Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI

Differentiation of Jugular Foramen Paragangliomas versus Schwannomas Using Golden-Angle Radial Sparse Parallel Dynamic Contrast-Enhanced MRI

BACKGROUND AND PURPOSE

Accurate differentiation of paragangliomas and schwannomas in the jugular foramen has important clinical implications because treatment strategies may vary but differentiation is not always straightforward with conventional imaging. Our aim was to evaluate the accuracy of both qualitative and quantitative metrics derived from dynamic contrast-enhanced MR imaging using golden-angle radial sparse parallel MR imaging to differentiate paragangliomas and schwannomas in the jugular foramen.

MATERIALS AND METHODS

A retrospective study of imaging data was performed on patients (n = 30) undergoing MR imaging for jugular foramen masses with the golden-angle radial sparse parallel MR imaging technique. Imaging data were postprocessed to obtain time-intensity curves and quantitative parameters. Data were normalized to the dural venous sinus for relevant parameters and analyzed for statistical significance using a Student t test. A univariate logistic model was created with a binary output, paraganglioma or schwannoma, using a wash-in rate as a variable. Additionally, lesions were clustered on the basis of the wash-in rate and washout rate using a 3-nearest neighbors method.

RESULTS

There were 22 paragangliomas and 8 schwannomas. All paragangliomas demonstrated a type 3 time-intensity curve, and all schwannomas demonstrated a type 1 time-intensity curve. There was a statistically significant difference between paragangliomas and schwannomas when comparing their values for area under the curve, peak enhancement, wash-in rate, and washout rate. A univariate logistic model with a binary output (paraganglioma or schwannoma) using wash-in rate as a variable was able to correctly predict all observed lesions (P < .001). All 30 lesions were classified correctly by using a 3-nearest neighbors method.

CONCLUSIONS

Paragangliomas at the jugular foramen can be reliably differentiated from schwannomas using golden-angle radial sparse parallel MR imaging-dynamic contrast-enhanced imaging when imaging characteristics cannot suffice.

Respiratory anomalies associated with gadoxetate disodium and gadoterate meglumine: compressed sensing MRI revealing physiologic phenomena during the entire injection cycle

OBJECTIVES

The goal of this study was to investigate the precise timeline of respiratory events occurring after the administration of two gadolinium-based contrast agents, gadoxetate disodium and gadoterate meglumine.

MATERIALS AND METHODS

This retrospective study examined 497 patients subject to hepatobiliary imaging using the GRASP MRI technique (TR/TE = 4/2 ms; ST = 2.5 mm; 384 × 384 mm). Imaging was performed after administration of gadoxetate (N = 338) and gadoterate (N = 159). All GRASP datasets were reconstructed using a temporal resolution of 1 s. Four regions-of-interest (ROIs) were placed in the liver dome, the right and left cardiac ventricle, and abdominal aorta detecting liver displacement and increasing vascular signal intensities over time. Changes in hepatic intensity reflected respiratory dynamics in temporal correlation to the vascular contrast bolus.

RESULTS

In total, 216 (67%) and 41 (28%) patients presented with transient respiratory motion after administration of gadoxetate and gadoterate, respectively. The mean duration from start to acme of the respiratory episode was similar (p = 0.4) between gadoxetate (6.0 s) and gadoterate (5.6 s). Its mean onset in reference to contrast arrival in the right ventricle differed significantly (p < 0.001) between gadoxetate (15.3s) and gadoterate (1.8 s), analogously to peak inspiration timepoint in reference to the aortic enhancement arrival (gadoxetate: 0.9s after, gadoterate: 11.2 s before aortic enhancement, p < 0.001).

CONCLUSIONS

The timepoint of occurrence of transient respiratory anomalies associated with gadoxetate disodium and gadoterate meglumine differs significantly between both contrast agents while the duration of the event remains similar.

KEY POINTS

• Transient respiratory anomalies following the administration of gadoterate meglumine occurred during a time period usually not acquired in MR imaging. • Transient respiratory anomalies following the administration of gadoxetate disodium occurred around the initiation of arterial phase imaging. • The estimated duration of respiratory events was similar between both contrast agents.

Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping

PURPOSE

This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping.

METHODS

An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver).

RESULTS

ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers.

CONCLUSION

This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.

Intraindividual Comparison of Compressed Sensing-Accelerated Cartesian and Radial Arterial Phase Imaging of the Liver in an Experimental Tumor Model

OBJECTIVES

The aim of this study was to intraindividually compare the performance of 2 compressed sensing (CS)-accelerated magnetic resonance imaging (MRI) sequences, 1 featuring Cartesian (compressed sensing volumetric interpolated breath-hold examination [CS-VIBE]) and the other radial (golden-angle radial sparse parallel [GRASP]) k-space sampling in continuous dynamic imaging during hepatic vascular phases, using extracellular and hepatocyte-specific contrast agents.

MATERIALS AND METHODS

Seven New Zealand white rabbits, with induced VX2 liver tumors (median number of lesions, 2 ± 0.83; range, 1-3), received 2 continuously acquired T1-weighted prototype CS-accelerated MRI sequences (CS-VIBE and GRASP) with high spatial (0.8 × 0.8 × 1.5 mm) and temporal resolution (3.5 seconds) in randomized order on 2 separate days using a 1.5-T scanner. In all animals, imaging was performed using first gadobutrol at a dose of 0.1 mmol/kg and, then 45 minutes later, gadoxetic acid at a dose of 0.025 mmol/kg.The following qualitative parameters were assessed using 3- and 5-point Likert scales (3 and 5 being the highest scores respectively): image quality (IQ), arterial and venous vessel delineation, tumor enhancement, motion artifacts, and sequence-specific artifacts. Furthermore, the following quantitative parameters were obtained: relative peak signal enhancement, time to peak, mean transit time, and plasma flow ratios. Paired sampled t tests and Wilcoxon signed rank tests were used for intraindividual comparison. Image analysis was performed by 2 radiologists.

RESULTS

Six of 7 animals underwent the full imaging protocol and obtained data were analyzed statistically. Overall IQ was rated moderate to excellent, not differing significantly between the 2 sequences.Gadobutrol-enhanced CS-VIBE examinations revealed the highest mean Likert scale values in terms of vessel delineation and tumor enhancement (arterial 4.4 [4-5], venous 4.3 [3-5], and tumor 2.9 [2-3]). Significantly, more sequence-specific artifacts were seen in GRASP examinations (P = 0.008-0.031). However, these artifacts did not impair IQ. Excellent Likert scale ratings were found for motion artifacts in both sequences. In both sequences, a maximum of 4 hepatic arterial dominant phases were obtained. Regarding the relative peak signal enhancement, CS-VIBE and GRASP showed similar results. The relative peak signal enhancement values did not differ significantly between the 2 sequences in the aorta, the hepatic artery, or the inferior vena cava (P = 0.063-0.536). However, significantly higher values were noted for CS-VIBE in gadoxetic acid-enhanced examinations in the portal vein (P = 0.031) and regarding the tumor enhancement (P = 0.005). Time to peak and mean transit time or plasma flow ratios did not differ significantly between the sequences.

CONCLUSIONS

Both CS-VIBE and GRASP provide excellent results in dynamic liver MRI using extracellular and hepatocyte-specific contrast agents, in terms of IQ, peak signal intensity, and presence of artifacts.

Spatiotemporal Flexible Sparse Reconstruction for Rapid Dynamic Contrast-enhanced MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques combining compressed sensing (CS) and parallel imaging with golden-angle radial sampling have been developed to improve motion robustness with high spatial and temporal resolution. These methods have demonstrated good diagnostic performance in clinical setting, but the reconstruction quality will degrade at high acceleration rates and overall reconstruction time remains long. In this paper, we proposed a new parallel CS reconstruction model for DCE-MRI that enforces flexible weighted sparse constraint along both spatial and temporal dimensions. Weights were introduced to flexibly adjust the importance of time and space sparsity, and we derived a fast-thresholding algorithm which was proven to be simple and efficient for solving the proposed reconstruction model. Results on both the brain tumor DCE and liver DCE show that, at relatively high acceleration factor of fast sampling, lowest reconstruction error and highest image structural similarity are obtained by the proposed method. Besides, the proposed method achieves faster reconstruction for liver datasets and better physiological measures are also obtained on tumor images.

Investigating difficult to detect pancreatic lesions: Characterization of benign pancreatic islet cell tumors using multiparametric pancreatic 3-T MRI

Introduction

Pancreatic islet-cell tumors (PICT) often present with atypical signal-characteristics and are often missed on preoperative imaging. The aim of this study is to provide a multiparametric PICT characterization and investigate factors impeding PICT detection.

Material and methods

This is a detailed MRI analysis of a prospective, monocenter study, including 49 consecutive patients (37 female, 12 male; median age 50) with symptoms due to endogenous hyperinsulinemic hypoglycemia (EHH) and mostly negative prior-imaging. All patients received a 3-T MRI and a ⁶⁸Ga-DOTA-exendin-4-PET/CT. Pooled accuracy, sensitivity, specificity and inter-reader agreement were calculated. Reference-standard was histopathology and ⁶⁸Ga-DOTA-Exendin-4-PET/CT in one patient who refused surgery. For PICT analyses, 34 patients with 49 PICTs (48 histologically proven; one ⁶⁸Ga-DOTA-exendin-4-PET/CT positive) were assessed. Dynamic contrast-enhanced (DCE) Magnetic Resonance Images (MRI) with Golden-Angle-Radial-Sparse-Parallel (GRASP) reconstruction, enabling imaging at high spatial and temporal resolution, was used to assess enhancement-patterns of PICTs. Tumor-to-background (T2B) ratio for each sequence and the employed quantitative threshold for conspicuity of PICTs were analyzed in regard to prediction of true-positive PICTs.

Results

Evaluation of 49 patients revealed a pooled lesion-based accuracy, sensitivity and specificity of 70.3%, 72.9% and 62.5%, respectively. Mean PICT size was 12.9±5.3mm for detected, 9.0±2.9mm for undetected PICTs (p-value 0.0112). In-phase T1w detected the most PICT (67.3%). Depending on the sequence, PICTs were isointense and poorly visible in 29–68%. Only 2/41(4.9%) PICTs showed typical signal-characteristics across T1w, T2w, DWI and ceT1w combined. 66.6% of PICTs enhanced simultaneously to the parenchyma, 17.8% early and 15.6% late. Predictor screening analysis showed number of sequences detecting a PICT, lesion size and in-phase T1w T2B ratio had the highest contribution for detecting a true-positive PICT.

Conclusion

The majority of PICTs enhance simultaneously to surrounding parenchyma, present with atypical signal-characteristics and thus are poorly visible. In non-enhancing PICTs, radiologists should search for small lesions most likely conspicuous on unenhanced T1w or DWI.

Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma

Background

Advances in sub-Nyquist–sampled dynamic contrast-enhanced (DCE) MRI enable monitoring of brain tumors with millimeter resolution and whole-brain coverage. Such undersampled quantitative methods need careful characterization regarding achievable test-retest reproducibility.

Purpose

To demonstrate a fully automated high-resolution whole-brain DCE MRI pipeline with 30-fold sparse undersampling and estimate its reproducibility on the basis of reference regions of stable tissue types during multiple posttreatment time points by using longitudinal clinical images of high-grade glioma.

Materials and Methods

Two methods for sub-Nyquist–sampled DCE MRI were extended with automatic estimation of vascular input functions. Continuously acquired three-dimensional k-space data with ramped-up flip angles were partitioned to yield high-resolution, whole-brain tracer kinetic parameter maps with matched precontrast-agent T1 and M₀ maps. Reproducibility was estimated in a retrospective study in participants with high-grade glioma, who underwent three consecutive standard-of-care examinations between December 2016 and April 2019. Coefficients of variation and reproducibility coefficients were reported for histogram statistics of the tracer kinetic parameters plasma volume fraction and volume transfer constant (K^trans) on five healthy tissue types.

Results

The images from 13 participants (mean age ± standard deviation, 61 years ± 10; nine women) with high-grade glioma were evaluated. In healthy tissues, the protocol achieved a coefficient of variation less than 57% for median K^trans, if K^trans was estimated consecutively. The maximum reproducibility coefficient for median K^trans was estimated to be at 0.06 min^–1 for large or low-enhancing tissues and to be as high as 0.48 min^–1 in smaller or strongly enhancing tissues.

Conclusion

A fully automated, sparsely sampled DCE MRI reconstruction with patient-specific vascular input function offered high spatial and temporal resolution and whole-brain coverage; in healthy tissues, the protocol estimated median volume transfer constant with maximum reproducibility coefficient of 0.06 min^–1 in large, low-enhancing tissue regions and maximum reproducibility coefficient of less than 0.48 min^–1 in smaller or more strongly enhancing tissue regions.

Published under a CC BY 4.0 license.

Online supplemental material is available for this article.

See also the editorial by Lenkinski in this issue.

Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI

PURPOSE

Current breast DCE-MRI strategies provide high sensitivity for cancer detection but are known to be insufficient in fully capturing rapidly changing contrast kinetics at high spatial resolution across both breasts. Advanced acquisition and reconstruction strategies aim to improve spatial and temporal resolution and increase specificity for disease characterization. In this work, we evaluate the spatial and temporal fidelity of a modified data-driven low-rank-based model (known as MOCCO, model consistency condition) compressed-sensing (CS) reconstruction compared to CS with temporal total variation with radial acquisition for high spatial-temporal breast DCE MRI.

METHODS

Reconstruction performance was characterized using numerical simulations of a golden-angle stack-of-stars breast DCE-MRI acquisition at 5-second temporal resolution. Specifically, MOCCO was compared with CS total variation and conventional SENSE reconstructions. The temporal model for MOCCO was prelearned over the source data, whereas CS total variation was performed using a first-order temporal gradient sparsity transform.

RESULTS

The MOCCO reconstruction was able to capture rapid lesion kinetics while providing high image quality across a range of optimal regularization values. It also recovered kinetics in small lesions (1.5 mm) in line-profile analysis and error images, whereas g-factor maps showed relatively low and constant values with no significant artifacts. The CS-TV method demonstrated either recovery of high spatial resolution with reduced temporal accuracy using large regularization values, or recovery of rapid lesion kinetics with reduced image quality using low regularization values.

CONCLUSION

Simulations demonstrated that MOCCO with radial acquisition provides a robust imaging technique for improving temporal fidelity, while maintaining high spatial resolution and image quality in the setting of bilateral breast DCE MRI.

Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula

BACKGROUND AND PURPOSE

4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times.

METHODS

Two 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study.

RESULTS

For 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images.

CONCLUSION

Our results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.

Three-dimensional assessment of brain arterial compliance: Technical development, comparison with aortic pulse wave velocity, and age effect

PURPOSE

The ability to measure cerebral vascular compliance (VC) is important in the evaluation of vascular diseases. Additionally, quantification of arterial wall pulsation in the brain may be useful for understanding the driving force of the recently discovered glymphatic system. Our goal is to develop an MRI technique to measure VC and arterial wall pulsation in major intracranial vessels.

METHODS

A total of 17 healthy subjects were studied on a 3T MRI system. The technique, called VaCom-PCASL, uses pseudo-continuous arterial spin labeling (PCASL) to obtain pure blood vessel signal, uses a 3D radial acquisition, and applies a golden-angle radial sparse parallel (GRASP) algorithm for image reconstruction. The k-space data were retrospectively sorted into different cardiac phases. The GRASP algorithm allows the reconstruction of 5D (three spatial dimensions, one control/label dimension, and one cardiac-phase dimension) data simultaneously. The proposed technique was optimized in terms of reconstruction parameters and labeling duration. Intracranial VC was compared with aortic pulse wave velocity measured with phase-contrast MRI. Age differences in VC were studied.

RESULTS

The VaCom-PCASL technique using 10 cardiac phases and GRASP sparsity constraints of λ_{label/control} = 0.05 and λ_cardiac = 0.05 provided the highest contrast-to-noise ratio. A labeling duration of 800 ms was found to yield signals comparable to those of longer duration (P > .2), whereas 400 ms yielded significant overestimation (P < .005). A significant correlation was observed between intracranial VC and aortic pulse wave velocity (r = -0.73, P = .038, N = 8). Vascular compliance in the older group was lower than that in the younger group.

CONCLUSION

The VaCom-PCASL-MRI technique represents a promising approach for noninvasive assessment of arterial stiffness and pulsatility.

Phase contrast coronary blood velocity mapping with both high temporal and spatial resolution using triggered Golden Angle rotated Spiral k-t Sparse Parallel imaging (GASSP) with shifted binning

PURPOSE

High temporal and spatial resolutions are required for coronary blood flow measures. Current spiral breath-hold phase contrast (PC) MRI at 3T focus on either high spatial or high temporal resolution. We propose a golden angle (GA) rotated Spiral k-t Sparse Parallel imaging (GASSP) sequence for both high spatial (0.8 mm) and high temporal (<21 ms) resolutions.

METHODS

GASSP PC data are acquired in left anterior descending and right coronary arteries of eight healthy subjects. Binning of GA rotated spiral data into cardiac frames may lead to large k-space gaps. To reduce those gaps, the binning window is shifted and a triggered GA scheme that resets the rotation angle every heartbeat is proposed. The gap reductions are evaluated in simulations and all subjects. Peak systolic velocity (PSV), peak diastolic velocity (PDV), coronary blood flow rate, and vessel area are validated against two reference scans, and repeatability/reproducibility are determined.

RESULTS

Shifted binning reduced the mean k-space gaps of the triggered GA scheme by 14°-22° in simulations and about 20° in vivo. The k-space gap across three cardiac frames was reduced with the triggered GA scheme compared to the standard GA scheme (35.3°± 3.6° vs. 43°± 13.7°, t-test P = .04). PSV, PDV, flow rate, and area had high intra-scan repeatability (0.92 ≤ intraclass correlation coefficient [ICC] ≤ 0.99), and inter-scan (0.78 ≤ ICC ≤ 0.91) and intra-observer (0.91 ≤ ICC ≤ 0.98) reproducibility.

CONCLUSION

GASSP enables single breath-hold coronary PC MRI with high temporal and spatial resolutions. Shifted binning and a triggered GA scheme reduce k-space gaps. Quantitative coronary flow metrics are highly reproducible, especially within the same scanning session.

Real-time dynamic vocal tract imaging using an accelerated spiral GRE sequence and low rank plus sparse reconstruction

PURPOSE

To develop a real-time dynamic vocal tract imaging method using an accelerated spiral GRE sequence and low rank plus sparse reconstruction.

METHODS

Spiral k-space sampling has high data acquisition efficiency and thus is suited for real-time dynamic imaging; further acceleration can be achieved by undersampling k-space and using a model-based reconstruction. Low rank plus sparse reconstruction is a promising method with fast computation and increased robustness to global signal changes and bulk motion, as the images are decomposed into low rank and sparse terms representing different dynamic components. However, the combination with spiral scanning has not been well studied. In this study an accelerated spiral GRE sequence was developed with an optimized low rank plus sparse reconstruction and compared with L1-SPIRiT and XD-GRASP methods. The off-resonance was also corrected using a Chebyshev approximation method to reduce blurring on a frame-by-frame basis.

RESULTS

The low rank plus sparse reconstruction method is sensitive to the weights of the low rank and sparse terms. The optimized reconstruction showed advantages over other methods with reduced aliasing and improved SNR. With the proposed method, spatial resolution of 1.3*1.3 mm² with 150 mm field-of-view (FOV) and temporal resolution of 30 frames-per-second (fps) was achieved with good image quality. Blurring was reduced using the Chebyshev approximation method.

CONCLUSION

This work studies low rank plus sparse reconstruction using the spiral trajectory and demonstrates a new method for dynamic vocal tract imaging which can benefit studies of speech disorders.

Application of a Novel Iterative Denoising and Image Enhancement Technique in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of the Abdomen: Improvement of Image Quality and Diagnostic Confidence

OBJECTIVES

The aim of this study was to investigate the impact of a novel iterative denoising and image enhancement technique in T1-weighted precontrast and postcontrast volume-interpolated breath-hold examination (VIBE) of the abdomen on image quality, noise levels, and diagnostic confidence without change of acquisition parameters.

MATERIALS AND METHODS

Fifty patients were included in this retrospective, monocentric, institutional review board-approved study after clinically indicated magnetic resonance imaging of the abdomen including T1-weighted precontrast and postcontrast imaging. After acquisition of the standard VIBE (VIBES), images were processed with a novel reconstruction algorithm using the same raw data as for VIBES, resulting in a denoised and enhanced dataset (VIBEDE). Two different radiologists evaluated both datasets in a randomized order regarding sharpness of organs as well as vessels, noise levels, artifacts, overall image quality, and diagnostic confidence using a Likert scale ranging from 1 to 4 with 4 being the best. Furthermore, in the presence of focal liver lesions, the largest lesion was measured in the postcontrast dataset, and lesion detectability was analyzed using a Likert scale (1-4).

RESULTS

Precontrast and postcontrast sharpness of organs and sharpness of vessels were rated significantly superior by both readers in VIBEDE with a median of 4 (interquartile range, 0) compared with VIBES with a median of 3 (1) (all P's < 0.0001). Precontrast and postcontrast noise levels were also rated superior by both readers in VIBEDE with a median of 4 (0) compared with VIBES with a median of 3 (1) for precontrast and a median of 3 (0) (median of 3 [1] for reader 2) for postcontrast imaging (all P's < 0.0001).Overall image quality was also rated higher with a median of 4 (0) in VIBEDE versus 3 (1) in VIBES (P < 0.0001). Twenty-seven imaging studies contained liver lesions. There was no difference regarding the number and localization between the readers and between VIBES and VIBEDE. Lesion detectability was rated by both readers significantly better in VIBEDE with a median of 4 (0) compared with a median of 4 (1) for reader 1 and a median of 3 (1) for reader 2 (P = 0.001 for reader 1; P < 0.001 for reader 2). Consequently, diagnostic confidence was also significantly superior in VIBEDE versus VIBES with a median of 4 (0) for both (P = 0.001). Interreader agreement resulted in a Cohen κ of 0.76 for precontrast analysis as well as of 0.76 for postcontrast analysis.

CONCLUSIONS

Application of a novel iterative denoising and image enhancement technique in T1-weighted VIBE precontrast and postcontrast imaging of the abdomen is feasible, providing superior image quality, noise levels, and diagnostic confidence.

Morphological and functional assessment of the uterus: "one-stop shop imaging" using a compressed-sensing accelerated, free-breathing T1-VIBE sequence

BACKGROUND

The combination of motion-insensitive, high-temporal, and spatial resolution imaging with evaluation of quantitative perfusion has the potential to increase the diagnostic capabilities of magnetic resonance imaging (MRI) in the female pelvis.

PURPOSE

To compare a free-breathing compressed-sensing VIBE (fbVIBE) with flexible temporal resolution (range = 4.6-13.8 s) with breath-hold VIBE (bhVIBE) and to evaluate the potential value of quantifying uterine perfusion.

MATERIAL AND METHODS

A total of 70 datasets from 60 patients (bhVIBE: n = 30; fbVIBE: n = 40) were evaluated by two radiologists. Only temporally resolved reconstruction (fbVIBE) was performed on 30 of the fbVIBE datasets. For a subset (n = 10) of the fbVIBE acquisitions, a time- and motion-resolved reconstruction (mrVIBE) was evaluated. Image quality (IQ), artifacts, diagnostic confidence (DC), and delineation of uterine structures (DoS) were graded on Likert scales (IQ/DC/DoS: 1 (non-diagnostic) to 5 (perfect); artifacts: 1 (no artifacts) to 5 (severe artifacts)). A Tofts model was applied for perfusion analysis. Ktrans was obtained in the myometrium (Mm), junctional zone (Jz), and cervix (Cx).

RESULTS

The median IQ/DoS/DC scores of fbVIBE (4/5/5 κ >0.7-0.9) and bhVIBE (4/4/4; κ = 0.5-0.7; P > 0.05) were high, but Artifacts were graded low (fbVIBE/bhVIBE: 2/2; κ = 0.6/0.5; P > 0.05). Artifacts were only slightly improved by the additional motion-resolved reconstruction (fbVIBE/mrVIBE: 2/1.5; P = 0.08); fbVIBE was preferred in most cases (7/10). Significant differences of Ktrans values were found between Cx, Jz, and Mm (0.12/0.21/0.19; P < 0.05).

CONCLUSION

The fbVIBE sequence allows functional and morphological assessment of the uterus at comparable IQ to bhVIBE.

Elastic AlignedSENSE for Dynamic MR Reconstruction: A Proof of Concept in Cardiac Cine

Numerous methods in the extensive literature on magnetic resonance imaging (MRI) reconstruction exploit temporal redundancy to accelerate cardiac cine. Some of them include motion compensation, which involves high computational costs and long runtimes. In this work, we proposed a method—elastic alignedSENSE (EAS)—for the direct reconstruction of a motion-free image plus a set of nonrigid deformations to reconstruct a 2D cardiac sequence. The feasibility of the proposed approach was tested in 2D Cartesian and golden radial multi-coil breath-hold cardiac cine acquisitions. The proposed approach was compared against parallel imaging compressed sense (sPICS) and group-wise motion corrected compressed sense (GWCS) reconstructions. EAS provides better results on objective measures with considerable less runtime when an acceleration factor is higher than 10×. Subjective assessment of an expert, however, invited proposing the combination of EAS and GWCS as a preferable alternative to GWCS or EAS in isolation.

Radial Undersampling-Based Interpolation Scheme for Multislice CSMRI Reconstruction Techniques

Magnetic Resonance Imaging (MRI) is an important yet slow medical imaging modality. Compressed sensing (CS) theory has enabled to accelerate the MRI acquisition process using some nonlinear reconstruction techniques from even 10% of the Nyquist samples. In recent years, interpolated compressed sensing (iCS) has further reduced the scan time, as compared to CS, by exploiting the strong interslice correlation of multislice MRI. In this paper, an improved efficient interpolated compressed sensing (EiCS) technique is proposed using radial undersampling schemes. The proposed efficient interpolation technique uses three consecutive slices to estimate the missing samples of the central target slice from its two neighboring slices. Seven different evaluation metrics are used to analyze the performance of the proposed technique such as structural similarity index measurement (SSIM), feature similarity index measurement (FSIM), mean square error (MSE), peak signal to noise ratio (PSNR), correlation (CORR), sharpness index (SI), and perceptual image quality evaluator (PIQE) and compared with the latest interpolation techniques. The simulation results show that the proposed EiCS technique has improved image quality and performance using both golden angle and uniform angle radial sampling patterns, with an even lower sampling ratio and maximum information content and using a more practical sampling scheme.

Diagnostic Performance of a Lower-dose Contrast-Enhanced 4D Dynamic MR Angiography of the Lower Extremities at 3 T Using Multisegmental Time-Resolved Maximum Intensity Projections

BACKGROUND

For peripheral artery disease (PAD), MR angiography (MRA) is a well-established diagnostic modality providing morphologic and dynamic information comparable to digital subtraction angiography (DSA). However, relatively large amounts of contrast agents are necessary to achieve this.

PURPOSE

To evaluate the diagnostic accuracy of time-resolved 4D MR-angiography with interleaved stochastic trajectories (TWIST-MRA) by using maximum intensity projections (MIPs) of dynamic images acquired with reduced doses of contrast agent.

STUDY TYPE

Retrospective.

POPULATION

Forty adult PAD patients yielding 1088 artery segments.

FIELD STRENGTH/SEQUENCE

A 3.0 T, time-resolved 4D MR-angiography with TWIST-MRA and MIP of dynamic images.

ASSESSMENT

DSA was available in 14 patients (256 artery segments) and used as reference standard. Three-segmental MIP reconstructions of TWIST-images after administration of 3 mL of gadolinium-based contrast agent (Gadoteridol/Prohance®, 0.5 M) per anatomical level (pelvis, thighs, and lower legs) yielded 256 artery segments for correlation between MRA and DSA. Three independent observers rated image quality (scale: 1 [nondiagnostic] to 4 [excellent]) and the degree of venous overlay (scale: 0 [none] to 2 [significant]) for all segments. Diagnostic accuracy for the detection of >50% stenosis and artery occlusion was calculated for all observers.

STATISTICAL TESTS

Binary classification test (sensitivity, specificity, positive/negative predictive values, diagnostic accuracy). Intraclass correlation coefficients (ICCs), logistic regression analysis with comparison of areas under the receiver-operating-characteristics (ROC) curves (AUCs) with the DeLong method. Bland-Altman-comparison.

RESULTS

High diagnostic performance was achieved for the detection of >50% stenosis (sensitivity 92.9% [84.3-99.9% (95%-CI)] and specificity 98.5% [95.7-99.8% (95%-CI)]) and artery occlusion (sensitivity 93.1% [77.2-99.2% (95%-CI)] and specificity 99.1% [96.9-99.9% (95%-CI)]). Inter-reader agreement was excellent with ICC values ranging from 0.95 to 1.0 for >50% artery stenosis and occlusion. Image quality was good to excellent for both readers (3.41 ± 0.72, 3.33 ± 0.65, and 3.38 ± 0.61 [mean ± SD]) with good correlation between observer ratings (ICC 0.71-0.81). No significant venous overlay was observed (0.06 ± 0.24, 0.23 ± 0.43 and 0.11 ± 0.45 [mean ± SD]).

DATA CONCLUSION

MIPs of dynamic TWIST-MRA offer a promising diagnostic alternative necessitating only reduced amounts (50%) of gadolinium-based contrast agents for the entire runoff vasculature.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Free-breathing contrast-enhanced upper abdominal MRI in children: comparison between Cartesian acquisition and stack-of-stars acquisition with two different fat-suppression techniques

BACKGROUND

Respiratory artifacts impair image quality of magnetic resonance imaging (MRI) in children who cannot hold breath during MRI examination.

PURPOSE

To compare the quality of free-breathing contrast-enhanced 3D T1-weighted (T1W) images of the upper abdomen in children using Cartesian acquisition (Cartesian eTHRIVE), stack-of-stars acquisition with spectral fat suppression (3D VANE eTHRIVE), and stack-of-stars acquisition with fat suppression using modified Dixon (3D VANE mDixon).

MATERIAL AND METHODS

Pediatric patients (aged <19 years) who underwent whole-body MRI with free-breathing contrast-enhanced T1W axial scans of upper abdomen using Cartesian eTHRIVE, 3D VANE eTHRIVE, and 3D VANE mDixon were enrolled. Image quality parameters were assessed including overall image quality, hepatic edge sharpness, hepatic vessel clarity, respiratory artifacts, radial artifacts, lesion conspicuity, and lesion edge sharpness using the Likert scale, where a lower score indicated poorer image quality. The coefficients of variation of signal intensity of liver and spleen were analyzed.

RESULTS

In 41 patients, 3D VANE eTHRIVE showed the highest scores for all image quality parameters (P ≤ 0.001). 3D VANE eTHRIVE also showed higher scores for respiratory (P ≤ 0.001) and radial artefacts than 3D VANE mDixon (P = 0.001). There were no significant differences in coefficients of variation of signal intensity of the liver and spleen between 3D VANE eTHRIVE and 3D VANE mDixon. Acquisition time was longer for 3D VANE eTHRIVE (81.26 ± 16 s) than for Cartesian eTHRIVE (7.87 ± 0.95 s) and 3D VANE mDixon (76.66 ± 12.4 s, P < 0.001).

CONCLUSION

The application of stack-of-stars acquisition to 3D T1W abdominal MRI resulted in better image quality than Cartesian acquisition in free-breathing children. In stack-of-stars acquisition, spectral fat suppression resulted in better image quality and fewer artifacts than mDixon.

DEEP GENERATIVE STORM MODEL FOR DYNAMIC IMAGING

We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent. We penalize the norm of the gradient of the generator to encourage the learning of a smooth surface/manifold, while temporal gradients of the latent vectors are penalized to encourage the time series to be smooth. The main benefits of the proposed scheme are (a) the quite significant reduction in memory demand compared to the analysis based SToRM model, and (b) the spatial regularization brought in by the CNN model. We also introduce efficient progressive approaches to minimize the computational complexity of the algorithm.

SpiNet: A deep neural network for Schatten p-norm regularized medical image reconstruction

PURPOSE

To propose a generic deep learning based medical image reconstruction model (named as SpiNet) that can enforce any Schatten p-norm regularization with 0 < p ≤ 2, where the p can be learnt (or fixed) based on the problem at hand.

METHODS

Model-based deep learning architecture for solving inverse problems consists of two parts, a deep learning based denoiser and an iterative data consistency solver. The former has either L2 norm or L1 norm enforced on it, which are convex and can be easily minimized. This work proposes a method to enforce any p norm on the noise prior where 0 < p ≤ 2. This is achieved by using Majorization-Minimization algorithm, which upper bounds the cost function with a convex function, thus can be easily minimized. The proposed SpiNet has the capability to work for a fixed p or it can learn p based on the data. The network was tested for solving the inverse problem of reconstructing magnetic resonance (MR) images from undersampled k space data and the results were compared with a popular model-based deep learning architecture MoDL which enforces L2 norm along with other compressive sensing-based algorithms. This comparison between MoDL and proposed SpiNet was performed for undersampling rates (R) of 2×, 4×, 6×, 8×, 12×, 16×, and 20×. Multiple figures of merit such as PSNR, SSIM, and NRMSE were utilized in this comparison. A two-tailed t test was performed for all undersampling rates and for all metrices for proving the superior performance of proposed SpiNet compared to MoDL. For training and testing, the same dataset that was utilized in MoDL implementation was deployed.

RESULTS

The results indicate that for all undersampling rates, the proposed SpiNet shows higher PSNR and SSIM and lower NRMSE than MoDL. However, for low undersampling rates of 2× and 4×, there is no significant difference in performance of proposed SpiNet and MoDL in terms of PSNR and NRMSE. This can be expected as the learnt p value is close to 2 (norm enforced by MoDL). For higher undersampling rates ≥6×, SpiNet significantly outperforms MoDL in all metrices with improvement as high as 4 dB in PSNR and 0.5 points in SSIM.

CONCLUSION

As deep learning based medical image reconstruction methods are gaining popularity, the proposed SpiNet provides a generic framework to incorporate Schatten p-norm regularization with 0 <p ≤ 2 with an added advantage of providing superior performance compared to its counterparts. The proposed SpiNet also has useful addition of Schatten p-norm value in regularization term being automatically chosen based on the available training data.

Measurement of Three-Dimensional Internal Dynamic Strains in the Intervertebral Disc of the Lumbar Spine With Mechanical Loading and Golden-Angle Radial Sparse Parallel-Magnetic Resonance Imaging

BACKGROUND

Noninvasive measurement of internal dynamic strain can be potentially useful to characterize spine intervertebral disc (IVD) in the setting of injury or degenerative disease.

PURPOSE

To develop and demonstrate a noninvasive technique to quantify three-dimensional (3D) internal dynamic strains in the IVD using a combination of static mechanical loading of the IVD using a magnetic resonance imaging (MRI)-compatible ergometer.

STUDY TYPE

Prospective.

SUBJECTS

Silicone gel phantom studies were conducted to assess strain variation with load and repeatability. Mechanical testing was done on the phantoms to confirm MR results. Eight healthy human volunteers (four men and four woman, age = 29 ± 5 years) underwent MRI using a rest, static loading, and recovery paradigm. Repeatability tests were conducted in three subjects.

FIELD STRENGTH/SEQUENCE

MRI (3 T) with 3D continuous golden-angle radial sparse parallel (GRASP) and compressed sensing (CS) reconstruction.

ASSESSMENT

CS reconstruction of the images, motion deformation, and Lagrangian strain maps were calculated for five IVD segments from L1/L2 to L5/S1.

STATISTICAL TESTS

Ranges of displacement and strain in each subject and the resulting mean and standard deviation were calculated. Student t-tests were used to calculate changes in strain from loading to recovery. The correlation coefficient (CC) in the repeatability study was calculated.

RESULTS

The most compressive strain experienced by the IVD segments under loaded conditions was in the L4/L5 segment (-7.5 ± 2.9%). The change in minimum strain from load to recovery was the most for the L4/L5 segment (-7.5% to -5.0%, P = 0.026) and the least for the L1/L2 segment (-4.4% to -3.9%, P = 0.51). In vivo repeatability in three subjects shows strong correlation between scans in subjects done 6 months apart, with CCs equal to 0.86, 0.94, and 0.94 along principal directions.

DATA CONCLUSION

This study shows the feasibility of using static mechanical loading with continuous GRASP-MRI acquisition with CS reconstruction to measure 3D internal dynamic strains in the spine IVD.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Effects of k-space orders on the time-intensity curves in dynamic contrast-enhanced magnetic resonance imaging of the breast based on simulation study

PURPOSE

This study aims to investigate the influence of time-intensity curves (TICs) on the shapes using a dynamic contrast-enhanced magnetic resonance imaging (MRI) study depending on the Cartesian and radial orders for benign and cancerous breast tumors.

METHODS

Based on kinetic curve parameters, the signal intensities of six concentration gradients comprising two benign and four cancer models were used. The study aimed to construct a dynamic simulated image by creating a digital phantom image according to the following steps: (1) creating a simple numerical phantom, (2) setting the signal intensity in the contrast area, (3) creating the k-space in each time phase, (4) extracting data from k-space in each time phase, (5) filling in the k-space and adding data to the k-space assembly, and (6) creating a magnitude image. The TICs of Cartesian (centric and sequential) and radial (full-length [RFL] and half-length [RHL]) orders were created and sigmoid curve fitting was performed to compare these curves. Maximum slope (MS, s^-1), width of the response (WOR, s), and primary signal response (PSR) were then calculated. Phase encode steps were set for 512 and 256.

RESULTS

MS was significantly decreased by radial order in the cancer model. No change was observed in WOR in Cartesian order, whereas RFL and RHL orders increased in the cancer models. PSR increased remarkably in the radial orders of cancer models. The difference in the fill slope in radial orders was remarkable when the TIC was steeper compared with when it was gentle, especially RHL. In WOR, both radial RFL and RHL were well matched except for the one benign model, and the shape of radial TIC was similar to sequential order as compared to centric order in 256 steps.

CONCLUSION

The effects of Cartesian and radial orders on the patterns of TICs in a dynamic contrast-enhanced MRI study of benign and cancerous breast tumors were revealed. Interestingly, the TIC gradient of radial orders became gentler, particularly in the breast cancer MRI.

High spatial resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage at 3 T

PURPOSE

To develop and evaluate a high spatial resolution (1.25 × 1.25 mm² ) spiral first-pass myocardial perfusion imaging technique with whole-heart coverage at 3T, to better assess transmural differences in perfusion between the endocardium and epicardium, to quantify the myocardial ischemic burden, and to improve the detection of obstructive coronary artery disease.

METHODS

Whole-heart high-resolution spiral perfusion pulse sequences and corresponding motion-compensated reconstruction techniques for both interleaved single-slice (SS) and simultaneous multi-slice (SMS) acquisition with or without outer-volume suppression (OVS) were developed. The proposed techniques were evaluated in 34 healthy volunteers and 8 patients (55 data sets). SS and SMS images were reconstructed using motion-compensated L1-SPIRiT and SMS-Slice-L1-SPIRiT, respectively. Images were blindly graded by 2 experienced cardiologists on a 5-point scale (5, excellent; 1, poor).

RESULTS

High-quality perfusion imaging was achieved for both SS and SMS acquisitions with or without OVS. The SS technique without OVS had the highest scores (4.5 [4, 5]), which were greater than scores for SS with OVS (3.5 [3.25, 3.75], P < .05), MB = 2 without OVS (3.75 [3.25, 4], P < .05), and MB = 2 with OVS (3.75 [2.75, 4], P < .05), but significantly higher than those for MB = 3 without OVS (4 [4, 4], P = .95). SMS image quality was improved using SMS-Slice-L1-SPIRiT as compared to SMS-L1-SPIRiT (P < .05 for both reviewers).

CONCLUSION

We demonstrated the successful implementation of whole-heart spiral perfusion imaging with high resolution at 3T. Good image quality was achieved, and the SS without OVS showed the best image quality. Evaluation in patients with expected ischemic heart disease is warranted.

Accelerated radial echo-planar spectroscopic imaging using golden angle view-ordering and compressed-sensing reconstruction with total variation regularization

PURPOSE

To implement a novel, accelerated, 2D radial echo-planar spectroscopic imaging (REPSI) sequence using undersampled radial k-space trajectories and compressed-sensing reconstruction, and to compare results with those from an undersampled Cartesian spectroscopic sequence.

METHODS

The REPSI sequence was implemented using golden-angle view-ordering on a 3T MRI scanner. Radial and Cartesian echo-planar spectroscopic imaging (EPSI) data were acquired at six acceleration factors, each with time-equivalent scan durations, and reconstructed using compressed sensing with total variation regularization. Results from prospectively and retrospectively undersampled phantom and in vivo brain data were compared over estimated concentrations and Cramer-Rao lower-bound values, normalized RMS errors of reconstructed metabolite maps, and percent absolute differences between fully sampled and reconstructed spectroscopic images.

RESULTS

The REPSI method with compressed sensing is able to tolerate greater reductions in scan time compared with EPSI. The reconstruction and quantitation metrics (i.e., spectral normalized RMS error maps, metabolite map normalized RMS error values [e.g., for total N-acetyl asparate, REPSI = 9.4% vs EPSI = 16.3%; acceleration factor = 2.5], percent absolute difference maps, and concentration and Cramer-Rao lower-bound estimates) showed that accelerated REPSI can reduce the scan time by a factor of 2.5 while retaining image and quantitation quality.

CONCLUSION

Accelerated MRSI using undersampled radial echo-planar acquisitions provides greater reconstruction accuracy and more reliable quantitation for a range of acceleration factors compared with time-equivalent compressed-sensing reconstructions of undersampled Cartesian EPSI. Compared to the Cartesian approach, radial undersampling with compressed sensing could help reduce 2D spectroscopic imaging acquisition time, and offers a better trade-off between imaging speed and quality.

A guaranteed convergence analysis for the projected fast iterative soft-thresholding algorithm in parallel MRI

Sparse sampling and parallel imaging techniques are two effective approaches to alleviate the lengthy magnetic resonance imaging (MRI) data acquisition problem. Promising data recoveries can be obtained from a few MRI samples with the help of sparse reconstruction models. To solve the optimization models, proper algorithms are indispensable. The pFISTA, a simple and efficient algorithm, has been successfully extended to parallel imaging. However, its convergence criterion is still an open question. Besides, the existing convergence criterion of single-coil pFISTA cannot be applied to the parallel imaging pFISTA, which, therefore, imposes confusions and difficulties on users about determining the only parameter - step size. In this work, we provide the guaranteed convergence analysis of the parallel imaging version pFISTA to solve the two well-known parallel imaging reconstruction models, SENSE and SPIRiT. Along with the convergence analysis, we provide recommended step size values for SENSE and SPIRiT reconstructions to obtain fast and promising reconstructions. Experiments on in vivo brain images demonstrate the validity of the convergence criterion.

On the use of low-dimensional temporal subspace constraints to reduce reconstruction time and improve image quality of accelerated 4D-MRI

BACKGROUND AND PURPOSE

The purpose of this work is to investigate the use of low-dimensional temporal subspace constraints for 4D-MRI reconstruction from accelerated data in the context of MR-guided online adaptive radiation therapy (MRgOART).

MATERIALS AND METHODS

Subspace basis functions are derived directly from the accelerated golden angle radial stack-of-stars 4D-MRI data. The reconstruction times, image quality, and motion estimates are investigated as a function of the number of subspace coefficients and compared with a conventional frame-by-frame reconstruction. These experiments were performed in five patients with four 4D-MRI scans per patient on a 1.5T MR-Linac.

RESULTS

If two or three subspace coefficients are used, the iterative reconstruction time is reduced by 32% and 18%, respectively, compared to conventional parallel imaging with compressed sensing reconstructions. No significant difference was found between motion estimates made with the subspace-constrained reconstructions (p > 0.08). Qualitative improvements in image quality included reduction in apparent noise and reductions in streaking artifacts from the radial k-space coverage.

CONCLUSION

Incorporating subspace constraints for accelerated 4D-MRI reconstruction reduces noise and residual undersampling artifacts in the images while reducing computation time, making it a strong candidate for use in clinical MRgOART workflows.

Comparison of image quality of subtracted and nonsubtracted breath hold VIBE and free breathing GRASP in the evaluation of renal masses

OBJECTIVE

To compare the image quality of subtracted and nonsubtracted images obtained using volumetric interpolated breath-hold exam (VIBE) and free breathing T1 weighted Golden-angle Radial Sparse Parallel (GRASP).

METHODS

We retrospectively evaluated 27 consecutive patients who underwent MRI for the evaluation of renal masses. Contrast enhanced VIBE and free breathing GRASP imaging were performed, and subtraction images generated. Two radiologists performed quantitative and qualitative evaluations of image quality of nonsubtracted and subtracted data sets. Statistical analysis was performed using the Wilcoxon signed-rank test, paired t-test and kappa statistics.

RESULTS

VIBE images scored statistically higher for the following parameters in the coronal and axial plane: sharpness, streak artifact, image noise, and overall image quality for standard and subtracted images (all P values P < 0.001). GRASP images had significantly less subtraction artifact in the coronal (P = 0.042) plane with a similar trend in the axial plane (P = 0.079). Interreader Kappa values for qualitative images scores were fair to good (0.23-0.71). Quantitative subtracted GRASP images had significant less subtraction artifact compared to VIBE in the anterior-posterior (3.9 mm SD 2.6 mm versus 5.8 mm SD 3.6 mm, P = 0.010), and craniocaudal direction (4.4 mm SD 2.9 mm versus 7.0 mm SD 5.3 mm, P = 0.010); a trend was seen in the left-right direction (2.6 mm SD 1.4 mm versus 4.0 mm SD 3.9 mm, P = 0.084).

CONCLUSION

VIBE images have significantly better image quality than free breathing GRASP images, however free breathing GRASP images have significantly less subtraction artifact.

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an "end-to-end" reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×10-5) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods.

The Role of Magnetic Resonance Imaging in (Future) Cancer Staging: Note the Nodes

The presence or absence of lymph node metastases is a very important prognostic factor in patients with solid tumors. Current invasive and noninvasive diagnostic methods for N-staging like lymph node dissection, morphologic computed tomography/magnetic resonance imaging (MRI), or positron emission tomography-computed tomography have significant limitations because of technical, biological, or anatomical reasons. Therefore, there is a great clinical need for more precise, reliable, and noninvasive N-staging in patients with solid tumors. Using ultrasmall superparamagnetic particles of ironoxide (USPIO)-enhanced MRI offers noninvasive diagnostic possibilities for N-staging of different types of cancer, including the 4 examples given in this work (head and neck cancer, esophageal cancer, rectal cancer, and prostate cancer). The excellent soft tissue contrast of MRI and an USPIO-based differentiation of metastatic versus nonmetastatic lymph nodes can enable more precise therapy and, therefore, fewer side effects, essentially in cancer patients in oligometastatic disease stage. By discussing 3 important questions in this article, we explain why lymph node staging is so important, why the timing for more accurate N-staging is right, and how it can be done with MRI. We illustrate this with the newest developments in magnetic resonance methodology enabling the use of USPIO-enhanced MRI at ultrahigh magnetic field strength and in moving parts of the body like upper abdomen or mediastinum. For prostate cancer, a comparison with radionuclide tracers connected to prostate specific membrane antigen is made. Under consideration also is the use of MRI for improvement of ex vivo cancer diagnostics. Further scientific and clinical development is needed to assess the accuracy of USPIO-enhanced MRI of detecting small metastatic deposits for different cancer types in different anatomical locations and to broaden the indications for the use of (USPIO-enhanced) MRI in lymph node imaging in clinical practice.

Compressed sensing with signal averaging for improved sensitivity and motion artifact reduction in fluorine-19 MRI

Fluorine-19 (19 F) MRI of injected perfluorocarbon emulsions (PFCs) allows for the non-invasive quantification of inflammation and cell tracking, but suffers from a low signal-to-noise ratio and extended scan time. To address this limitation, we tested the hypotheses that a 19 F MRI pulse sequence that combines a specific undersampling regime with signal averaging has both increased sensitivity and robustness against motion artifacts compared with a non-averaged fully sampled pulse sequence, when both datasets are reconstructed with compressed sensing. As a proof of principle, numerical simulations and phantom experiments were performed on selected variable ranges to characterize the point spread function of undersampling patterns, as well as the vulnerability to noise of undersampling and reconstruction parameters with paired numbers of x signal averages and acceleration factor x (NAx-AFx). The numerical simulations demonstrated that a probability density function that uses 25% of the samples to fully sample the k-space central area allowed for an optimal balance between limited blurring and artifact incoherence. At all investigated noise levels, the Dice similarity coefficient (DSC) strongly depended on the regularization parameters and acceleration factor. In phantoms, the motion robustness of an NA8-AF8 undersampling pattern versus NA1-AF1 was evaluated with simulated and real motion patterns. Differences were assessed with the DSC, which was consistently higher for the NA8-AF8 compared with the NA1-AF1 strategy, for both simulated and real cyclic motion patterns (P < 0.001). Both strategies were validated in vivo in mice (n = 2) injected with perfluoropolyether. Here, the images displayed a sharper delineation of the liver with the NA8-AF8 strategy than with the NA1-AF1 strategy. In conclusion, we validated the hypotheses that in 19 F MRI the combination of undersampling and averaging improves both the sensitivity and the robustness against motion artifacts.

Modeling dynamic radial contrast enhanced MRI with linear time invariant systems for motion correction in quantitative assessment of kidney function

Early identification of kidney function deterioration is essential to determine which newborn patients with congenital kidney disease should be considered for surgical intervention as opposed to observation. Kidney function can be measured by fitting a tracer kinetic (TK) model onto a series of Dynamic Contrast Enhanced (DCE) MR images and estimating the filtration rate parameter from the model. Unfortunately, breathing and large bulk motion events due to patient movement in the scanner create outliers and misalignments that introduce large errors in the TK model parameter estimates even when using a motion-robust dynamic radial VIBE sequence for DCE-MR imaging. The misalignments between the series of volumes are difficult to correct using standard registration due to 1) the large differences in geometry and contrast between volumes of the dynamic sequence and 2) the requirement of fast dynamic imaging to achieve high temporal resolution and motion deteriorates image quality. These difficulties reduce the accuracy and stability of registration over the dynamic sequence. An alternative registration approach is to generate noise and motion free templates of the original data from the TK model and use them to register each volume to its contrast-matched template. However, the TK models used to characterize DCE-MRI are tissue specific, non-linear and sensitive to the same motion and sampling artifacts that hinder registration in the first place. Hence, these can only be applied to register accurately pre-segmented regions of interest, such as kidneys, and might converge to local minima under the presence of large artifacts. Here we introduce a novel linear time invariant (LTI) model to characterize DCE-MR data for different tissue types within a volume. We approximate the LTI model as a sparse sum of first order LTI functions to introduce robustness to motion and sampling artifacts. Hence, this model is well suited for registration of the entire field of view of DCE-MR data with artifacts and outliers. We incorporate this LTI model into a registration framework and evaluate it on both synthetic data and data from 20 children. For each subject, we reconstructed the sequence of DCE-MR images, detected corrupted volumes acquired during motion, aligned the sequence of volumes and recovered the corrupted volumes using the LTI model. The results show that our approach correctly aligned the volumes, provided the most stable registration in time and improved the tracer kinetic model fit.

Rapid reconstruction of highly undersampled, non-Cartesian real-time cine k-space data using a perceptual complex neural network (PCNN)

Highly accelerated real-time cine MRI using compressed sensing (CS) is a promising approach to achieve high spatio-temporal resolution and clinically acceptable image quality in patients with arrhythmia and/or dyspnea. However, its lengthy image reconstruction time may hinder its clinical translation. The purpose of this study was to develop a neural network for reconstruction of non-Cartesian real-time cine MRI k-space data faster (<1 min per slice with 80 frames) than graphics processing unit (GPU)-accelerated CS reconstruction, without significant loss in image quality or accuracy in left ventricular (LV) functional parameters. We introduce a perceptual complex neural network (PCNN) that trains on complex-valued MRI signal and incorporates a perceptual loss term to suppress incoherent image details. This PCNN was trained and tested with multi-slice, multi-phase, cine images from 40 patients (20 for training, 20 for testing), where the zero-filled images were used as input and the corresponding CS reconstructed images were used as practical ground truth. The resulting images were compared using quantitative metrics (structural similarity index (SSIM) and normalized root mean square error (NRMSE)) and visual scores (conspicuity, temporal fidelity, artifacts, and noise scores), individually graded on a five-point scale (1, worst; 3, acceptable; 5, best), and LV ejection fraction (LVEF). The mean processing time per slice with 80 frames for PCNN was 23.7 ± 1.9 s for pre-processing (Step 1, same as CS) and 0.822 ± 0.004 s for dealiasing (Step 2, 166 times faster than CS). Our PCNN produced higher data fidelity metrics (SSIM = 0.88 ± 0.02, NRMSE = 0.014 ± 0.004) compared with CS. While all the visual scores were significantly different (P < 0.05), the median scores were all 4.0 or higher for both CS and PCNN. LVEFs measured from CS and PCNN were strongly correlated (R² = 0.92) and in good agreement (mean difference = -1.4% [2.3% of mean]; limit of agreement = 10.6% [17.6% of mean]). The proposed PCNN is capable of rapid reconstruction (25 s per slice with 80 frames) of non-Cartesian real-time cine MRI k-space data, without significant loss in image quality or accuracy in LV functional parameters.

MRI-guided Radiation Therapy: An Emerging Paradigm in Adaptive Radiation Oncology

Radiation therapy (RT) continues to be one of the mainstays of cancer treatment. Considerable efforts have been recently devoted to integrating MRI into clinical RT planning and monitoring. This integration, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, organ motion visualization, and ability to monitor tumor and tissue physiologic changes provided by MRI compared with CT. Offline MRI is already used for treatment planning at many institutions. Furthermore, MRI-guided linear accelerator systems, allowing use of MRI during treatment, enable improved adaptation to anatomic changes between RT fractions compared with CT guidance. Efforts are underway to develop real-time MRI-guided intrafraction adaptive RT of tumors affected by motion and MRI-derived biomarkers to monitor treatment response and potentially adapt treatment to physiologic changes. These developments in MRI guidance provide the basis for a paradigm change in treatment planning, monitoring, and adaptation. Key challenges to advancing MRI-guided RT include real-time volumetric anatomic imaging, addressing image distortion because of magnetic field inhomogeneities, reproducible quantitative imaging across different MRI systems, and biologic validation of quantitative imaging. This review describes emerging innovations in offline and online MRI-guided RT, exciting opportunities they offer for advancing research and clinical care, hurdles to be overcome, and the need for multidisciplinary collaboration.

Hybrid PET/MR imaging in myocardial inflammation post-myocardial infarction

Hybrid PET/MR imaging is an emerging imaging modality combining positron emission tomography (PET) and magnetic resonance imaging (MRI) in the same system. Since the introduction of clinical PET/MRI in 2011, it has had some impact (e.g., imaging the components of inflammation in myocardial infarction), but its role could be much greater. Many opportunities remain unexplored and will be highlighted in this review. The inflammatory process post-myocardial infarction has many facets at a cellular level which may affect the outcome of the patient, specifically the effects on adverse left ventricular remodeling, and ultimately prognosis. The goal of inflammation imaging is to track the process non-invasively and quantitatively to determine the best therapeutic options for intervention and to monitor those therapies. While PET and MRI, acquired separately, can image aspects of inflammation, hybrid PET/MRI has the potential to advance imaging of myocardial inflammation. This review contains a description of hybrid PET/MRI, its application to inflammation imaging in myocardial infarction and the challenges, constraints, and opportunities in designing data collection protocols. Finally, this review explores opportunities in PET/MRI: improved registration, partial volume correction, machine learning, new approaches in the development of PET and MRI pulse sequences, and the use of novel injection strategies.

Learning the Sampling Pattern for MRI

The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are "incomplete". This is particularly interesting in magnetic resonance imaging (MRI), where long acquisition times can limit its use. In this work, we consider the problem of learning a sparse sampling pattern that can be used to optimally balance acquisition time versus quality of the reconstructed image. We use a supervised learning approach, making the assumption that our training data is representative enough of new data acquisitions. We demonstrate that this is indeed the case, even if the training data consists of just 7 training pairs of measurements and ground-truth images; with a training set of brain images of size 192 by 192, for instance, one of the learned patterns samples only 35% of k-space, however results in reconstructions with mean SSIM 0.914 on a test set of similar images. The proposed framework is general enough to learn arbitrary sampling patterns, including common patterns such as Cartesian, spiral and radial sampling.

Assessment of metastatic lymph nodes in head and neck squamous cell carcinomas using simultaneous 18F-FDG-PET and MRI

In this study, we investigate the feasibility of using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), diffusion weighted imaging (DWI), and dynamic positron emission tomography (PET) for detection of metastatic lymph nodes in head and neck squamous cell carcinoma (HNSCC) cases. Twenty HNSCC patients scheduled for lymph node dissection underwent DCE-MRI, dynamic PET, and DWI using a PET-MR scanner within one week prior to their planned surgery. During surgery, resected nodes were labeled to identify their nodal levels and sent for routine clinical pathology evaluation. Quantitative parameters of metastatic and normal nodes were calculated from DCE-MRI (v_e, v_p, PS, F_p, K^trans), DWI (ADC) and PET (K_i, K₁, k₂, k₃) to assess if an individual or a combination of parameters can classify normal and metastatic lymph nodes accurately. There were 38 normal and 11 metastatic nodes covered by all three imaging methods and confirmed by pathology. 34% of all normal nodes had volumes greater than or equal to the smallest metastatic node while 4 normal nodes had SUV > 4.5. Among the MRI parameters, the median v_p, F_p, PS, and K^trans values of the metastatic lymph nodes were significantly lower (p = <0.05) than those of normal nodes. v_e and ADC did not show any statistical significance. For the dynamic PET parameters, the metastatic nodes had significantly higher k₃ (p value = 8.8 × 10^-8) and K_i (p value = 5.3 × 10^-8) than normal nodes. K₁ and k₂ did not show any statistically significant difference. K_i had the best separation with accuracy = 0.96 (sensitivity = 1, specificity = 0.95) using a cutoff of K_i = 5.3 × 10^-3 mL/cm³/min, while k₃ and volume had accuracy of 0.94 (sensitivity = 0.82, specificity = 0.97) and 0.90 (sensitivity = 0.64, specificity = 0.97) respectively. 100% accuracy can be achieved using a multivariate logistic regression model of MRI parameters after thresholding the data with K_i < 5.3 × 10^-3 mL/cm³/min. The results of this preliminary study suggest that quantitative MRI may provide additional value in distinguishing metastatic nodes, particularly among small nodes, when used together with FDG-PET.

Performance Comparison of Compressed Sensing Algorithms for Accelerating T1ρ Mapping of Human Brain

BACKGROUND

3D-T_1ρ mapping is useful to quantify various neurologic disorders, but data are currently time-consuming to acquire.

PURPOSE

To compare the performance of five compressed sensing (CS) algorithms-spatiotemporal finite differences (STFD), exponential dictionary (EXP), 3D-wavelet transform (WAV), low-rank (LOW) and low-rank plus sparse model with spatial finite differences (L + S SFD)-for 3D-T_1ρ mapping of the human brain with acceleration factors (AFs) of 2, 5, and 10.

STUDY TYPE

Retrospective.

SUBJECTS

Eight healthy volunteers underwent T_1ρ imaging of the whole brain.

FIELD STRENGTH/SEQUENCE

The sequence was fully sampled 3D Cartesian ultrafast gradient echo sequence with a customized T_1ρ preparation module on a clinical 3T scanner.

ASSESSMENT

The fully sampled data was undersampled by factors of 2, 5, and 10 and reconstructed with the five CS algorithms. Image reconstruction quality was evaluated and compared to the SENSE reconstruction of the fully sampled data (reference) and T_1ρ estimation errors were assessed as a function of AF.

STATISTICAL TESTS

Normalized root mean squared errors (nRMSE) and median normalized absolute deviation (MNAD) errors were calculated to compare image reconstruction errors and T_1ρ estimation errors, respectively. Linear regression plots, Bland-Altman plots, and Pearson correlation coefficients (CC) are shown.

RESULTS

For image reconstruction quality, at AF = 2, EXP transforms had the lowest mRMSE (1.56%). At higher AF values, STFD performed better, with the smallest errors (3.16% at AF = 5, 4.32% at AF = 10). For whole-brain quantitative T_1ρ mapping, at AF = 2, EXP performed best (MNAD error = 1.62%). At higher AF values (AF = 5, 10), the STFD technique had the least errors (2.96% at AF = 5, 4.24% at AF = 10) and the smallest variance from the reference T_1ρ estimates.

DATA CONCLUSION

This study demonstrates the use of different CS algorithms that may be useful in reducing the scan time required to perform volumetric T_1ρ mapping of the brain.

LEVEL OF EVIDENCE

2.

TECHNICAL EFFICACY STAGE

1.

Quantitative T2 mapping using accelerated 3D stack-of-spiral gradient echo readout

PURPOSE

To develop a rapid T2 mapping protocol using optimized spiral acquisition, accelerated reconstruction, and model fitting.

MATERIALS AND METHODS

A T2-prepared stack-of-spiral gradient echo (GRE) pulse sequence was applied. A model-based approach joined with compressed sensing was compared with the two methods applied separately for accelerated reconstruction and T2 mapping. A 2-parameter-weighted fitting method was compared with 2- or 3-parameter models for accurate T2 estimation under the influences of noise and B1 inhomogeneity. The performance was evaluated using both digital phantoms and healthy volunteers. Mitigating partial voluming with cerebrospinal fluid (CSF) was also tested.

RESULTS

Simulations demonstrates that the 2-parameter-weighted fitting approach was robust to a large range of B1 scales and SNR levels. With an in-plane acceleration factor of 5, the model-based compressed sensing-incorporated method yielded around 8% normalized errors compared to references. The T2 estimation with and without CSF nulling was consistent with literature values.

CONCLUSION

This work demonstrated the feasibility of a T2 quantification technique with 3D high-resolution and whole-brain coverage in 2-3 min. The proposed iterative reconstruction method, which utilized the model consistency, data consistency and spatial sparsity jointly, provided reasonable T2 estimation. The technique also allowed mitigation of CSF partial volume effect.

CT and MR perfusion techniques to assess diffuse liver disease

Perfusion imaging allows for the quantitative extraction of physiological perfusion parameters of the liver microcirculation at levels far below the spatial the resolution of CT and MR imaging. Because of its peculiar structure and architecture, perfusion imaging is more challenging in the liver than in other organs. Indeed, the liver is a mobile organ and significantly deforms with respiratory motion. Moreover, it has a dual vascular supply and the sinusoidal capillaries are fenestrated in the normal liver. Using extracellular contrast agents, perfusion imaging has shown its ability to discriminate patients with various stages of liver fibrosis. The recent introduction of hepatobiliary contrast agents enables quantification of both the liver perfusion and the hepatocyte transport function using advanced perfusion models. The purpose of this review article is to describe the characteristics of liver perfusion imaging to assess chronic liver disease, with a special focus on CT and MR imaging.

Analysis of accelerated 4D flow MRI in the murine aorta by radial acquisition and compressed sensing reconstruction

Preclinical 4D flow MRI remains challenging and is restricted for parallel imaging acceleration due to the limited number of available receive channels. A radial acquisition with combined parallel imaging and temporal compressed sensing reconstruction was implemented to achieve accelerated preclinical 4D flow MRI. In order to increase the accuracy of the measured velocities, a quantitative evaluation of different temporal regularization weights for the compressed sensing reconstruction based on velocity instead of magnitude data is performed. A 3D radial retrospectively triggered phase contrast sequence with a combined parallel imaging and compressed sensing reconstruction with temporal regularization was developed. It was validated in a phantom and in vivo (C57BL/6 J mice), against an established fully sampled Cartesian sequence. Different undersampling factors (USFs [12, 15, 20, 30, 60]) were evaluated, and the effect of undersampling was analyzed in detail for magnitude and velocity data. Temporal regularization weights λ were evaluated for different USFs. Acceleration factors of up to 20 compared with full Nyquist sampling were achieved. The peak flow differences compared with the Cartesian measurement were the following: USF 12, 3.38%; USF 15, 4.68%; USF 20, 0.95%. The combination of 3D radial center-out trajectories and compressed sensing reconstruction is robust against motion and flow artifacts and can significantly reduce measurement time to 30 min at a resolution of 180 μm³ . Concisely, radial acquisition with combined compressed sensing and parallel imaging proved to be an excellent method for analyzing complex flow patterns in mice.

Rapid single scan ramped hybrid-encoding for bicomponent T2* mapping in a human knee joint: A feasibility study

The purpose of this study is to determine the feasibility of using a single scan ramped hybrid-encoding (RHE) method for rapid bicomponent T2* analysis of the human knee joint. The proposed method utilizes RHE to acquire ultrashort echo time (UTE) and subsequent gradient echo images at 16 different echo times ranging between 40 μs and 30 ms in a single scan. In the proposed RHE technique, UTE imaging was followed by acquisition of 14 gradient recalled echo images, where an additional UTE image was obtained within the first readout by oversampling single point imaging (SPI) encoding. The single scan RHE method with a 9-minute scan time was performed on human cadaveric knee joints from six donors and in vivo knee joints from four healthy volunteers at 3 T. A bicomponent signal model was used to characterize the short T2* and long T2* water components. Mean bicomponent T2* parameters for patellar tendon, anterior cruciate ligament (ACL), posterior cruciate ligament (PCL) and meniscus were calculated. In the experimental results, the RHE technique provided bicomponent T2* parameter estimations of tendon, ACL, PCL and meniscus, which were similar to previously reported values in the literature. In conclusion, the proposed single scan RHE technique provides rapid bicomponent T2* analysis of the human knee joint with a total scan time of less than 9 minutes.

Cardiac Magnetic Resonance Quantification of Structure-Function Relationships in Heart Failure

Classification of heart failure is based on the left ventricular ejection fraction (EF): preserved EF, midrange EF, and reduced EF. There remains an unmet need for further heart failure phenotyping of ventricular structure-function relationships. Because of high spatiotemporal resolution, cardiac magnetic resonance (CMR) remains the reference modality for quantification of ventricular contractile function. The authors aim to highlight novel frameworks, including theranostic use of ferumoxytol, to enable more efficient evaluation of ventricular function in heart failure patients who are also frequently anemic, and to discuss emerging quantitative CMR approaches for evaluation of ventricular structure-function relationships in heart failure.

Real-time exercise stress cardiac MRI with Fourier-series reconstruction from golden-angle radial data

Magnetic resonance imaging (MRI) can measure cardiac response to exercise stress for evaluating and managing heart patients in the practice of clinical cardiology. However, exercise stress cardiac MRI have been clinically limited by the ability of available MRI techniques to quantitatively measure fast and unstable cardiac dynamics during exercise. The presented work is to develop a new real-time MRI technique for improved quantitative performance of exercise stress cardiac MRI. This technique seeks to represent real-time cardiac images as a sparse Fourier-series along the time. With golden-angle radial acquisition, parallel imaging and compressed sensing can be integrated into a linear system of equations for resolving Fourier coefficients that are in turn used to generate real-time cardiac images from the Fourier-series representation. Fourier-series reconstruction from golden-angle radial data can effectively address data insufficiency due to MRI speed limitation, providing a real-time approach to exercise stress cardiac MRI. To demonstrate the feasibility, an exercise stress cardiac MRI experiment was run to investigate biventricular response to in-scanner biking exercise in a cohort of sixteen healthy volunteers. It was found that Fourier-series reconstruction from golden-angle radial data effectively detected exercise-induced increase in stroke volume and ejection fraction in a healthy heart. The presented work will improve the applications of exercise stress cardiac MRI in the practice of clinical cardiology.

Update on MRI of Cystic Renal Masses Including Bosniak Version 2019

Incidental cystic renal masses are common, usually benign, and almost always indolent. Since 1986, the Bosniak classification has been used to express the risk of malignancy in a cystic renal mass detected at imaging. Historically, magnetic resonance imaging (MRI) was not included in that classification. The proposed Bosniak v.2019 update has formally incorporated MRI, included definitions of imaging terms designed to improve interobserver agreement and specificity for malignancy, and incorporated a variety of masses that were incompletely defined or not included in the original classification. For example, at unenhanced MRI, homogeneous masses markedly hyperintense at T₂ -weighted imaging (similar to cerebrospinal fluid) and homogeneous masses markedly hyperintense at fat suppressed T₁ -weighted imaging (approximately ≥2.5 times more intense than adjacent renal parenchyma) are classified as Bosniak II and may be safely ignored, even when they have not been imaged with a complete renal mass MRI protocol. MRI has specific advantages and is recommended to evaluate masses that at computed tomography (CT) 1) have abundant thick or nodular calcifications; 2) are homogeneous, hyperattenuating, ≥3 cm, and nonenhancing; or 3) are heterogeneous and nonenhancing. Although MRI is generally excellent for characterizing cystic renal masses, there are unique weaknesses of MRI that bear consideration. These details and others related to MRI of cystic renal masses are described in this review, with an emphasis on Bosniak v.2019. A website (https://bosniak-calculator.herokuapp.com/) and mobile phone apps named "Bosniak Calculator" have been developed for ease of assignment of Bosniak classes. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.

Non-contrast coronary magnetic resonance angiography: current frontiers and future horizons

Coronary magnetic resonance angiography (coronary MRA) is advantageous in its ability to assess coronary artery morphology and function without ionizing radiation or contrast media. However, technical limitations including reduced spatial resolution, long acquisition times, and low signal-to-noise ratios prevent it from clinical routine utilization. Nonetheless, each of these limitations can be specifically addressed by a combination of novel technologies including super-resolution imaging, compressed sensing, and deep-learning reconstruction. In this paper, we first review the current clinical use and motivations for non-contrast coronary MRA, discuss currently available coronary MRA techniques, and highlight current technical developments that hold unique potential to optimize coronary MRA image acquisition and post-processing. In the final section, we examine the various research-based coronary MRA methods and metrics that can be leveraged to assess coronary stenosis severity, physiological function, and atherosclerotic plaque characterization. We specifically discuss how such technologies may contribute to the clinical translation of coronary MRA into a robust modality for routine clinical use.

Achieving high spatial and temporal resolution with perfusion MRI in the head and neck region using golden-angle radial sampling

Objectives

Conventional perfusion-weighted MRI sequences often provide poor spatial or temporal resolution. We aimed to overcome this problem in head and neck protocols using a golden-angle radial sparse parallel (GRASP) sequence.

Methods

We prospectively included 58 patients for examination on a 3.0-T MRI using a study protocol. GRASP (A) was applied to a volumetric interpolated breath-hold examination (VIBE) with 135 reconstructed pictures and high temporal (2.5 s) and spatial resolution (0.94 × 0.94 × 3.00 mm). Additional sequences of matching temporal resolution (B: 2.5 s, 1.88 × 1.88 × 3.00 mm), with a compromise between temporal and spatial resolution (C: 7.0 s, 1.30 × 1.30 × 3.00 mm) and with matching spatial resolution (D: 145 s, 0.94 × 0.94 × 3.00 mm), were subsequently without GRASP. Instant inline-image reconstructions (E) provided one additional series of averaged contrast information throughout the entire acquisition duration of A. Overall diagnostic image quality, edge sharpness and contrast of soft tissues, vessels and lesions were subjectively rated using 5-point Likert scales. Objective image quality was measured as contrast-to-noise ratio in D and E.

Results

Overall, the anatomic and pathologic image quality was substantially better with the GRASP sequence for the temporally (A/B/C, all p < 0.001) and spatially resolved comparisons (D/E, all p < 0.002 except lesion edge sharpness with p = 0.291). Image artefacts were also less likely to occur with GRASP. Differences in motion, aliasing and truncation were mainly significant, but pulsation and fat suppression were comparable. In addition, the contrast-to-noise ratio of E was significantly better than that of D (p_D-E < 0.001).

Conclusions

High temporal and spatial resolution can be obtained synchronously using a GRASP-VIBE technique for perfusion evaluation in head and neck MRI.

Key Points

• Golden-angle radial sparse parallel (GRASP) sampling allows for temporally resolved dynamic acquisitions with a very high image quality.

• Very low-contrast structures in the head and neck region can benefit from using the GRASP sequence.

• Inline-image reconstruction of dynamic and static series from one single acquisition can replace the conventional combination of two acquisitions, thereby saving examination time.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07263-0) contains supplementary material, which is available to authorized users.

Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI

PURPOSE

Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath-holding difficulty or non-sinus rhythms. To reduce scan time, we propose a multi-domain convolutional neural network (MD-CNN) for fast reconstruction of highly undersampled radial cine images.

METHODS

MD-CNN is a complex-valued network that processes MR data in k-space and image domains via k-space interpolation and image-domain subnetworks for residual artifact suppression. MD-CNN exploits spatio-temporal correlations across timeframes and multi-coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective-gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD-CNN and k-t Radial Sparse-Sense(kt-RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD-CNN images were evaluated quantitatively using mean-squared-error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5-point Likert-scale (1-non-diagnostic, 2-poor, 3-fair, 4-good, and 5-excellent).

RESULTS

MD-CNN showed improved MSE and SSIM compared to kt-RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD-CCN significantly outperformed kt-RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end-diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end-systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01).

CONCLUSION

MD-CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt-RASPS.

Rotated spiral RARE for high spatial and temporal resolution volumetric arterial spin labeling acquisition

Background:

Arterial Spin Labeling (ASL) MRI can provide quantitative images that are sensitive to both time averaged blood flow and its temporal fluctuations. 3D image acquisitions for ASL are desirable because they are more readily compatible with background suppression to reduce noise, can reduce signal loss and distortion, and provide uniform flow sensitivity across the brain. However, single-shot 3D acquisition for maximal temporal resolution typically involves degradation of image quality through blurring or noise amplification by parallel imaging. Here, we report a new approach to accelerate a common stack of spirals 3D image acquisition by pseudo golden-angle rotation and compressed sensing reconstruction without any degradation of time averaged blood flow images.

Methods:

28 healthy volunteers were imaged at 3T with background-suppressed unbalanced pseudo-continuous ASL combined with a pseudo golden-angle Stack-of-Spirals 3D RARE readout. A fully-sampled perfusion-weighted volume was reconstructed by 3D non-uniform Fast Fourier Transform (nuFFT) followed by sum-of-squares combination of the 32 individual channels. Coil sensitivities were estimated followed by reconstruction of the 39 single-shot volumes using an L₁-wavelet Compressed-Sensing reconstruction. Finally, brain connectivity analyses were performed in regions where BOLD signal suffers from low signal-to-noise ratio and susceptibility artifacts.

Results:

Image quality, assessed with a non-reference 3D blurring metric, of full time averaged blood flow was comparable to a conventional interleaved acquisition. The temporal resolution provided by the acceleration enabled identification and quantification of resting-state networks even in inferior regions such as the amygdala and inferior frontal lobes, where susceptibility artifacts can degrade conventional resting-state fMRI acquisitions.

Conclusion:

This approach can provide measures of blood flow modulations and resting-state networks for free within any research or clinical protocol employing ASL for resting blood flow.

Rapid golden-angle diffusion-weighted propeller MRI for simultaneous assessment of ADC and IVIM

Purpose:

Golden-angle single-shot PROPLLER (GA-SS-PROP) is proposed to accelerate the PROPELLER acquisition for distortion-free diffusion-weighted (DW) imaging. Acceleration is achieved by acquiring one-shot per b-value and several b-values can be acquired along a diffusion direction, where the DW signal follows a bi-exponential decay (i.e. IVIM). Sparse reconstruction is used to reconstruct full resolution DW images. Consequently, apparent diffusion coefficient (ADC) map and IVIM maps (i.e., perfusion fraction (f) and the perfusion-free diffusion coefficient (D)) are obtained simultaneously. The performance of GA-SS-PROP was demonstrated with simulation and human experiments.

Methods:

A realistic numerical phantom of high-quality diffusion images of the brain was developed. The error of the reconstructed DW images and quantitative maps were compared to the ground truth. The pulse sequence was developed to acquire human brain data. For comparison, fully sampled PROPELLER and conventional single-shot echo planar imaging (SS-EPI) acquisitions were performed.

Results:

GA-SS-PROP was 5 times faster than conventional PROPELLER acquisition with comparable image quality. The simulation demonstrated that sparse reconstruction is effective in restoring contrast and resolution. The human experiments demonstrated that GA-SS-PROP achieved superior image fidelity compared to SS-EPI for the same acquisition time and same in-plane resolution (1 × 1 mm²).

Conclusion:

GA-SS-PROP offers fast, high-resolution and distortion-free DW images. The generated quantitative maps (f, D and ADC) can provide valuable information on tissue perfusion and diffusion properties simultaneously, which are desirable in many applications, especially in oncology. As a turbo spin-echo based technique, it can be applied in most challenging regions where SS-EPI is problematic.