Attenuation correction and truncation completion for breast PET/MR imaging using deep learning.

Objective. Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust attenuation correction algorithm for breast PET/MR images that relies on deep learning (DL) to recreate the missing portions of the patient's anatomy (truncation completion), as well as to provide bone information for attenuation correction from only the PET data.Approach. Data acquired from 23 female subjects with invasive breast cancer scanned with18F-fluorodeoxyglucose PET/CT and PET/MR localized to the breast region were used for this study. Three DL models, U-Net with mean absolute error loss (DLMAE) model, U-Net with mean squared error loss (DLMSE) model, and U-Net with perceptual loss (DLPerceptual) model, were trained to predict synthetic CT images (sCT) for PET attenuation correction (AC) given non-attenuation corrected (NAC) PETPET/MRimages as inputs. The DL and Dixon-based sCT reconstructed PET images were compared against those reconstructed from CT images by calculating the percent error of the standardized uptake value (SUV) and conducting Wilcoxon signed rank statistical tests.Main results. sCT images from the DLMAEmodel, the DLMSEmodel, and the DLPerceptualmodel were similar in mean absolute error (MAE), peak-signal-to-noise ratio, and normalized cross-correlation. No significant difference in SUV was found between the PET images reconstructed using the DLMSEand DLPerceptualsCTs compared to the reference CT for AC in all tissue regions. All DL methods performed better than the Dixon-based method according to SUV analysis.Significance. A 3D U-Net with MSE or perceptual loss model can be implemented into a reconstruction workflow, and the derived sCT images allow successful truncation completion and attenuation correction for breast PET/MR images.

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.

NCCN Guidelines® Insights: Breast Cancer Screening and Diagnosis, Version 1.2023.

The NCCN Guidelines for Breast Cancer Screening and Diagnosis provide health care providers with a practical, consistent framework for screening and evaluating a spectrum of clinical presentations and breast lesions. The NCCN Breast Cancer Screening and Diagnosis Panel is composed of a multidisciplinary team of experts in the field, including representation from medical oncology, gynecologic oncology, surgical oncology, internal medicine, family practice, preventive medicine, pathology, diagnostic and interventional radiology, as well as patient advocacy. The NCCN Breast Cancer Screening and Diagnosis Panel meets at least annually to review emerging data and comments from reviewers within their institutions to guide updates to existing recommendations. These NCCN Guidelines Insights summarize the panel's decision-making and discussion surrounding the most recent updates to the guideline's screening recommendations.

Breast Imaging and Intervention during Pregnancy and Lactation.

Physiologic changes that occur in the breast during pregnancy and lactation create challenges for breast cancer screening and diagnosis. Despite these challenges, imaging evaluation should not be deferred, because delayed diagnosis of pregnancy-associated breast cancer contributes to poor outcomes. Both screening and diagnostic imaging can be safely performed using protocols based on age, breast cancer risk, and whether the patient is pregnant or lactating. US is the preferred initial imaging modality for the evaluation of clinical symptoms in pregnant women, followed by mammography if the US findings are suspicious for malignancy or do not show the cause of the clinical symptom. Breast MRI is not recommended during pregnancy because of the use of intravenous gadolinium-based contrast agents. Diagnostic imaging for lactating women is the same as that for nonpregnant nonlactating individuals, beginning with US for patients younger than 30 years old and mammography followed by US for patients aged 30 years and older. MRI can be performed for high-risk screening and local-regional staging in lactating women. The radiologist may encounter a wide variety of breast abnormalities, some specific to pregnancy and lactation, including normal physiologic changes, benign disorders, and malignant neoplasms. Although most masses encountered are benign, biopsy should be performed if the imaging characteristics are suspicious for cancer or if the finding does not resolve after a short period of clinical follow-up. Knowledge of the expected imaging appearance of physiologic changes and common benign conditions of pregnancy and lactation is critical for differentiating these findings from pregnancy-associated breast cancer. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

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.

Community-based Participatory Research: A Practical Guide for Radiologists.

Community-based participatory research (CBPR) is defined by the Kellogg Community Health Scholars Program as a collaborative process that equitably involves all partners in the research process and recognizes the unique strengths that each community member brings. The CBPR process begins with a research topic of importance to the community, with the goal of combining knowledge and action with social change to improve community health and eliminate health disparities. CBPR engages and empowers affected communities to collaborate in defining the research question; sharing the study design process; collecting, analyzing, and disseminating the data; and implementing solutions. A CBPR approach in radiology has several potential applications, including removing limitations to high-quality imaging, improving secondary prevention, identifying barriers to technology access, and increasing diversity in the research participation for clinical trials. The authors provide an overview with the definitions of CBPR, explain how to conduct CBPR, and illustrate its applications in radiology. Finally, the challenges of CBPR and useful resources are discussed in detail. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI.

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.

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.

Clinical advances in PET-MRI for breast cancer.

Imaging is paramount for the early detection and clinical staging of breast cancer, as well as to inform management decisions and direct therapy. PET-MRI is a quantitative hybrid imaging technology that combines metabolic and functional PET data with anatomical detail and functional perfusion information from MRI. The clinical applicability of PET-MRI for breast cancer is an active area of research. In this Review, we discuss the rationale and summarise the clinical evidence for the use of PET-MRI in the diagnosis, staging, prognosis, tumour phenotyping, and assessment of treatment response in breast cancer. The continued development and approval of targeted radiopharmaceuticals, together with radiomics and automated analysis tools, will further expand the opportunity for PET-MRI to provide added value for breast cancer imaging and patient care.

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.

Hyperpolarized 13C Magnetic Resonance Spectroscopic Imaging of Pyruvate Metabolism in Murine Breast Cancer Models of Different Metastatic Potential.

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.

Measuring Glucose Uptake in Primary Invasive Breast Cancer Using Simultaneous Time-of-Flight Breast PET/MRI: A Method Comparison Study with Prone PET/CT.

Purpose: To compare the measurement of glucose uptake in primary invasive breast cancer using simultaneous, time-of-flight breast PET/MRI with prone time-of-flight PET/CT. Materials and Methods: In this prospective study, women with biopsy-proven invasive breast cancer undergoing preoperative breast MRI from 2016 to 2018 were eligible. Participants who had fasted underwent prone PET/CT of the breasts approximately 60 minutes after injection of 370 MBq (10 mCi) fluorine 18 fluorodeoxyglucose (18F-FDG) followed by prone PET/MRI using standard clinical breast MRI sequences performed simultaneously with PET acquisition. Volumes of interest were drawn for tumors and contralateral normal breast fibroglandular tissue to calculate standardized uptake values (SUVs). Spearman correlation, Wilcoxon signed ranked test, Mann-Whitney test, and Bland-Altman analyses were performed. Results: Twenty-three women (mean age, 50 years; range, 33-70 years) were included. Correlation between tumor uptake values measured with PET/MRI and PET/CT was strong (r s = 0.95-0.98). No difference existed between modalities for tumor maximum SUV (SUVmax) normalized to normal breast tissue SUVmean (normSUVmax) (P = .58). The least amount of measurement bias was observed with normSUVmax, +3.86% (95% limits of agreement: -28.92, +36.64). Conclusion: These results demonstrate measurement agreement between PET/CT, the current reference standard for tumor glucose uptake quantification, and simultaneous time-of-flight breast 18F-FDG PET/MRI.Keywords: Breast, Comparative Studies, PET/CT, PET/MR Supplemental material is available for this article. © RSNA, 2021See also the commentary by Mankoff and Surti in this issue.

Supplemental Screening for Patients at Intermediate and High Risk for Breast Cancer.

The sensitivity of mammography is more limited in patients with dense breasts and some patients at higher risk for breast cancer. Patients with intermediate or high risk for breast cancer may begin screening earlier and benefit from supplemental screening techniques beyond standard 2-dimensional mammography. A patient's individual risk factors for developing breast cancer, their breast density, and the evidence supporting specific modalities for a given clinical scenario help to determine the need for supplemental screening and the modality chosen. Additional factors include the availability of supplemental screening techniques at an individual institution, cost, insurance coverage, and state-specific breast density legislation.

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.

Development and Implementation of an Algorithm to Guide MRI Screening in Patients With a Personal History of Treated Breast Cancer.

INTRODUCTION: Limited data exist to guide appropriate use of magnetic resonance imaging (MRI) screening in women with a personal history of breast cancer. We developed an algorithm to inform the use of MRI screening in patients with a personal history, implemented it, and evaluated initial implementation at our community and academic practice sites. PATIENTS AND METHODS: A multidisciplinary committee of providers developed the initial algorithm on the basis of available literature and consensus. To evaluate projected MRI utilization based on the initial algorithm and inform algorithm revision, charts of patients < 80 years of age diagnosed and treated in 2010 with stage 0-III breast cancer (n = 236) were reviewed. The revised algorithm was implemented into the electronic medical record (September 2013). Thirteen months after implementation (2014-2015), chart review of patients with a personal history of breast cancer who underwent screening MRI was performed to assess algorithm adherence. RESULTS: Before algorithm development, 9% (20/236) of patients received MRI screening (6 genetic mutation/family history, 4 occult primary, 8 young age/breast density, 2 unknown). Use of MRI screening was projected to increase to 25% with algorithm implementation. In postimplementation review, we identified 183 patients with a personal history of breast cancer who underwent screening MRI, with 94% algorithm adherence. CONCLUSION: We successfully developed and implemented an algorithm to guide MRI screening in patients with a personal breast cancer history. Clinicians can use this algorithm to guide patient discussions regarding the utility of MRI screening. Further prospective study, including cancer detection rates, biopsy rate, and mortality, are necessary to confirm the algorithm's usefulness.

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.

Improved reconstruction stability for chemical shift encoded hyperpolarized 13 C magnetic resonance spectroscopic imaging using k-t spiral acquisitions.

PURPOSE: A multiecho, field of view (FOV)-oversampled k-t spiral acquisition and direct iterative decomposition of water and fat with echo asymmetry and least-squares estimation reconstruction is demonstrated to improve the stability of hyperpolarized 13 C magnetic resonance spectroscopic imaging (MRSI) in the presence of signal ambiguities attributed to low-SNR (signal-to-noise-ratio) species, local uncertainties in metabolite peaks, and echo-to-echo signal inconsistencies. THEORY: k-t spiral acquisitions redistribute readout points to be more densely spaced radially in k-space by acquiring an FOV and matrix that are oversampled by η. These more densely spaced spiral turns constitute effective intraspiral echoes and can supplement conventional interspiral echoes to improve spectral separation and reduce spectral cross-talk to better resolve 13 C-labeled species for spectroscopic imaging. METHODS: Digital simulations and imaging phantom experiments were performed for a range of interspiral echo spacings and η using multiecho, k-t spiral acquisitions. Image spectral cross-talk artifacts were evaluated both qualitatively and quantitatively as the percent error in measured metabolite ratios. In vivo murine experiments evaluated the feasibility of multiecho, k-t spiral [1-13 C]pyruvate MRSI to reduce spectral cross-talk for 3 scenarios of different expected reconstruction stability. RESULTS: Digital simulations and imaging phantom experiments both demonstrated reduced or comparable image spectral cross-talk and percent errors in measured metabolite ratios with increasing η and better choices of echo spacings. In vivo images displayed markedly reduced spectral cross-talk in lactate images acquired with η = 7 versus η = 1. CONCLUSION: The precision of hyperpolarized 13 C metabolic imaging and quantification in the presence of low-SNR species, local uncertainties in metabolite resonances, and echo-to-echo signal inconsistencies can be improved with the use of FOV-oversampled k-t spiral acquisitions.

Image-based screening for men at high risk for breast cancer: Benefits and drawbacks.

Male breast cancer is a rare malignancy. Due to low prevalence and limited data to support male breast cancer screening, there are currently no recommendations for image-based screening in asymptomatic men and few recommendations for men at high risk for breast cancer. However, symptomatically diagnosed cancers in men are typically advanced, suggesting that earlier detection may improve outcomes. In this article we briefly review the risk factors for male breast cancer, and discuss the potential benefits and possible drawbacks of routine image-based screening for men at high risk for breast cancer.

Recent Advances in Imaging Steroid Hormone Receptors in Breast Cancer.

Estrogen receptor (ER) and progesterone receptor (PR) are important prognostic and predictive biomarkers in breast cancer. PET using ER- and PR-specific radioligands enables a whole-body, noninvasive assessment of receptor expression. Recent investigations of ER imaging with 18F-fluoroestradiol have focused on diagnosing ER-positive metastatic disease, optimizing ER-targeted drug dosage, and predicting endocrine therapy benefit. Studies of PR imaging with 18F-fluorofuranyl norprogesterone have investigated how imaging changes in PR expression as a downstream target of ER activation may reflect an early response to ER-targeted therapy. This focused review highlights recent achievements in preclinical and clinical imaging of ER and PR in breast cancer.