Comparative image quality and dosimetric performance of two generations of dedicated breast CT systems.

Background: Dedicated breast computed tomography (bCT) systems offer detailed imaging for breast cancer diagnosis and treatment. As new bCT generations are developed, it is important to evaluate their imaging performance and dose efficiency to understand differences over previous models.

Purpose: To characterize the imaging performance and dose efficiency of a second-generation (GEN2) bCT system and compare them to those of a first-generation (GEN1) system.

The Screening Tomosynthesis Trial with Advanced Reader Methods (STREAM): design and rationale of a population-based breast cancer screening trial.

Objectives: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.

Materials And Methods: The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50-72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT.

Virtual imaging trials in medicine: A brief takeaway of the lessons from the first international summit.

Background: The rapid advancement of medical technologies presents significant challenges for researchers and practitioners. While traditional clinical trials remain the gold standard, they are often limited by high costs, lengthy durations, and ethical constraints. In contrast, in-silico trials and digital twins have emerged not only as efficient and ethical alternatives but also as a complementary technology that can extend beyond classical trials to predict and design new strategies.

Accuracy of dynamic stress CT myocardial perfusion in patients with suspected non-ST elevation myocardial infarction.

Coronary CT angiography (CCTA) and dynamic stress CT myocardial perfusion (CT-MPI) are established modalities in the analysis of patients with chronic coronary syndromes. Their role in patients with suspected non-ST elevation myocardial infarction (NSTEMI) is unknown. CCTA with CT-MPI might assist in the triage of NSTEMI patients to the Cath lab.

In-line spectroscopy for iodine quantification in dynamic contrast-enhanced dedicated breast CT.

Background: In 4D dynamic contrast-enhanced dedicated breast computed tomography (4D DCE-bCT), the functional properties of the breast will be characterized by monitoring the uptake and washout of iodine-based contrast agents over time. This information could be valuable in breast cancer treatment. However, prior to clinical implementation, it is crucial to validate the quantitative estimates of iodine concentrations at each time point during acquisition.

Performance evaluation of a 4D similarity filter for dynamic CT angiography imaging of the liver.

Background: Dynamic computed tomography (CT) angiography of the abdomen provides perfusion information and characteristics of the tissues present in the abdomen. This information could potentially help characterize liver metastases. However, radiation dose has to be relatively low for the patient, causing the images to have very high noise content.

Pain Experience and Image Quality with Curved versus Standard Compression for Breast Cancer Screening Mammography: A Randomized Controlled Trial.

Background A curve-shaped compression paddle could reduce the pain experienced by some women at breast cancer screening. Purpose To compare curved and standard compression systems in terms of pain experience and image quality in mammography screening. Materials and Methods In this randomized controlled trial conducted between October 2021 and February 2022, participants screened at three screening sites in the Netherlands were randomized to either a curved-paddle or sham-paddle group.

Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.

Purpose: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance.

Methods: Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients.

A nonparametric measure of contrast in x-ray images.

We propose a nonparametric figure of merit, the contrast equivalent distance CED, to measure contrast directly from clinical images.A relative brightness distanceis calculated by making use of the order statistic of the pixel values. By multiplyingwith the grey value range, the mean brightness distance MBD is obtained.

Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

Background: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image-domain noise simulation methods useful.

Automatic analysis of the scapholunate distance using 4DCT imaging: normal values in the healthy wrist.

Aim: Early diagnosis of scapholunate ligament (SLL) injuries is crucial to prevent progression to debilitating osteoarthritis. Four-Dimensional Computed Tomography (4DCT) is a promising dynamic imaging modality for assessing such injuries. Capitalizing on the known correlation between SLL injuries and an increased scapholunate distance (SLD), this study aims to develop a fully automatic approach to evaluate the SLD continuously during wrist motion and to apply it to a dataset of healthy wrists to establish reference values.

Image quality of opportunistic breast examinations in photon-counting computed tomography: A phantom study.

Purpose: To compare the breast imaging performance of a clinical whole-body photon-counting CT (PCCT) to that of a dedicated breast CT (BCT) to determine the image quality of opportunistic breast examinations in clinical PCCT.

Materials And Methods: To quantify image quality for breast cancer applications, acquisitions of a breast phantom including representations of calcifications, fibers, and masses were performed using a clinical PCCT and a dedicated BCT. When imaging with the PCCT, the phantom was also combined with a thorax phantom to simulate realistic patient positioning, while only the breast phantom was imaged in the BCT.

Proceedings Virtual Imaging Trials in Medicine 2024.

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare.

Perceptual thresholds for differences in CT noise texture.

Purpose: The average () or peak () noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it.

Approach: A model of CT NPS was created based on its and a half-Gaussian fit () to the downslope.

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.

Radiation dose reduction of 50% in dynamic myocardial CT perfusion with skipped beat acquisition: a retrospective study.

Background: Dynamic myocardial computed tomography perfusion (CTP) is a novel imaging technique that increases the applicability of CT for cardiac imaging; however, the scanning requires a substantial radiation dose.

Purpose: To investigate the feasibility of dose reduction in dynamic CTP by comparing all-heartbeat acquisitions to periodic skipping of heartbeats.

Material And Methods: We retrieved imaging data of 38 dynamic CTP patients and created new datasets with every fourth, third or second beat (Skip1:4, Skip1:3, Skip1:2, respectively) removed.

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.

Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors.

Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views.

Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women.

Joint AAPM Task Group 282/EFOMP Working Group Report: Breast dosimetry for standard and contrast-enhanced mammography and breast tomosynthesis.

Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system.

Optimizing the Pairs of Radiologists That Double Read Screening Mammograms.

Background Despite variation in performance characteristics among radiologists, the pairing of radiologists for the double reading of screening mammograms is performed randomly. It is unknown how to optimize pairing to improve screening performance. Purpose To investigate whether radiologist performance characteristics can be used to determine the optimal set of pairs of radiologists to double read screening mammograms for improved accuracy.

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 < 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 mm slice thickness), analyzed using a dedicated workstation (Vitrea research 7.

Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study.

Objectives: To develop a radiomics model in contrast-enhanced cone-beam breast CT (CE-CBBCT) for preoperative prediction of axillary lymph node (ALN) status and metastatic burden of breast cancer.

Methods: Two hundred and seventy-four patients who underwent CE-CBBCT examination with two scanners between 2012 and 2021 from two institutions were enrolled. The primary tumor was annotated in each patient image, from which 1781 radiomics features were extracted with PyRadiomics.