Digital breast tomosynthesis system concept addressing the needs in breast cancer screening and diagnosis.

Purpose: Digital breast tomosynthesis (DBT) has been introduced more than a decade ago. Studies have shown higher breast cancer detection rates and lower recall rates, and it has become an established imaging method in diagnostic settings. However, full-field digital mammography (FFDM) remains the most common imaging modality for screening in many countries, as it delivers high-resolution planar images of the breast.

Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans.

Deep learning-based image analysis offers great potential in clinical practice. However, it faces mainly two challenges: scarcity of large-scale annotated clinical data for training and susceptibility to adversarial data in inference. As an example, an artificial intelligence (AI) system could check patient positioning, by segmenting and evaluating relative positions of anatomical structures in medical images.

Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial.

Objectives: Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach.

Materials And Methods: Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory.

Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography.

Background: Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches.

Purpose: We investigate the feasibility of BME assessment using kV-switching DE CBCT with a comprehensive CBCT artifact correction framework and a two-stage projection- and image-domain three-material decomposition algorithm.

Simulation-driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans.

Background: Due to the high attenuation of metals, severe artifacts occur in cone beam computed tomography (CBCT). The metal segmentation in CBCT projections usually serves as a prerequisite for metal artifact reduction (MAR) algorithms.

Purpose: The occurrence of truncation caused by the limited detector size leads to the incomplete acquisition of metal masks from the threshold-based method in CBCT volume.

Deep learning based tomosynthesis denoising: a bias investigation across different breast types.

Purpose: High noise levels due to low X-ray dose are a challenge in digital breast tomosynthesis (DBT) reconstruction. Deep learning algorithms show promise in reducing this noise. However, these algorithms can be complex and biased toward certain patient groups if the training data are not representative.

Deep learning in computed tomography super resolution using multi-modality data training.

Background: One of the limitations in leveraging the potential of artificial intelligence in X-ray imaging is the limited availability of annotated training data. As X-ray and CT shares similar imaging physics, one could achieve cross-domain data sharing, so to generate labeled synthetic X-ray images from annotated CT volumes as digitally reconstructed radiographs (DRRs). To account for the lower resolution of CT and the CT-generated DRRs as compared to the real X-ray images, we propose the use of super-resolution (SR) techniques to enhance the CT resolution before DRR generation.

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.

Background: Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo-3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative accuracy due to scatter radiation. Recent advancements in deep learning (DL) have shown promise in using fast convolutional neural networks for scatter correction, achieving comparable results to Monte Carlo (MC) simulations.

Towards a biomechanical breast model to simulate and investigate breast compression and its effects in mammography and tomosynthesis.

. In mammography, breast compression forms an essential part of the examination and is achieved by lowering a compression paddle on the breast. Compression force is mainly used as parameter to estimate the degree of compression.

Clinical prototype implementation enabling an improved day-to-day mammography compression.

Purpose: In mammography, breast compression is achieved by lowering a compression paddle on the breast. Despite the directive that compression is needed, there is no concrete guideline on its execution. To estimate the degree of compression, current mammography units only provide compression force and breast thickness as parameters.

Generative compressed breast shape model for digital mammography and digital breast tomosynthesis.

Background: Modelling of the 3D breast shape under compression is of interest when optimizing image processing and reconstruction algorithms for mammography and digital breast tomosynthesis (DBT). Since these imaging techniques require the mechanical compression of the breast to obtain appropriate image quality, many such algorithms make use of breast-like phantoms. However, if phantoms do not have a realistic breast shape, this can impact the validity of such algorithms.

Dual-Energy Cone-Beam CT with Three-Material Decomposition for Bone Marrow Edema Imaging.

We investigate the feasibility of bone marrow edema (BME) detection using a kV-switching Dual-Energy (DE) Cone-Beam CT (CBCT) protocol. This task is challenging due to unmatched x-ray paths in the low-energy (LE) and high-energy (HE) spectral channels, CBCT non-idealities such as x-ray scatter, and narrow spectral separation between fat (bone marrow) and water (BME). We propose a comprehensive DE decomposition framework consisting of projection interpolation onto matching LE and HE view angles, fast Monte Carlo scatter correction with low number of tracked photons and Gaussian denoising, and two-stage three-material decompositions involving two-material (fat-Aluminium) Projection-Domain Decomposition (PDD) followed by image-domain three-material (fat-water-bone) base-change.

Feasibility of Dual-Energy Cone-Beam CT of Bone Marrow Edema Using Dual-Layer Flat Panel Detectors.

Purpose: We investigated the feasibility of detection and quantification of bone marrow edema (BME) using dual-energy (DE) Cone-Beam CT (CBCT) with a dual-layer flat panel detector (FPD) and three-material decomposition.

Methods: A realistic CBCT system simulator was applied to study the impact of detector quantization, scatter, and spectral calibration errors on the accuracy of fat-water-bone decompositions of dual-layer projections. The CBCT system featured 975 mm source-axis distance, 1,362 mm source-detector distance and a 430 × 430 mm dual-layer FPD (top layer: 0.

Slot-scan dual-energy bone densitometry using motorized X-ray systems.

Purpose: We investigate the feasibility of slot-scan dual-energy (DE) bone densitometry on motorized radiographic equipment. This approach will enable fast quantitative measurements of areal bone mineral density (aBMD) for opportunistic evaluation of osteoporosis.

Methods: We investigated DE slot-scan protocols to obtain aBMD measurements at the lumbar spine (L-spine) and hip using a motorized x-ray platform capable of synchronized translation of the x-ray source and flat-panel detector (FPD).

Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis.

Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs makes its use challenging. Purpose To compare radiologist performance when reading single-view wide-angle DBT images with and without an artificial intelligence (AI) system for decision and navigation support. Materials and Methods A retrospective observer study was performed with bilateral mediolateral oblique examinations and corresponding synthetic two-dimensional images acquired between June 2016 and February 2018 with a wide-angle DBT system.

A phantom study on dose efficiency for orthopedic applications: Comparing slot-scanning radiography using ultra-small-angle tomosynthesis to conventional radiography.

Purpose: This paper studies the abilities of a twin-robotic x-ray slot-scanning system for orthopedic imaging to reduce dose by scatter rejection compared to conventional digital radiography.

Methods: We investigate the dose saving capabilities, especially in terms of the signal- and the contrast-to-noise ratio, as well as the scatter-to-primary ratio of the proposed slot-scanning method in comparison to the state-of-the-art method for length-extended imaging. As a baseline, we use x-ray parameters of two clinically established acquisition protocols that provide the same detector entrance dose but are profoundly different in patient dose.

Cone-beam imaging with tilted rotation axis: Method and performance evaluation.

Purpose: The recently introduced robotic x-ray systems provide the flexibility to acquire cone-beam computed tomography (CBCT) data using customized, application-specific source-detector trajectories. We exploit this capability to mitigate the effects of x-ray scatter and noise in CBCT imaging of weight-bearing foot and cervical spine (C-spine) using scan orbits with a tilted rotation axis.

Methods: We used an advanced CBCT simulator implementing accurate models of x-ray scatter, primary attenuation, and noise to investigate the effects of the orbital tilt angle in upright foot and C-spine imaging.

High-speed slot-scanning radiography using small-angle tomosynthesis: Investigation of spatial resolution.

Purpose: This paper studies spatial resolution that is achievable with a fast slot-scanning tomosynthesis approach for orthopedic examinations. Hereby, we use parallel scanning motion implemented in a twin robotic x-ray system.

Methods: We have measured and analyzed the modulation transfer function (MTF) for various combinations of scanning speed, x-ray tube voltage, pulse length, the nominal focal spot size as well as source-to-object distances.

Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment.

Assessment of breast density at the point of mammographic examination could lead to optimized breast cancer screening pathways. The onsite breast density information may offer guidance of when to recommend supplemental imaging for women in a screening program. A software application (Insight BD, Siemens Healthcare GmbH) for fast onsite quantification of volumetric breast density is evaluated.

Quarter-millimeter spectral coronary stent imaging with photon-counting CT: Initial experience.

Purpose: To evaluate the performance and clinical feasibility of 0.25 mm resolution mode of a dual-energy photon-counting detector (PCD) computed tomography (CT) system for coronary stent imaging and to compare the results to state-of-the-art dual-energy energy-integrating detector (EID) CT.

Materials And Methods: Coronary stents with different diameters (2.

Corrigendum to "Polychromatic Iterative Statistical Material Image Reconstruction for Photon-Counting Computed Tomography".

[This corrects the article DOI: 10.1155/2016/5871604.].

Spatio-energetic cross-talk in photon counting detectors: N × N binning and sub-pixel masking.

Purpose: Smaller pixel sizes of x-ray photon counting detectors (PCDs) benefit count rate capabilities but increase cross-talk and "double-counting" between neighboring PCD pixels. When an x-ray photon produces multiple (n) counts at neighboring (sub-)pixels and they are added during post-acquisition N × N binning process, the variance of the final PCD output-pixel will be larger than its mean. In the meantime, anti-scatter grids are placed at the pixel boundaries in most of x-ray CT systems and will decrease cross-talk between sub-pixels because the grids mask sub-pixels underneath them, block the primary x-rays, and increase the separation distance between active sub-pixels.

150-μm Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images.

Objective: The aims of this study were to quantitatively assess two new scan modes on a photon-counting detector computed tomography system, each designed to maximize spatial resolution, and to qualitatively demonstrate potential clinical impact using patient data.

Materials And Methods: This Health Insurance Portability Act-compliant study was approved by our institutional review board. Two high-spatial-resolution scan modes (Sharp and UHR) were evaluated using phantoms to quantify spatial resolution and image noise, and results were compared with the standard mode (Macro).

Spatio-energetic cross-talk in photon counting detectors: Numerical detector model (PcTK) and workflow for CT image quality assessment.

Purpose: The interpixel cross-talk of energy-sensitive photon counting x-ray detectors (PCDs) has been studied and an analytical model (version 2.1) has been developed for double-counting between neighboring pixels due to charge sharing and K-shell fluorescence x-ray emission followed by its reabsorption (Taguchi K, et al., Medical Physics 2016;43(12):6386-6404).

Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon-counting detector computed tomography system.

An ultra-high resolution (UHR) mode, with a detector pixel size of [Formula: see text] relative to isocenter, has been implemented on a whole body research photon-counting detector (PCD) computed tomography (CT) system. Twenty synthetic lung nodules were scanned using UHR and conventional resolution (macro) modes and reconstructed with medium and very sharp kernels. Linear regression was used to compare measured nodule volumes from CT images to reference volumes.