with compressed sensing and variable view sharing using complementary poisson-disc sampling

Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation

PURPOSE

To achieve automatic hyperparameter estimation for the model-based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts.

THEORY

We introduce a novel approximate message passing framework "AMP-PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps.

METHODS

We employed the variable-flip-angle method to acquire multi-echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP-PE with conventional compressed sensing approaches such as thel 1 $$ {l}_1 $$ -norm minimization, PICS and other model-based approaches such as GraSP, MOBA.

RESULTS

Compared to conventional compressed sensing approaches such as thel 1 $$ {l}_1 $$ -norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors inT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ mapping and comparable performance inT 1 $$ {\mathrm{T}}_1 $$ and proton density mappings. When compared to other model-based approaches including GraSP and MOBA, AMP-PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP-PE offers faster speed than MOBA. AMP-PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP-PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches.

CONCLUSION

AMP-PE offers the benefits of model-based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground-truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient.

Clinical evaluation of isotropic MAVRIC-SL for symptomatic hip arthroplasties at 3 T MRI

BACKGROUND

3D multi-spectral imaging (MSI) of metal implants necessitates relatively long scan times.

OBJECTIVE

We implemented a fast isotropic 3D MSI technique at 3 T and compared its image quality and clinical utility to non-isotropic MSI in the evaluation of hip implants.

METHODS

Two musculoskeletal radiologists scored images from coronal proton density-weighted conventional MAVRIC-SL and an isotropic MAVRIC-SL sequence accelerated with robust-component-analysis on a 3-point scale (3: diagnostic, 2: moderately diagnostic, 1: non-diagnostic) for overall image quality, metal artifact, and visualization around femoral and acetabular components. Grades were compared using a signed Wilcoxon test. Images were evaluated for effusion, synovitis, osteolysis, loosening, pseudotumor, fracture, and gluteal tendon abnormalities. Reformatted axial and sagittal images for both sequences were subsequently generated and compared for image quality with the Wilcoxon test. Whether these reformats increased diagnostic confidence or revealed additional pathology, including findings unrelated to arthroplasty that may contribute to hip pain, was also compared using the McNemar test. Inter-rater agreement was measured by Cohen's kappa.

RESULTS

39 symptomatic patients with a total of 59 hip prostheses were imaged (mean age, 70 years ±9, 14 males, 25 females). Comparison scores between coronal images showed no significant difference in image quality, metal artifact, or visualization of the femur and acetabulum. Except for loosening, reviewers identified more positive cases of pathology on the original coronally-acquired isotropic sequence. In comparison of reformatted axial and sagittal images, the isotropic sequence scored significantly (p < 0.01) higher for overall image quality (3.0 vs 2.0) and produced significantly (p < 0.01) more cases of increased diagnostic confidence (42.4% vs 7.6%) or additional diagnoses (50.8% vs 22.9%). Inter-rater agreement was substantial (k = 0.798) for image quality. Mean scan times were 4.2 mins (isotropic) and 7.1 mins (non-isotropic).

CONCLUSION

Compared to the non-isotropic sequence, isotropic 3D MSI was acquired in less time while maintaining diagnostically acceptable image quality. It identified more pathology, including postoperative complications and potential pain-generating pathology unrelated to arthroplasty. This fast isotropic 3D MSI sequence demonstrates promise for improving diagnostic evaluation of symptomatic hip prostheses at 3 T while simultaneously reducing scan time.

Joint MAPLE: Accelerated joint T1 and T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ mapping with scan-specific self-supervised networks

PURPOSE

Quantitative MRI finds important applications in clinical and research studies. However, it is encoding intensive and may suffer from prohibitively long scan times. Accelerated MR parameter mapping techniques have been developed to help address these challenges. Here, an accelerated joint T1,T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ , frequency and proton density mapping technique with scan-specific self-supervised network reconstruction is proposed to synergistically combine parallel imaging, model-based, and deep learning approaches to speed up parameter mapping.

METHODS

Proposed framework, Joint MAPLE, includes parallel imaging, signal modeling, and data consistency blocks which are optimized jointly in a combined loss function. A scan-specific self-supervised reconstruction is embedded into the framework, which takes advantage of multi-contrast data from a multi-echo, multi-flip angle, gradient echo acquisition.

RESULTS

In comparison with parallel reconstruction techniques powered by low-rank methods, emerging scan specific networks, and model-basedT 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ estimation approaches, the proposed framework reduces the reconstruction error in parameter maps by approximately two-fold on average at acceleration rates as high as R = 16 with uniform sampling. It can outperform evaluated parallel reconstruction techniques up to four-fold on average in the presence of challenging sub-sampling masks. It is observed that Joint MAPLE performs well at extreme acceleration rates of R = 25 and R = 36 with error values less than 20%.

CONCLUSION

Joint MAPLE enables higher fidelity parameter estimation at high acceleration rates by synergistically combining parallel imaging and model-based parameter mapping and exploiting multi-echo, multi-flip angle datasets. Utilizing a scan-specific self-supervised reconstruction obviates the need for large data sets for training while improving the parameter estimation ability.

GRASP reconstruction amplified with view-sharing and KWIC filtering reduces underestimation of peak velocity in highly-accelerated real-time phase-contrast MRI: A preliminary evaluation in pediatric patients with congenital heart disease

PURPOSE

To develop a highly-accelerated, real-time phase contrast (rtPC) MRI pulse sequence with 40 fps frame rate (25 ms effective temporal resolution).

METHODS

Highly-accelerated golden-angle radial sparse parallel (GRASP) with over regularization may result in temporal blurring, which in turn causes underestimation of peak velocity. Thus, we amplified GRASP performance by synergistically combining view-sharing (VS) and k-space weighted image contrast (KWIC) filtering. In 17 pediatric patients with congenital heart disease (CHD), the conventional GRASP and the proposed GRASP amplified by VS and KWIC (or GRASP + VS + KWIC) reconstruction for rtPC MRI were compared with respect to clinical standard PC MRI in measuring hemodynamic parameters (peak velocity, forward volume, backward volume, regurgitant fraction) at four locations (aortic valve, pulmonary valve, left and right pulmonary arteries).

RESULTS

The proposed reconstruction method (GRASP + VS + KWIC) achieved better effective spatial resolution (i.e., image sharpness) compared with conventional GRASP, ultimately reducing the underestimation of peak velocity from 17.4% to 6.4%. The hemodynamic metrics (peak velocity, volumes) were not significantly (p > 0.99) different between GRASP + VS + KWIC and clinical PC, whereas peak velocity was significantly (p < 0.007) lower for conventional GRASP. RtPC with GRASP + VS + KWIC also showed the ability to assess beat-to-beat variation and detect the highest peak among peaks.

CONCLUSION

The synergistic combination of GRASP, VS, and KWIC achieves 25 ms effective temporal resolution (40 fps frame rate), while minimizing the underestimation of peak velocity compared with conventional GRASP.

Ultra-rapid, Free-breathing, Real-time Cardiac Cine MRI Using GRASP Amplified with View Sharing and KWIC Filtering

Purpose To achieve ultra-high temporal resolution (approximately 20 msec) in free-breathing, real-time cardiac cine MRI using golden-angle radial sparse parallel (GRASP) reconstruction amplified with view sharing (VS) and k-space-weighted image contrast (KWIC) filtering. Materials and Methods Fourteen pediatric patients with congenital heart disease (mean age [SD], 9 years ± 2; 13 male) and 10 adult patients with arrhythmia (mean age, 62 years ± 8; nine male) who underwent both standard breath-hold cine and free-breathing real-time cine using GRASP were retrospectively identified. To achieve high temporal resolution, each time frame was reconstructed using six radial spokes, corresponding to acceleration factors ranging from 24 to 32. To compensate for loss in spatial resolution resulting from over-regularization in GRASP, VS and KWIC filtering were incorporated. The blur metric, visual image quality scores, and biventricular parameters were compared between clinical and real-time cine images. Results In pediatric patients, the incorporation of VS and KWIC into GRASP (ie, GRASP + VS + KWIC) produced significantly (P < .05) sharper x-y-t (blur metric: 0.36 ± 0.03, 0.41 ± 0.03, 0.48 ± 0.03, respectively) and x-y-f (blur metric: 0.28 ± 0.02, 0.31 ± 0.03, 0.37 ± 0.03, respectively) component images compared with GRASP + VS and conventional GRASP. Only the noise score differed significantly between GRASP + VS + KWIC and clinical cine; all visual scores were above the clinically acceptable (3.0) cutoff point. Biventricular volumetric parameters strongly correlated (R2 > 0.85) between clinical and real-time cine images reconstructed with GRASP + VS + KWIC and were in good agreement (relative error < 6% for all parameters). In adult patients, the visual scores of all categories were significantly lower (P < .05) for clinical cine compared with real-time cine with GRASP + VS + KWIC, except for noise (P = .08). Conclusion Incorporating VS and KWIC filtering into GRASP reconstruction enables ultra-high temporal resolution (approximately 20 msec) without significant loss in spatial resolution. Keywords: Cine, View Sharing, k-Space-weighted Image Contrast Filtering, Radial k-Space, Pediatrics, Arrhythmia, GRASP, Compressed Sensing, Real-Time, Free-Breathing Supplemental material is available for this article. © RSNA, 2024.

Motion-compensated T1 mapping in cardiovascular magnetic resonance imaging: a technical review

T 1 mapping is becoming a staple magnetic resonance imaging method for diagnosing myocardial diseases such as ischemic cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and more. Clinically, most T 1 mapping sequences acquire a single slice at a single cardiac phase across a 10 to 15-heartbeat breath-hold, with one to three slices acquired in total. This leaves opportunities for improving patient comfort and information density by acquiring data across multiple cardiac phases in free-running acquisitions and across multiple respiratory phases in free-breathing acquisitions. Scanning in the presence of cardiac and respiratory motion requires more complex motion characterization and compensation. Most clinical mapping sequences use 2D single-slice acquisitions; however newer techniques allow for motion-compensated reconstructions in three dimensions and beyond. To further address confounding factors and improve measurement accuracy, T 1 maps can be acquired jointly with other quantitative parameters such as T 2 , T 2 ∗ , fat fraction, and more. These multiparametric acquisitions allow for constrained reconstruction approaches that isolate contributions to T 1 from other motion and relaxation mechanisms. In this review, we examine the state of the literature in motion-corrected and motion-resolved T 1 mapping, with potential future directions for further technical development and clinical translation.

SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling

PURPOSE

To develop an ultrafast and robust MR parameter mapping network using deep learning.

THEORY AND METHODS

We design a deep learning framework called SuperMAP that directly converts a series of undersampled (both in k-space and parameter-space) parameter-weighted images into several quantitative maps, bypassing the conventional exponential fitting procedure. We also present a novel technique to simultaneously reconstruct T1rho and T2 relaxation maps within a single scan. Full data were acquired and retrospectively undersampled for training and testing using traditional and state-of-the-art techniques for comparison. Prospective data were also collected to evaluate the trained network. The performance of all methods is evaluated using the parameter qualification errors and other metrics in the segmented regions of interest.

RESULTS

SuperMAP achieved accurate T1rho and T2 mapping with high acceleration factors (R = 24 and R = 32). It exploited both spatial and temporal information and yielded low error (normalized mean square error of 2.7% at R = 24 and 2.8% at R = 32) and high resemblance (structural similarity of 97% at R = 24 and 96% at R = 32) to the gold standard. The network trained with retrospectively undersampled data also works well for the prospective data (with a slightly lower acceleration factor). SuperMAP is also superior to conventional methods.

CONCLUSION

Our results demonstrate the feasibility of generating superfast MR parameter maps through very few undersampled parameter-weighted images. SuperMAP can simultaneously generate T1rho and T2 relaxation maps in a short scan time.

Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation

This work proposes to develop and evaluate a deep learning framework that jointly optimizes k-t sampling patterns and reconstruction for head and neck dynamic contrast-enhanced (DCE) MRI aiming to reduce bias and uncertainty of pharmacokinetic (PK) parameter estimation. 2D Cartesian phase encoding k-space subsampling patterns for a 3D spoiled gradient recalled echo (SPGR) sequence along a time course of DCE MRI were jointly optimized in a deep learning-based dynamic MRI reconstruction network by a loss function concerning both reconstruction image quality and PK parameter estimation accuracy. During training, temporal k-space data sharing scheme was optimized as well. The proposed method was trained and tested by multi-coil complex digital reference objects of DCE images (mcDROs). The PK parameters estimated by the proposed method were compared with two published iterative DCE MRI reconstruction schemes using normalized root mean squared errors (NRMSEs) and Bland-Altman analysis at temporal resolutions of [Formula: see text] = 2s, 3s, 4s, and 5s, which correspond to undersampling rates of R = 50, 34, 25, and 20. The proposed method achieved low PK parameter NRMSEs at all four temporal resolutions compared with the benchmark methods on testing mcDROs. The Bland-Altman plots demonstrated that the proposed method reduced PK parameter estimation bias and uncertainty in tumor regions at temporal resolution of 2s. The proposed method also showed robustness to contrast arrival timing variations across patients. This work provides a potential way to increase PK parameter estimation accuracy and precision, and thus facilitate the clinical translation of DCE MRI.

Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications

This work proposes an alternating learning approach to learn the sampling pattern (SP) and the parameters of variational networks (VN) in accelerated parallel magnetic resonance imaging (MRI). We investigate four variations of the learning approach, that alternates between improving the SP, using bias-accelerated subset selection, and improving parameters of the VN, using ADAM. The variations include the use of monotone or non-monotone alternating steps and systematic reduction of learning rates. The algorithms learn an effective pair to be used in future scans, including an SP that captures fewer k-space samples in which the generated undersampling artifacts are removed by the VN reconstruction. The quality of the VNs and SPs obtained by the proposed approaches is compared against different methods, including other kinds of joint learning methods and state-of-art reconstructions, on two different datasets at various acceleration factors (AF). We observed improvements visually and in three different figures of merit commonly used in deep learning (RMSE, SSIM, and HFEN) on AFs from 2 to 20 with brain and knee joint datasets when compared to the other approaches. The improvements ranged from 1% to 62% over the next best approach tested with VNs. The proposed approach has shown stable performance, obtaining similar learned SPs under different initial training conditions. We observe that the improvement is not only due to the learned sampling density, it is also due to the learned position of samples in k-space. The proposed approach was able to learn effective pairs of SPs and reconstruction VNs, improving 3D Cartesian accelerated parallel MRI applications.

Utilizing the Wavelet Transform's Structure in Compressed Sensing

Compressed sensing has empowered quality image reconstruction with fewer data samples than previously thought possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is commonly used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns using both magnetic resonance and optical images.

Fast data-driven learning of parallel MRI sampling patterns for large scale problems

In this study, a fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. BASS is applicable when Cartesian fully-sampled k-space measurements of specific anatomy are available for training and the reconstruction method for undersampled measurements is specified; such information is used to define the efficacy of any SP for recovering the values at the non-sampled k-space points. BASS produces a sequence of SPs with the aim of finding one of a specified size with (near) optimal efficacy. BASS was tested with five reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Three datasets were used for testing, two of high-resolution brain images ([Formula: see text]-weighted images and, respectively, [Formula: see text]-weighted images) and another of knee images for quantitative mapping of the cartilage. The proposed approach has low computational cost and fast convergence; in the tested cases it obtained SPs up to 50 times faster than the currently best greedy approach. Reconstruction quality increased by up to 45% over that provided by variable density and Poisson disk SPs, for the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Quantitative MRI and prospective accelerated MRI results show improvements. Compared with greedy approaches, BASS rapidly learns effective SPs for various reconstruction methods, using larger SPs and larger datasets; enabling better selection of sampling-reconstruction pairs for specific MRI problems.

Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI

We present a fast method for generating random samples according to a variable density poisson-disc distribution. A minimum parameter value is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.

Free-breathing radial imaging using a pilot-tone radiofrequency transmitter for detection of respiratory motion

PURPOSE

To describe an approach for detection of respiratory signals using a transmitted radiofrequency (RF) reference signal called Pilot-Tone (PT) and to use the PT signal for creation of motion-resolved images based on 3D stack-of-stars imaging under free-breathing conditions.

METHODS

This work explores the use of a reference RF signal generated by a small RF transmitter, placed outside the MR bore. The reference signal is received in parallel to the MR signal during each readout. Because the received PT amplitude is modulated by the subject's breathing pattern, a respiratory signal can be obtained by detecting the strength of the received PT signal over time. The breathing-induced PT signal modulation can then be used for reconstructing motion-resolved images from free-breathing scans. The PT approach was tested in volunteers using a radial stack-of-stars 3D gradient echo (GRE) sequence with golden-angle acquisition.

RESULTS

Respiratory signals derived from the proposed PT method were compared to signals from a respiratory cushion sensor and k-space-center-based self-navigation under different breathing conditions. Moreover, the accuracy was assessed using a modified acquisition scheme replacing the golden-angle scheme by a zero-angle acquisition. Incorporating the PT signal into eXtra-Dimensional (XD) motion-resolved reconstruction led to improved image quality and clearer anatomical depiction of the lung and liver compared to k-space-center signal and motion-averaged reconstruction, when binned into 6, 8, and 10 motion states.

CONCLUSION

PT is a novel concept for tracking respiratory motion. Its small dimension (8 cm), high sampling rate, and minimal interaction with the imaging scan offers great potential for resolving respiratory motion.

J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction

Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce the scan time. The image quality of these approaches is heavily dependent on the sampling pattern. We introduce a continuous strategy to optimize the sampling pattern and the network parameters jointly. We use a multichannel forward model, consisting of a non-uniform Fourier transform with continuously defined sampling locations, to realize the data consistency block within a model-based deep learning image reconstruction scheme. This approach facilitates the joint and continuous optimization of the sampling pattern and the CNN parameters to improve image quality. We observe that the joint optimization of the sampling patterns and the reconstruction module significantly improves the performance of most deep learning reconstruction algorithms. The source code is available at https://github.com/hkaggarwal/J-MoDL.

Rapid Imaging: Recent Advances in Abdominal MRI for Reducing Acquisition Time and Its Clinical Applications

Magnetic resonance imaging (MRI) plays an important role in abdominal imaging. The high contrast resolution offered by MRI provides better lesion detection and its capacity to provide multiparametric images facilitates lesion characterization more effectively than computed tomography. However, the relatively long acquisition time of MRI often detrimentally affects the image quality and limits its accessibility. Recent developments have addressed these drawbacks. Specifically, multiphasic acquisition of contrast-enhanced MRI, free-breathing dynamic MRI using compressed sensing technique, simultaneous multi-slice acquisition for diffusion-weighted imaging, and breath-hold three-dimensional magnetic resonance cholangiopancreatography are recent notable advances in this field. This review explores the aforementioned state-of-the-art techniques by focusing on their clinical applications and potential benefits, as well as their likely future direction.

2D multislice MP2RAGE sequence for fast T1 mapping at 7 T: Application to mouse imaging and MR thermometry

PURPOSE

To develop a 2D radial multislice MP2RAGE sequence for fast and reliable T₁ mapping at 7 T in mice and for MR thermometry.

METHODS

The 2D-MP2RAGE sequence was performed with the following parameters: TI₁ -TI₂ -MP2RAGE_TR = 1000-3000-9000 ms. The multiple dead times within the sequence were used for interleaved multislice acquisition, enabling one to acquire six slices in 9 seconds. The excitation pulse shape, inversion selectivity, and interslice gap were optimized. In vitro comparison with the inversion-recovery sequence was performed. The T₁ variations with temperature were measured on tubes with T₁ ranging from 800 ms to 2000 ms. The sequence was used to acquire T₁ maps continuously during 30 minutes on the brain and abdomen of healthy mice.

RESULTS

A three-lobe cardinal sine excitation pulse, combined with an inversion slice thickness and an interslice gap of respectively 150% and 50% of the imaging slice thickness, led to a SD and bias of the T₁ measurements below 1% and 2%, respectively. A linear dependence of T₁ with temperature was measured between 10°C and 60°C. In vivo, less than 1% variation was measured between successive T₁ maps in the mouse brain. In the abdomen, no obvious in-plane motion artifacts were observed but respiratory motion in the slice dimension led to 6% T₁ underestimation.

CONCLUSION

The multislice MP2RAGE sequence could be used for fast whole-body T₁ mapping and MR thermometry. Its reconstruction method would enable on-the-fly reconstruction.

Quantitative renal function assessment of atheroembolic renal disease using view-shared compressed sensing based dynamic-contrast enhanced MR imaging: An in vivo study

Atheroembolic renal disease (AERD) is the major cause of renal insufficiency in the elderly, and particularly, the diagnose of AERD is often delayed and even missed due to its nonspecific presentation and the sudden occurrence of an embolic event. To investigate the feasibility of the view-shared compressed sensing (VCS) based dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in the assessment of AERD in animal models. The reproducibility of VCS DCE-MRI based glomerular filtration rate (GFR) estimation was first evaluated using the three healthy rabbits. Animal models of unilateral AERD were then conducted. All the rabbits underwent VCS DCE-MRI and the GFR maps were estimated by a commonly used cortical-compartment model. The whole kidney and suspicious lesion region GFR values of embolized kidneys were then compared with the corresponding values of normal kidneys. Finally, the suspicious lesion regions were confirmed by the corresponding renal specimens and histological findings. The reproducibility of GFR measurements was analyzed using the coefficient of variation and Bland-Altman analysis. The GFR values of normal and embolized kidneys were compared using the Student t-test. Contrast-enhanced images with sufficient diagnostic quality and reduced motion artifacts are obtained at a temporal resolution of 2.5 s. The Bland-Altman plot indicated close agreement between the GFR values estimated from between-day scans in healthy rabbits. Besides, there existed significant differences between the pixel-wise GFR values of normal and AERD kidneys in region-based comparison(P < 0.0001). The suspicious lesions are consistent well with the renal specimen and histological findings. The preliminary animal study verified the feasibility of VCS DCE-MRI for renal function evaluation, and the strategy could potentially provide a valuable tool to identify AERD.

Impact of the Number of Iterations in Compressed Sensing Reconstruction on Ultrafast Dynamic Contrast-enhanced Breast MR Imaging

PURPOSE

To assess the impact of the number of iterations of compressed sensing (CS) reconstruction on the kinetic parameters and image quality in dynamic contrast-enhanced (DCE)-MRI of the breast, with prospectively undersampled CS-accelerated scans.

MATERIALS AND METHODS

Breast examinations including ultrafast DCE-MRI using CS were conducted for 21 patients. Images were reconstructed with different numbers of iterations. The peak enhancement ratio of the aorta and wash-in slope, initial area under the curve, and Ktrans of the breast lesions were measured. The root mean square error and structural similarity between the images using 50 iterations and images with a lower number of iterations were evaluated as criterion for quantitative image evaluation.

RESULTS

Using an insufficient number of iterations, the contrast-enhanced effect was highly underestimated. In all semi-quantitative parameters, the number of iterations that stabilized the parameters in malignant lesions was higher than that in benign lesions. At least 15 iterations were needed for semi-quantitative parameters. For Ktrans, there were no significant differences between 10 and 50 iterations in both malignant and benign lesions.

CONCLUSION

The kinetic parameters using ultrafast DCE-MRI with CS are affected by the number of iterations, especially in malignant lesions. However, if the images are reconstructed with an adequate number of iterations, ultrafast DCE-MRI with CS can be a powerful technique having high temporal and spatial resolution.

Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI

A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo [Formula: see text]-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.

Compressed Sensing 3D-GRASE for faster High-Resolution MRI

Purpose

High‐resolution three‐dimensional (3D) structural MRI is useful for delineating complex or small structures of the body. However, it requires long acquisition times and high SAR, limiting its clinical use. The purpose of this work is to accelerate the acquisition of high‐resolution images by combining compressed sensing and parallel imaging (CSPI) on a 3D‐GRASE sequence and to compare it with a (CS)PI 3D‐FSE sequence. Several sampling patterns were investigated to assess their influence on image quality.

Methods

The proposed k‐space sampling patterns are based on two undersampled k‐space grids, variable density (VD) Poisson‐disc, and VD pseudo‐random Gaussian, and five different trajectories described in the literature. Bloch simulations are performed to obtain the transform point spread function and evaluate the coherence of each sampling pattern. Image resolution was assessed by the full‐width at half‐maximum (FWHM). Prospective CSPI 3D‐GRASE phantom and in vivo experiments in knee and brain are carried out to assess image quality, SNR, SAR, and acquisition time compared to PI 3D‐GRASE, PI 3D‐FSE, and CSPI 3D‐FSE acquisitions.

Results

Sampling patterns with VD Poisson‐disc obtain the lowest coherence for both PD‐weighted and T2‐weighted acquisitions. VD pseudo‐random Gaussian obtains lower FWHM, but higher sidelobes than VD Poisson‐disc. CSPI 3D‐GRASE reduces acquisition time (43% for PD‐weighted and 40% for T2‐weighted) and SAR (∼45% for PD‐weighted and T2‐weighted) compared to CSPI 3D‐FSE.

Conclusions

CSPI 3D‐GRASE reduces acquisition time compared to a CSPI 3DFSE acquisition, preserving image quality. The design of the sampling pattern is crucial for image quality in CSPI 3D‐GRASE image acquisitions.

Volumetric abdominal perfusion measurement using a pseudo-randomly sampled 3D fast-spin-echo (FSE) arterial spin labeling (ASL) sequence and compressed sensing reconstruction

PURPOSE

To improve image quality and spatial coverage for abdominal perfusion imaging by implementing an arterial spin labeling (ASL) sequence that combines variable-density 3D fast-spin-echo (FSE) with Cartesian trajectory and compressed-sensing (CS) reconstruction.

METHODS

A volumetric FSE sequence was modified to include background-suppressed pseudo-continuous ASL labeling and to support variable-density (VD) Poisson-disk sampling for acceleration. We additionally explored the benefits of center oversampling and variable outer k-space sampling. Fourteen healthy volunteers were scanned on a 3T scanner to test acceleration factors as well as the various sampling schemes described under synchronized-breathing to limit motion issues. A CS reconstruction was implemented using the BART toolbox to reconstruct perfusion-weighted ASL volumes, assessing the impact of acceleration, different reconstruction, and sampling strategies on image quality.

RESULTS

CS acceleration is feasible with ASL, and a strong renal perfusion signal could be observed even at very high acceleration rates (≈15). We have shown that ASL k-space complex subtraction was desirable before CS reconstruction. Although averaging of multiple highly accelerated images helped to reduce artifacts from physiologic fluctuations, superior image quality was achieved by interleaving of different highly undersampled pseudo-random spatial sampling patterns and using 4D-CS reconstruction. Combination of these enhancements produces high-quality ASL volumes in under 5 min.

CONCLUSIONS

High-quality isotropic ASL abdominal perfusion volumes can be obtained in healthy volunteers with a VD-FSE and CS reconstruction. This lays the groundwork for future developments toward whole abdomen free-breathing non-contrast perfusion imaging.

Evaluation of compressed sensing MRI for accelerated bowel motility imaging

Background

To investigate the feasibility of compressed sensing and parallel imaging (CS-PI)-accelerated bowel motility magnetic resonance imaging (MRI) and to compare its image quality and diagnostic quality to conventional sensitivity encoding (SENSE) accelerated scans.

Methods

Bowel MRI was performed in six volunteers using a three-dimensional balanced fast field-echo sequence. Static scans were performed after the administration of a spasmolytic agent to prevent bowel motion artefacts. Fully sampled reference scans and multiple prospectively 3× to 7× undersampled CS-PI and SENSE scans were acquired. Additionally, fully sampled CS-PI and SENSE scans were retrospectively undersampled and reconstructed. Dynamic scans were performed using 5× to 7× accelerated scans in the presence of bowel motion. Retrospectively, undersampled scans were compared to fully sampled scans using structural similarity indices. All reconstructions were visually assessed for image quality and diagnostic quality by two radiologists.

Results

For static imaging, the performance of CS-PI was lower than that of fully sampled and SENSE scans: the diagnostic quality was assessed as adequate or good for 100% of fully sampled scans, 95% of SENSE, but only for 55% of CS-PI scans. For dynamic imaging, CS-PI image quality was scored similar to SENSE at high acceleration. Diagnostic quality of all scans was scored as adequate or good; 55% of CS-PI and 83% of SENSE scans were scored as good.

Conclusion

Compared to SENSE, current implementation of CS-PI performed less or equally good in terms of image quality and diagnostic quality. CS-PI did not show advantages over SENSE for three-dimensional bowel motility imaging.

Accelerated imaging of metallic implants using model-based nonlinear reconstruction

PURPOSE

To accelerate imaging near metallic implants with multi-spectral imaging (MSI) techniques by exploiting a signal model in the spectral dimension.

METHODS

MSI techniques resolve metal-induced field perturbations by acquiring separate 3D spatial encodings at multiple excitation frequencies, which are referred to as spectral bins. The proposed model-based reconstruction exploits the correlation between spectral bins in image reconstruction by enforcing a signal model to describe the signal profile across bins. This work evaluates the accuracy of the MSI signal model in simulations and in vivo experiments. The proposed model-based reconstruction was evaluated in 6 subjects at an overall undersampling factor of 17.4 and compared with model-free parallel imaging and compressed sensing (PI & CS). The quality of reconstructed images was evaluated using normalized RMS error (nRMSE) and structural similarity index (SSIM) comparisons, with paired Wilcoxon tests in 6 subjects used to determine whether there was a significant difference in the metrics.

RESULTS

Both simulations and in vivo experiments show that the proposed signal model can represent the MSI signal profiles in the spectral dimension compactly and accurately. In the in vivo experiments, the model-based reconstruction significantly improved image quality over model-free PI & CS, with P < 0.05 for both nRMSE and SSIM at 17.4× acceleration.

CONCLUSION

This work presents the feasibility of using a model-based reconstruction to accelerate MSI techniques for faster MR imaging near metal.

View-Sharing Artifact Reduction With Retrospective Compressed Sensing Reconstruction in the Context of Contrast-Enhanced Liver MRI for Hepatocellular Carcinoma (HCC) Screening

BACKGROUND

View-sharing (VS) increases spatiotemporal resolution in dynamic contrast-enhanced (DCE) MRI by sharing high-frequency k-space data across temporal phases. This temporal sharing results in respiratory motion within any phase to propagate artifacts across all shared phases. Compressed sensing (CS) eliminates the need for VS by recovering missing k-space data from pseudorandom undersampling, reducing temporal blurring while maintaining spatial resolution.

PURPOSE

To evaluate a CS reconstruction algorithm on undersampled DCE-MRI data for image quality and hepatocellular carcinoma (HCC) detection.

STUDY TYPE

Retrospective.

SUBJECTS

Fifty consecutive patients undergoing MRI for HCC screening (29 males, 21 females, 52-72 years).

FIELD STRENGTH/SEQUENCE

3.0T MRI. Multiphase 3D-SPGR T₁ -weighted sequence undersampled in arterial phases with a complementary Poisson disc sampling pattern reconstructed with VS and CS algorithms.

ASSESSMENT

VS and CS reconstructions evaluated by blinded assessments of image quality and anatomic delineation on Likert scales (1-4 and 1-5, respectively), and HCC detection by OPTN/UNOS criteria including a diagnostic confidence score (1-5). Blinded side-by-side reconstruction comparisons for lesion depiction and overall series preference (-3-3).

STATISTICAL ANALYSIS

Two-tailed Wilcoxon signed rank tests for paired nonparametric analyses with Bonferroni-Holm multiple-comparison corrections. McNemar's test for differences in lesion detection frequency and transplantation eligibility.

RESULTS

CS compared with VS demonstrated significantly improved contrast (mean 3.6 vs. 2.9, P < 0.0001) and less motion artifact (mean 3.6 vs. 3.2, P = 0.006). CS compared with VS demonstrated significantly improved delineations of liver margin (mean 4.5 vs. 3.8, P = 0.0002), portal veins (mean 4.5 vs. 3.7, P < 0.0001), and hepatic veins (mean 4.6 vs. 3.5, P < 0.0001), but significantly decreased delineation of hepatic arteries (mean 3.2 vs. 3.7, P = 0.004). No significant differences were seen in the other assessments.

DATA CONCLUSION

Applying a CS reconstruction to data acquired for a VS reconstruction significantly reduces motion artifacts in a clinical DCE protocol for HCC screening.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:984-993.

Imaging biomarkers in oncology: Basics and application to MRI

Cancer remains a global killer alongside cardiovascular disease. A better understanding of cancer biology has transformed its management with an increasing emphasis on a personalized approach, so-called "precision cancer medicine." Imaging has a key role to play in the management of cancer patients. Imaging biomarkers that objectively inform on tumor biology, the tumor environment, and tumor changes in response to an intervention complement genomic and molecular diagnostics. In this review we describe the key principles for imaging biomarker development and discuss the current status with respect to magnetic resonance imaging (MRI).

LEVEL OF EVIDENCE

5 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2018;48:13-26.

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

Accelerated three-dimensional multispectral MRI with robust principal component analysis for separation of on- and off-resonance signals

PURPOSE

To enable highly accelerated distortion-free MRI near metal by separating on- and off-resonance to exploit the redundancy of slice-phase encoding for the dominant on-resonance component.

METHODS

Multispectral MRI techniques resolve off-resonance distortions by a combination of limited excitation bins and additional encoding. Inspired by robust principal component analysis, a novel compact representation of multispectral images as a sum of rank-one and sparse matrices corresponding to on- and off-resonance respectively is described. This representation is used in a calibration-free and model-free reconstruction for data with an undersampling pattern that varies between bins. Retrospective undersampling was used to compare the proposed reconstruction and bin-by-bin compressed sensing. Hip images were acquired in eight patients with standard and prospectively undersampled three-dimensional multispectral imaging, and image quality was evaluated by two radiologists on a 5-point scale.

RESULTS

Experiments with retrospective undersampling showed that the enhanced sparsity afforded by the separation greatly reduces reconstruction errors and artifacts. Images from prospectively undersampled multispectral imaging offered 2.6-3.4-fold (18-24-fold overall) acceleration compared to standard multispectral imaging with parallel imaging and partial-Fourier acceleration with equivalence in all qualitative assessments within a tolerance of one point (P < 0.004).

CONCLUSION

Three-dimensional multispectral imaging can be highly accelerated by varying undersampling between bins and separating on- and off-resonance. Magn Reson Med 79:1495-1505, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Feasibility of high spatiotemporal resolution for an abbreviated 3D radial breast MRI protocol

PURPOSE

To develop a volumetric imaging technique with 0.8-mm isotropic resolution and 10-s/volume rate to detect and analyze breast lesions in a bilateral, dynamic, contrast-enhanced MRI exam.

METHODS

A local low-rank temporal reconstruction approach that also uses parallel imaging and spatial compressed sensing was designed to create rapid volumetric frame rates during a contrast-enhanced breast exam (vastly undersampled isotropic projection [VIPR] spatial compressed sensing with temporal local low-rank [STELLR]). The dynamic-enhanced data are subtracted in k-space from static mask data to increase sparsity for the local low-rank approach to maximize temporal resolution. A T₁ -weighted 3D radial trajectory (VIPR iterative decomposition with echo asymmetry and least squares estimation [IDEAL]) was modified to meet the data acquisition requirements of the STELLR approach. Additionally, the unsubtracted enhanced data are reconstructed using compressed sensing and IDEAL to provide high-resolution fat/water separation. The feasibility of the approach and the dual reconstruction methodology is demonstrated using a 16-channel breast coil and a 3T MR scanner in 6 patients.

RESULTS

The STELLR temporal performance of subtracted data matched the expected temporal perfusion enhancement pattern in small and large vascular structures. Differential enhancement within heterogeneous lesions is demonstrated with corroboration from a basic reconstruction using a strict 10-second temporal footprint. Rapid acquisition, reliable fat suppression, and high spatiotemporal resolution are presented, despite significant data undersampling.

CONCLUSION

The STELLR reconstruction approach of 3D radial sampling with mask subtraction provides a high-performance imaging technique for characterizing enhancing structures within the breast. It is capable of maintaining temporal fidelity, while visualizing breast lesions with high detail over a large FOV to include both breasts.

Quasi-Random Single-Point Imaging Using Low-Discrepancy $k$ -Space Sampling

Magnetic resonance imaging of short relaxation time spin systems has been a widely discussed topic with serious clinical applications and led to the emergence of fast imaging ultra-short echo-time sequences. Nevertheless, these sequences suffer from image blurring, due to the related sampling point spread function and are highly prone to imaging artefacts arising from, e.g., chemical shifts or magnetic susceptibilities. In this paper, we present a concept of spherical quasi-random single-point imaging. The approach is highly accelerateable, due to intrinsic undersampling properties and capable of strong metal artefact suppression. Imaging acceleration is achieved by sampling of quasi-random points in -space, based on a low-discrepancy sequence, and a combination with non-linear optimization reconstruction techniques [compressed sensing (CS)]. The presented low-discrepancy trajectory shows ideal noise like undersampling properties for the combination with CS, leading to denoised images with excellent metal artefact reduction. Using eightfold undersampling, acquisition time of a few minutes can be achieved for volume acquisitions.

Single-breath-hold abdominal [Formula: see text] mapping using 3D Cartesian Look-Locker with spatiotemporal sparsity constraints

OBJECTIVE

Our aim was to develop and validate a 3D Cartesian Look-Locker [Formula: see text] mapping technique that achieves high accuracy and whole-liver coverage within a single breath-hold.

MATERIALS AND METHODS

The proposed method combines sparse Cartesian sampling based on a spatiotemporally incoherent Poisson pattern and k-space segmentation, dedicated for high-temporal-resolution imaging. This combination allows capturing tissue with short relaxation times with volumetric coverage. A joint reconstruction of the 3D + inversion time (TI) data via compressed sensing exploits the spatiotemporal sparsity and ensures consistent quality for the subsequent multistep [Formula: see text] mapping. Data from the National Institute of Standards and Technology (NIST) phantom and 11 volunteers, along with reference 2D Look-Locker acquisitions, are used for validation. 2D and 3D methods are compared based on [Formula: see text] values in different abdominal tissues at 1.5 and 3 T.

RESULTS

[Formula: see text] maps obtained from the proposed 3D method compare favorably with those from the 2D reference and additionally allow for reformatting or volumetric analysis. Excellent agreement is shown in phantom [bias[Formula: see text] < 2%, bias[Formula: see text] < 5% for (120; 2000) ms] and volunteer data (3D and 2D deviation < 4% for liver, muscle, and spleen) for clinically acceptable scan (20 s) and reconstruction times (< 4 min).

CONCLUSION

Whole-liver [Formula: see text] mapping with high accuracy and precision is feasible in one breath-hold using spatiotemporally incoherent, sparse 3D Cartesian sampling.