Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.

Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implementation, these methods must be validated for specific use cases. In this work, the quality of standard-of-care (SOC) T2w and a high-spatial-resolution (HR) imaging of the breast were assessed both with and without prototype DL reconstruction. Studies were performed using data collected from phantoms, 20 retrospectively collected SOC patient exams, and 56 prospectively acquired SOC and HR patient exams. Image quality was quantitatively assessed via signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Qualitatively, all in vivo images were scored by either two or four radiologist readers using 5-point Likert scales in the following categories: artifacts, perceived sharpness, perceived SNR, and overall quality. Differences in reader scores were tested for significance. Reader preference and perception of signal intensity changes were also assessed. Application of the DL resulted in higher average SNR (1.2-2.8 times), CNR (1.0-1.8 times), and image sharpness (1.2-1.7 times). Qualitatively, the SOC acquisition with DL resulted in significantly improved image quality scores in all categories compared to non-DL images. HR acquisition with DL significantly increased SNR, sharpness, and overall quality compared to both the non-DL SOC and the non-DL HR images. The acquisition time for the HR data only required a 20% increase compared to the SOC acquisition and readers typically preferred DL images over non-DL counterparts. Overall, the DL reconstruction demonstrated improved T2w image quality in clinical breast MRI.

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was -2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model.

Gadolinium-based contrast agent attenuation does not impact PET quantification in simultaneous dynamic contrast enhanced breast PET/MR.

PURPOSE: Simultaneous PET/MR imaging involves injection of a radiopharmaceutical and often also includes administration of a gadolinium-based contrast agent (GBCA). Phantom model studies indicate that attenuation of annihilation photons by GBCAs does not bias quantification metrics of PET radiopharmaceutical uptake. However, a direct comparison of attenuation-corrected PET values before and after administration of GBCA has not been performed in patients imaged with simultaneous dynamic PET/MR. The purpose of this study was to investigate the attenuating effect of GBCAs on standardized uptake value (SUV) quantification of 18 F-fluorodeoxyglucose (FDG) uptake in invasive breast cancer and normal tissues using simultaneous PET/MR. METHODS: The study included 13 women with newly diagnosed invasive breast cancer imaged using simultaneous dedicated prone breast PET/MR with FDG. PET data collection and two-point Dixon-based MR attenuation correction sequences began simultaneously before the administration of GBCA to avoid a potential impact of GBCA on the attenuation correction map. A standard clinical dose of GBCA was intravenously administered for the dynamic contrast enhanced MR sequences obtained during the simultaneous PET data acquisition. PET data were dynamically reconstructed into 60 frames of 30 s each. Three timing windows were chosen consisting of a single frame (30 s), two frames (60 s), or four frames (120 s) immediately before and after contrast administration. SUVmax and SUVmean of the biopsy-proven breast malignancy, fibroglandular tissue of the contralateral normal breast, descending aorta, and liver were calculated prior to and following GBCA administration. Percent change in the SUV metrics were calculated to test for a statistically significant, non-zero percent change using Wilcoxon signed-rank tests. RESULTS: No statistical change in SUVmax or SUVmean was found for the breast malignancies or normal anatomical regions during the timing windows before and after GBCA administration. CONCLUSIONS: GBCAs do not significantly impact the results of PET quantification by means of additional attenuation. However, GBCAs may still affect quantification by affecting MR acquisitions used for MR-based attenuation correction which this study did not address. Corrections to account for attenuation due to clinical concentrations of GBCAs are not necessary in simultaneous PET/MR examinations when MR-based attenuation correction sequences are performed prior to GBCA administration.

Proton density water fraction as a reproducible MR-based measurement of breast density.

PURPOSE: To introduce proton density water fraction (PDWF) as a confounder-corrected (CC) MR-based biomarker of mammographic breast density, a known risk factor for breast cancer. METHODS: Chemical shift encoded (CSE) MR images were acquired using a low flip angle to provide proton density contrast from multiple echo times. Fat and water images, corrected for known biases, were produced by a six-echo CC CSE-MRI algorithm. Fibroglandular tissue (FGT) volume was calculated from whole-breast segmented PDWF maps at 1.5T and 3T. The method was evaluated in (1) a physical fat-water phantom and (2) normal volunteers. Results from two- and three-echo CSE-MRI methods were included for comparison. RESULTS: Six-echo CC-CSE-MRI produced unbiased estimates of the total water volume in the phantom (mean bias 3.3%) and was reproducible across protocol changes (repeatability coefficient [RC] = 14.8 cm3 and 13.97 cm3 at 1.5T and 3.0T, respectively) and field strengths (RC = 51.7 cm3 ) in volunteers, while the two- and three-echo CSE-MRI approaches produced biased results in phantoms (mean bias 30.7% and 10.4%) that was less reproducible across field strengths in volunteers (RC = 82.3 cm3 and 126.3 cm3 ). Significant differences in measured FGT volume were found between the six-echo CC-CSE-MRI and the two- and three-echo CSE-MRI approaches (p = 0.002 and p = 0.001, respectively). CONCLUSION: The use of six-echo CC-CSE-MRI to create unbiased PDWF maps that reproducibly quantify FGT in the breast is demonstrated. Further studies are needed to correlate this quantitative MR biomarker for breast density with mammography and overall risk for breast cancer.

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.

Background parenchymal enhancement on breast MRI: A comprehensive review.

The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrast-enhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:43-61.

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

PURPOSE: To develop a volumetric imaging technique with 0.8-mm isotropic resolution and 10-s/volume rate to detect and analyze breast lesions in a bilateral, dynamic, contrast-enhanced MRI exam. METHODS: A local low-rank temporal reconstruction approach that also uses parallel imaging and spatial compressed sensing was designed to create rapid volumetric frame rates during a contrast-enhanced breast exam (vastly undersampled isotropic projection [VIPR] spatial compressed sensing with temporal local low-rank [STELLR]). The dynamic-enhanced data are subtracted in k-space from static mask data to increase sparsity for the local low-rank approach to maximize temporal resolution. A T1 -weighted 3D radial trajectory (VIPR iterative decomposition with echo asymmetry and least squares estimation [IDEAL]) was modified to meet the data acquisition requirements of the STELLR approach. Additionally, the unsubtracted enhanced data are reconstructed using compressed sensing and IDEAL to provide high-resolution fat/water separation. The feasibility of the approach and the dual reconstruction methodology is demonstrated using a 16-channel breast coil and a 3T MR scanner in 6 patients. RESULTS: The STELLR temporal performance of subtracted data matched the expected temporal perfusion enhancement pattern in small and large vascular structures. Differential enhancement within heterogeneous lesions is demonstrated with corroboration from a basic reconstruction using a strict 10-second temporal footprint. Rapid acquisition, reliable fat suppression, and high spatiotemporal resolution are presented, despite significant data undersampling. CONCLUSION: The STELLR reconstruction approach of 3D radial sampling with mask subtraction provides a high-performance imaging technique for characterizing enhancing structures within the breast. It is capable of maintaining temporal fidelity, while visualizing breast lesions with high detail over a large FOV to include both breasts.

Novel High Spatiotemporal Resolution Versus Standard-of-Care Dynamic Contrast-Enhanced Breast MRI: Comparison of Image Quality.

OBJECTIVE: Currently, dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI) prioritizes spatial resolution over temporal resolution given the limitations of acquisition techniques. The purpose of our intrapatient study was to assess the ability of a novel high spatial and high temporal resolution DCE breast MRI method to maintain image quality compared with the clinical standard-of-care (SOC) MRI. MATERIALS AND METHODS: Thirty patients, each demonstrating a focal area of enhancement (29 benign, 1 cancer) on their SOC MRI, consented to undergo a research DCE breast MRI on a second date. For the research DCE MRI, a method (DIfferential Subsampling with Cartesian Ordering [DISCO]) using pseudorandom k-space sampling, view sharing reconstruction, 2-point Dixon fat-water separation, and parallel imaging was used to produce images with an effective temporal resolution 6 times faster than the SOC MRI (27 vs 168 seconds, respectively). Both the SOC and DISCO MRI scans were acquired with matching spatial resolutions of 0.8 × 0.8 × 1.6 mm. Image quality (distortion/artifacts, resolution, fat suppression, lesion conspicuity, perceived signal-to-noise ratio, and overall image quality) was scored by 3 radiologists in a blinded reader study. RESULTS: Differences in image quality scores between the DISCO and SOC images were all less than 0.8 on a 10-point scale, and both methods were assessed as providing diagnostic image quality in all cases. DISCO images with the same high spatial resolution, but 6 times the effective temporal resolution as the SOC MRI scans, were produced, yielding 20 postcontrast time points with DISCO compared with 3 for the SOC MRI, over the same total time interval. CONCLUSIONS: DISCO provided comparable image quality compared with the SOC MRI, while also providing 6 times faster effective temporal resolution and the same high spatial resolution.

Utilization of a balanced steady state free precession signal model for improved fat/water decomposition.

PURPOSE: Chemical shift based fat/water decomposition methods such as IDEAL are frequently used in challenging imaging environments with large B0 inhomogeneity. However, they do not account for the signal modulations introduced by a balanced steady state free precession (bSSFP) acquisition. Here we demonstrate improved performance when the bSSFP frequency response is properly incorporated into the multipeak spectral fat model used in the decomposition process. THEORY AND METHODS: Balanced SSFP allows for rapid imaging but also introduces a characteristic frequency response featuring periodic nulls and pass bands. Fat spectral components in adjacent pass bands will experience bulk phase offsets and magnitude modulations that change the expected constructive and destructive interference between the fat spectral components. A bSSFP signal model was incorporated into the fat/water decomposition process and used to generate images of a fat phantom, and bilateral breast and knee images in four normal volunteers at 1.5 Tesla. RESULTS: Incorporation of the bSSFP signal model into the decomposition process improved the performance of the fat/water decomposition. CONCLUSION: Incorporation of this model allows rapid bSSFP imaging sequences to use robust fat/water decomposition methods such as IDEAL. While only one set of imaging parameters were presented, the method is compatible with any field strength or repetition time.