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.

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.

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.

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.

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.

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.

Incidence and outcomes of incidental breast lesions detected on cross-sectional imaging examinations.

The aim of this study was to determine the frequency and outcomes of incidental breast lesions detected on nonbreast specific cross-sectional imaging examinations. A retrospective review of the medical records was performed to identify all patients without a known history of breast cancer, who had an incidentally discovered breast lesion detected on a nonbreast imaging examination performed at our institution between September 2008 and August 2012 for this IRB-approved, HIPAA compliant study. Outcomes of the incidental lesions were determined by follow-up with dedicated breast imaging (mammography, breast ultrasound, and/or breast MRI) or results of biopsy, if performed. Imaging modality of detection, imaging features, patient age, patient location at the time of the nonbreast imaging examination, type of follow-up, and final outcome were recorded. Rates of malignancy were also calculated, and comparison was made across the different cross-sectional imaging modalities. Kruskal-Wallis and Fisher's exact tests were used to identify factors associated with an increased rate of malignancy. Logistic regression was used to model the risk of malignancy as a function of continuous predictors (such as patient age or lesion size); odds ratios and 95% confidence intervals were obtained. A total of 292 patients with incidental breast lesions were identified, 242 of whom had incidental lesions were noted on computed tomography (CT) studies, 25 on magnetic resonance imaging (MRI), and 25 on positron emission tomography (PET). Although most of the incidental breast lesions were detected on CT examinations, PET studies had the highest rate of detection of incidental breast lesions per number of studies performed (rate of incidental breast lesion detection on PET studies was 0.29%, compared to 0.10% for CT and 0.01% for MRI). Of the 121 of 292 (41%) patients who received dedicated breast imaging work-up at our institution, 40 of 121 (33%) underwent biopsy and 25 of 121 (21%) had malignancy. There was a significantly increased rate of malignancy in older patients (odds ratio: 1.05, 95% CI: 1.02-1.093; P = .006). Additionally, patients with PET-detected incidental breast lesions had a significantly higher rate of malignancy (55%), compared to patients with CT-detected (35%) and MRI-detected (8%) incidental breast lesions (P = .038). The rate of malignancy upon follow-up of incidental breast lesions detected on nonbreast imaging examinations in this retrospective study was 21%, supporting the importance of emphasizing further work-up of all incidentally detected breast lesions with dedicated breast imaging. Additionally, we found that PET examinations had the highest rate of detection of incidental breast lesions and the highest rate of malignancy, which suggests that PET examinations may be more specific for predicting the likelihood of malignancy of incidental breast lesions, compared to CT and MRI.

Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI.

OBJECTIVE: BI-RADS for mammography and ultrasound subdivides category 4 assessments by likelihood of malignancy into categories 4A (> 2% to ≤ 10%), 4B (> 10% to ≤ 50%), and 4C (> 50% to < 95%). Category 4 is not subdivided for breast MRI because of a paucity of data. The purpose of the present study is to determine the utility of categories 4A, 4B, and 4C for MRI by calculating their positive predictive values (PPVs) and comparing them with BI-RADS-specified rates of malignancy for mammography and ultrasound. MATERIALS AND METHODS: All screening breast MRI examinations performed from July 1, 2010, through June 30, 2013, were included in this study. We identified in medical records prospectively assigned MRI BI-RADS categories, including category 4 subdivisions, which are used routinely in our practice. Benign versus malignant outcomes were determined by pathologic analysis, findings from 12 months or more clinical or imaging follow-up, or a combination of these methods. Distribution of BI-RADS categories and positive predictive value level 2 (PPV2; based on recommendation for tissue diagnosis) for categories 4 (including its subdivisions) and 5 were calculated. RESULTS: Of 860 screening breast MRI examinations performed for 566 women (mean age, 47 years), 82 with a BI-RADS category 4 assessment were identified. A total of 18 malignancies were found among 84 category 4 and 5 assessments, for an overall PPV2 of 21.4% (18/84). For category 4 subdivisions, PPV2s were as follows: for category 4A, 2.5% (1/40); for category 4B, 27.6% (8/29); for category 4C, 83.3% (5/6); and for category 4 (not otherwise specified), 28.6% (2/7). CONCLUSION: Category 4 subdivisions for MRI yielded malignancy rates within BI-RADS-specified ranges, supporting their use for benefits to patient care and more meaningful practice audits.

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.

Screening magnetic resonance imaging recommendations and outcomes in patients at high risk for breast cancer.

The purpose of this study was to determine magnetic resonance imaging (MRI) screening recommendations and the subsequent outcomes in women with increased risk for breast cancer evaluated by oncology subspecialists at an academic center. Patients evaluated between 1/1/2007 and 3/1/2011 under diagnosis codes for family history of breast or ovarian cancer, genetic syndromes, lobular carcinoma in situ or atypical hyperplasia were included. Patients with a history of breast cancer were excluded. Retrospective review of prospectively acquired demographics, lifetime risk of breast cancer, and screening recommendations were obtained from the medical record. Retrospective review of the results of prospectively interpreted breast imaging examinations and image-guided biopsies were analyzed. 282 women were included. The majority of patients were premenopausal with a median age of 43. Most (69%) were referred due to a family history of breast or ovarian cancers. MRI was recommended for 84% of patients based on a documented lifetime risk >20%. Most women referred for MRI screening (88%) were compliant with this recommendation. A total of 299 breast MRI examinations were performed in 146 patients. Biopsy was performed for 32 (11%) exams and 10 cancers were detected for a positive predictive value (PPV) of 31% (based on biopsy performed) and an overall per exam cancer yield of 3.3%. Three cancers were detected in patients who did not undergo screening MRI. The 13 cancers were Stage 0-II; all patients were without evidence of disease with a median follow-up of 22 months. In a cohort of women seen by breast subspecialty providers, screening breast MRI was recommended according to guidelines, and used primarily in premenopausal women with a family history or genetic predisposition to breast cancer. Adherence to MRI screening recommendations was high and cancer yield from breast MRI was similar to that in clinical trials.

Metastasis of primary lung carcinoma to the breast: a systematic review of the literature.

BACKGROUND: The purpose of this systematic review was to summarize previously published case reports of primary lung carcinoma metastasis to the breast to assess common clinical and pathologic features and management strategies. MATERIALS AND METHODS: Case reports describing breast metastasis of primary lung carcinoma were systematically evaluated in MEDLINE and EMBASE. RESULTS: Thirty-one reported cases of non-small-cell lung carcinoma (NSCLC) metastasized to the breast were identified, along with eight cases of small-cell lung carcinoma. Sixty-seven percent of reported NSCLC metastases to the breast were detected metachronously with the primary lung abnormality, whereas 80% of small-cell lung carcinoma breast metastases appeared synchronously. Thyroid transcription factor 1 was found to be expressed in 58% of total NSCLC breast metastases, including 83% of those of adenocarcinoma origin. Therapeutic strategies among NSCLC cases varied widely, and only 36% of NSCLC breast metastasis patients were administered chemotherapy. Additional sites of metastasis in these cases are summarized as well. CONCLUSIONS: It is recommended to include metastatic lung cancer in the differential diagnosis of patients presenting with a breast abnormality in the context of a suspected lung cancer. Thyroid transcription factor 1 expression should be examined in these cases. The metachronous versus synchronous nature of lung carcinoma metastasis to the breast has consequences for both detection of the primary and secondary lesions and patient outlook. Clinical correlation is vital to effective management of the care of patients harboring these atypical secondary lesions.

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.

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.

Dynamic breast MRI: does lower temporal resolution negatively affect clinical kinetic analysis?

OBJECTIVE: The purpose of this study was to compare the differences in kinetic assessments of lesions at breast MRI performed with higher and lower temporal resolution. MATERIALS AND METHODS: All consecutively evaluated BI-RADS category 4, 5, and 6 lesions imaged with breast MRI and pathologically confirmed from October 2005 to August 2009 were identified. Patients underwent MRI with one of two dynamic contrast-enhanced protocols: one with 90-second (October 2005-June 2006) and another with 180-second (July 2006-August 2009) temporal resolution. Studies were processed with a computer-aided evaluation system with initial and delayed contrast-enhanced time points with the k-space centered 90 and 450 seconds after contrast injection. Initial-phase peak enhancement, delayed-phase predominant curve type, and worst curve type were recorded and compared for benign and malignant lesions across protocols. RESULTS: The analysis set comprised 993 lesions: 145 imaged with the 90-second acquisition (17 benign, 28 ductal carcinoma in situ [DCIS], 100 invasive cancer) and 848 imaged with the 180-second acquisition (212 benign, 145 DCIS, 491 invasive cancer). Peak enhancement was significantly higher for both benign lesions (p = 0.01) and invasive cancers (p = 0.0008) with the 180-second protocol. Peak enhancement of DCIS was similar in the two protocols (p = 0.88). Delayed-phase kinetics were similar for the two protocols for both benign and malignant lesions when defined by predominant or worst curve type. CONCLUSION: Although it has lower temporal resolution, a 180-second acquisition may be preferable because it allows higher spatial resolution and captures higher initial-phase peak enhancement without loss of delayed-phase kinetic information.

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.

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.