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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.

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.

This dialogue approaches the experiences and reflections of early career researchers (ECRs), that is, those academics who have completed their PhDs either recently or within the last 5–7 years. The dialogue brings to the forefront recollections of how the COVID pandemic changed research and teaching practices in higher education, emphasising, in particular, the challenges affecting ECRs, who often are on precarious contracts, are still developing their own pedagogical approaches, and have not yet built a network of support. These circumstances make the experiences of ECRs different from those of more experienced academics and university teachers. The dialogues aim to bring to the discussion how these experiences have shaped the way that ECRs engage with research and teaching. The dialogue brings interesting reflections on the way that the aftermath of the pandemic has brought more inclusive practices for participation, promoted collaborations, and inspired novel teaching approaches. However, it also notes how ECRs have been left in many cases to their own initiatives and without adequate mentorship or support, often overloaded by added challenges and feelings of isolation. Finally, the dialogue suggests improvements to current higher education practices and policies, which can significantly and positively impact the development of ECRs.

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with Ktrans values ranging from 0.01 to 0.8 min−1 and fixed Ve = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both Ktrans and Ve. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.

Advances in accelerated magnetic resonance imaging (MRI) continue to push the bounds on achievable spatial and temporal resolution while maintaining a clinically acceptable image quality. Validation tools, including numerical simulations, are needed to characterize the repeatability and reproducibility of such methods for use in quantitative imaging applications. We describe the development of a simulation framework for analyzing and optimizing accelerated MRI acquisition and reconstruction techniques used in dynamic contrast enhanced (DCE) breast imaging. The simulation framework, in the form of a digital reference object (DRO), consists of four modules that control different aspects of the simulation, including the appearance and physiological behavior of the breast tissue as well as the MRI acquisition settings, to produce simulated k-space data for a DCE breast exam. The DRO design and functionality are described along with simulation examples provided to show potential applications of the DRO. The included simulation results demonstrate the ability of the DRO to simulate a variety of effects including the creation of simulated lesions, tissue enhancement modeled by the generalized kinetic model, T1-relaxation, fat signal precession and saturation, acquisition SNR, and changes in temporal resolution.

BackgroundMotivated by the challenges in assessing physician-level cancer screening performance and the negative impact of misclassification, we propose a method (using mammography as an example) that enables confident assertion of adequate or inadequate performance or alternatively recognizes when more data is required.MethodsUsing established metrics for mammography screening performance-cancer detection rate (CDR) and recall rate (RR)-and observed benchmarks from the Breast Cancer Surveillance Consortium (BCSC), we calculate the minimum volume required to be 95% confident that a physician is performing at or above benchmark thresholds. We graphically display the minimum observed CDR and RR values required to confidently assert adequate performance over a range of interpretive volumes. We use a prospectively collected database of consecutive mammograms from a clinical screening program outside the BCSC to illustrate how this method classifies individual physician performance as volume accrues.ResultsOur analysis reveals that an annual interpretive volume of 2770 screening mammograms, above the United States' (US) mandatory (480) and average (1777) annual volumes but below England's mandatory (5000) annual volume is necessary to confidently assert that a physician performed adequately. In our analyzed US practice, a single year of data uniformly allowed confident assertion of adequate performance in terms of RR but not CDR, which required aggregation of data across more than one year.ConclusionFor individual physician quality assessment in cancer screening programs that target low incidence populations, considering imprecision in observed performance metrics due to small numbers of patients with cancer is important.

This study uses dynamic hyperpolarized [1-13C]pyruvate magnetic resonance spectroscopic imaging (MRSI) to estimate differences in glycolytic metabolism between highly metastatic (4T1, n = 7) and metastatically dormant (4T07, n = 7) murine breast cancer models. The apparent conversion rate of pyruvate-to-lactate (kPL) and lactate-to-pyruvate area-under-the-curve ratio (AUCL/P) were estimated from the metabolite images and compared with biochemical metabolic measures and immunohistochemistry (IHC). A non-significant trend of increasing kPL (p = 0.17) and AUCL/P (p = 0.11) from 4T07 to 4T1 tumors was observed. No significant differences in tumor IHC lactate dehydrogenase-A (LDHA), monocarboxylate transporter-1 (MCT1), cluster of differentiation 31 (CD31), and hypoxia inducible factor-α (HIF-1α), tumor lactate-dehydrogenase (LDH) activity, or blood lactate or glucose levels were found between the two tumor lines. However, AUCL/P was significantly correlated with tumor LDH activity (ρspearman = 0.621, p = 0.027) and blood glucose levels (ρspearman = −0.474, p = 0.042). kPL displayed a similar, non-significant trend for LDH activity (ρspearman = 0.480, p = 0.114) and blood glucose levels (ρspearman = −0.414, p = 0.088). Neither kPL nor AUCL/P were significantly correlated with blood lactate levels or tumor LDHA or MCT1. The significant positive correlation between AUCL/P and tumor LDH activity indicates the potential of AUCL/P as a biomarker of glycolytic metabolism in breast cancer models. However, the lack of a significant difference between in vivo tumor metabolism for the two models suggest similar pyruvate-to-lactate conversion despite differing metastatic potential.