Technical note:Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast-enhanced imaging

Background: The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization. Purpose: To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction. Methods: A parallel-cascaded system model of a state-of-the-art bCT system was developed and model predictions of the presampled modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) were compared with empirical data collected in the projection domain. Validation was performed using the default settings of 49 kV with 1.5 mm aluminum filter and at 65 kV and 0.257 mm copper filter. A 10 mm aluminum plate was added to replicate the breast attenuation. Air kerma at the isocenter was measured at different tube current levels. Discrepancies between the measured projection domain metrics and model-predicted values were quantified using percentage error and coefficient of variation (CoV) for MTF and NNPS, respectively. The optimal filtration was for a 5 mm iodine disk detection task at 49, 55, 60, and 65 kV. The detectability index was calculated for the default aluminum filtration and for copper thicknesses ranging from 0.05 to 0.4 mm. Results: At 49 kV, MTF errors were +5.1% and −5.1% at 1 and 2 cycles/mm, respectively; NNPS CoV was 5.3% (min = 3.7%; max = 8.5%). At 65 kV, MTF errors were -0.8% and -3.2%; NNPS CoV was 13.1% (min = 11.4%; max = 16.9%). Air kerma output was linear, with 11.67 µGy/mA (R2= 0.993) and 19.14 µGy/mA (R2= 0.996) at 49 and 65 kV, respectively. For iodine detection, a 0.25 mm-thick copper filter at 65 kV was found optimal, outperforming the default technique by 90%. Conclusion: The model accurately predicts bCT system performance, specifically in the projection domain, under varied imaging conditions, potentially contributing to the enhancement of 2D contrast-enhanced imaging in 4D DCE-bCT.</p

Comparing organ and effective dose of various CT localizer acquisition strategies:A Monte Carlo study

Background: CT examinations commonly start with the acquisition of one or two localizer radiographs (2D localizers). Recently, a manufacturer introduced the option to perform a heavily filtrated low-dose helical scan as a localizer acquisition. To compare the dose of one or two 2D localizer acquisitions to the dose of a 3D localizer acquisition, one cannot simply compare the CTDIs of the different acquisition techniques, because of the use of different geometries and spectra. Purpose: To compare the organ and effective dose for various CT localizer acquisition techniques. Methods: A Geant4-based Monte Carlo simulation, replicating a clinical wide-area CT scanner was developed and validated. Various localizer acquisition strategies were simulated: Anterior-posterior (AP) alone, PA alone, combined AP+lateral (LAT), and PA+LAT 2D localizers, and an Ag-filtered 3D localizer acquisition. Validation was performed by measuring and simulating CTDI100 in both the periphery and the center of a CTDI phantom. The software was subsequently used to estimate organ and effective doses for localizers for chest, abdomen + pelvis, and the combined chest, abdomen, and pelvis exams. As representations of patients, eight ICRP computational phantoms (adult, 15-, 10-, and 5-year, both male and female) and five female and five male XCAT phantoms with various BMIs were used. The dose of the various strategies was compared to the current clinically-implemented AP+LAT localizers. Results: CTDI100-measurements and simulations within the CTDI-phantom differs by a maximum of 8.1% and by an average of 0.9%. For chest, the average effective doses for AP, PA, AP+LAT, and the 3D localizer are 0.10, 0.07, 0.32, and 0.22 mSv, respectively. The organ dose to the breast varies the most across the various localizer strategies and is, on average, 0.17, 0.03, 0.44, and 0.33 mGy, in the same order. For abdomen, the average effective doses are 0.11, 0.07, 0.36, and 0.25 mSv for the AP, PA, AP+LAT and the 3D localizer, respectively. The organ dose to the stomach varies the most across the various localizers and is on average 0.14, 0.08, 0.58, and 0.30 mGy, in the same order. The PA-only localizer results in the lowest organ dose to the most radiosensitive organs and the lowest effective dose. For the chest exam, compared to AP+LAT, the PA+LAT results in a 7 ± 2% effective dose reduction (mean ± standard deviation), while the 3D localizer results in a 21 ± 3% effective dose reduction. Using AP or PA only would result in 69 ± 2% and 76 ± 2% reduction, respectively. For the abdomen exam, also compared to AP+LAT, PA+LAT results in 6 ± 2% effective dose reduction, while the 3D localizer results in a 20 ± 5% reduction. Using AP or PA only would result in 69 ± 5% and 76 ± 4% reduction, respectively. Conclusions: Using a PA localizer results in a lower or equivalent organ dose in the most radiosensitive organs, and a lower effective dose compared to an AP localizer for both chest and abdomen+pelvis exams. Compared to a two-localizer strategy, the 3D localizer results in a lower effective dose in both the chest and abdomen+pelvis region.</p

Artificial Intelligence for breast cancer detection:Technology, challenges, and prospects

Purpose: This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening. Methods: The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems. It discusses data pre-processing, learning paradigms, and the need for independent validation approaches. Results: DL-based AI systems have shown significant improvements in breast cancer detection. They have the potential to enhance screening outcomes, reduce false negatives and positives, and detect subtle abnormalities missed by human observers. However, challenges like the lack of standardised datasets, potential bias in training data, and regulatory approval hinder their widespread adoption. Conclusions: AI technology has the potential to improve breast cancer screening by increasing accuracy and reducing radiologist workload. DL-based AI systems show promise in enhancing detection performance and eliminating variability among observers. Standardised guidelines and trustworthy AI practices are necessary to ensure fairness, traceability, and robustness. Further research and validation are needed to establish clinical trust in AI. Collaboration between researchers, clinicians, and regulatory bodies is crucial to address challenges and promote AI implementation in breast cancer screening.</p

Non-Rigid Motion Compensation for Breast CT

The image quality in dynamic contrast-enhanced breast CT is expected to suffer from motion artifacts due to the extended acquisition time involved. We propose an iterative method for compensation of motion artifacts due to non-rigid movement of the breast. The motion vector field is approximated using b-splines on a sparse grid and its values are found by minimizing errors in the projection domain. We evaluated the method on an anthropomorphic phantom with realistic motion and visual assessment yielded a clear reduction in motion artifacts. Quantitatively, we observed an increase of the structural similarity from 0.9988 to 0.9995 and a decrease of the normalized root mean squared error from 0.1448 to 0.0932.</p

Stress-only dynamic computed tomography perfusion protocol (CTP) alone without computed tomography coronary angiography (CCTA) has limited specificity to diagnose ischemia:A retrospective two-center study

Purpose: To investigate diagnostic performance of stress-only dynamic myocardial computed tomography perfusion (CTP) without computed tomography coronary angiography (CCTA) to diagnose ischemia with invasive fractional flow reserve (FFR) as a reference standard. Method: 135 datasets (68 positive for ischemia with invasive FFR&nbsp;&lt;&nbsp;0.8) acquired with a 256-slice CT system (Revolution, GE Healthcare, Chicago, IL, USA) were retrieved, postprocessed with a deep learning-based algorithm (Advanced intelligent Clear-IQ Engine (AiCE), Canon Medical Systems, Otawara, Japan) (FC03/cardiac kernel, 8&nbsp;mm slice thickness), analyzed using a dedicated workstation (Vitrea research 7.11.0. Vital Images, Minnetonka, MN, USA), and loaded into a clinical workstation (CardIQ, GE Healthcare, Chicago, IL, USA) for review. Ten observers with various experience from two research sites evaluated the post-processed images, perfusion slices and maps to indicate presence vs absence of perfusion defect and its probability (five-point Likert scale). Binary decisions and probability scores were used to calculate sensitivity and specificity for each reader, and to create receiver operating characteristics (ROC) curves, respectively. Furthermore, the correlation coefficient (ICC) was computed. ROC AUC of a purely quantitative analysis was obtained thanks to a color-coded map with a fixed scale superimposed on myocardial walls displaying myocardial blood flow (MBF) values. Results: The overall case-based sensitivity and specificity for the detection of perfusion deficit were 0.79 and 0.30, respectively. No significant differences were detected in the AUC across readers (p value&nbsp;=&nbsp;0.66). The AUC values were 0.50, 0.58, 0.63, 0.59, 0.45, 0.60, 0.56, 0.61, 0.52, 0.61. Absolute reader agreement ICC was 0.60 (good agreement) for an average case. Conclusion: Dynamic CTP alone has good sensitivity, but low specificity when analyzed without CCTA. These findings reinforce the need to guide the interpretation functional test with the knowledge of coronary artery anatomy