Non-unique water and contrast agent solutions in dual-energy CT.

The goal of this work is to study occurrences of non-unique solutions in dual-energy CT (DECT) for objects containing water and a contrast agent. Previous studies of the Jacobian of nonlinear systems identified that a vanishing Jacobian determinant indicates the existence of multiple solutions to the system. Vanishing Jacobian determinants are identified for DECT setups by simulating intensity data for practical thickness ranges of water and contrast agent.

A directional total variation minimization algorithm for isotropic resolution in digital breast tomosynthesis.

An optimization-based image reconstruction algorithm is developed for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm minimizes directional total variation (TV) with a data discrepancy and non-negativity constraints. Iodinated contrast agent (ICA) imaging is performed by reconstructing images from dual-energy DBT data followed by weighted subtraction.

Implementation of a high-resolution, high-contrast magnetic resonance imaging protocol with extended delayed phases for peritoneal mesothelioma.

Background: Imaging of peritoneal malignancies using conventional cross-sectional imaging is challenging, but accurate assessment of peritoneal disease burden could guide better selection for definitive surgery. Here we demonstrate feasibility of high-resolution, high-contrast magnetic resonance imaging (MRI) of peritoneal mesothelioma and explore optimal timing for delayed post-contrast imaging.

Methods: Prospective data from inpatients with malignant peritoneal mesothelioma (MPM), imaged with a novel MRI protocol, were analyzed.

Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio, a theoretical study.

Background: Dual-energy CT (DECT) systems provide valuable material-specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast-to-noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x-ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall image quality.

Purpose: The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiation dose.

Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function.

Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images. However, networks trained with such pixel-wise losses are prone to wipe out small, low-contrast features that are critical for screening and diagnosis. To remedy this issue, we introduce a novel training loss inspired by the model observer framework to enhance the detectability of weak signals in the reconstructions.

Technical specifications of dose management systems: An international atomic energy agency survey.

Purpose: Dose management systems (DMS) have been introduced in radiological services to facilitate patient radiation dose management and optimization in medical imaging. The purpose of this study was to gather as much information as possible on the technical characteristics of DMS currently available, regarding features that may be considered essential for simply ensuring regulatory compliance or desirable to fully utilize the potential role of DMS in optimization of many aspects of radiological examinations.

Methods: A technical survey was carried out and all DMS developers currently available (both commercial and open source) were contacted and were asked to participate.

Lesion detection in digital breast tomosynthesis: human reader experiments indicate no benefit from the integration of information from multiple planes.

Purpose: In digital breast tomosynthesis (DBT), radiologists need to review a stack of 20 to 80 tomosynthesis images, depending upon breast size. This causes a significant increase in reading time. However, it is currently unknown whether there is a perceptual benefit to viewing a mass in the 3D tomosynthesis volume.

AAPM task group report 305: Guidance for standardization of vendor-neutral reject analysis in radiography.

Reject rate analysis is considered an integral part of a diagnostic radiography quality control (QC) program. A rejected image is a patient radiograph that was not presented to a radiologist for diagnosis and that contributes unnecessary radiation dose to the patient. Reject rates that are either too high or too low may suggest systemic department shortcomings in QC mechanisms.

Technical note: Water-equivalent thickness of Superflab bolus material at diagnostic x-ray energies.

Purpose: The purpose of this work was to determine the water-equivalent thickness of Superflab bolus material for narrow and broad field-of-view (FOV) x-ray geometries at diagnostic x-ray energies.

Methods: Transmission measurements were performed for incremental thicknesses of Superflab bolus material and water in narrow and broad FOV x-ray geometries. The transmission data was fit to a non-linear model for x-ray transmission - the Archer model.

A Clinically Optimal Protocol for the Imaging of Enteric Tubes: On the Basis of Radiologist Interpreted Diagnostic Utility and Radiation Dose Reduction.

Rationale And Objectives: The purpose of this study was to develop and evaluate a patient thickness-based protocol specifically for the confirmation of enteric tube placements in bedside abdominal radiographs. Protocol techniques were set to maintain image quality while minimizing patient dose.

Materials And Methods: A total of 226 pre-intervention radiographs were obtained to serve as a baseline cohort for comparison.

High-Resolution Full-3D Specimen Imaging for Lumpectomy Margin Assessment in Breast Cancer.

Background: Two-dimensional (2D) specimen radiography (SR) and tomosynthesis (DBT) for breast cancer yield data that lack high-depth resolution. A volumetric specimen imager (VSI) was developed to provide full-3D and thin-slice cross-sectional visualization at a 360° view angle. The purpose of this prospective trial was to compare VSI, 2D SR, and DBT interpretation of lumpectomy margin status with the final pathologic margin status of breast lumpectomy specimens.

Development of a neonate X-ray phantom for 2D imaging applications using single-tone inkjet printing.

Purpose: Inkjet printers can be used to fabricate anthropomorphic phantoms by the use of iodine-doped ink. However, challenges persist in implementing this technique. The calibration from grayscale to ink density is complex and time-consuming.

A signal detection model for quantifying overregularization in nonlinear image reconstruction.

Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for nonlinear image reconstruction. The vast majority of metrics employed for evaluating nonlinear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE). Use of such metrics can lead to overregularization in the sense that they can favor removal of subtle details in the image.

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation.

The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose.

Iodine quantification in limited angle tomography.

Purpose: To develop and test the feasibility of a two-pass iterative reconstruction algorithm with material decomposition designed to obtain quantitative iodine measurements in digital breast tomosynthesis.

Methods: Contrast-enhanced mammography has shown promise as a cost-effective alternative to magnetic resonance imaging for imaging breast cancer, especially in dense breasts. However, one limitation is the poor quantification of iodine contrast since the true three-dimensional lesion shape cannot be inferred from the two-dimensional (2D) projection.

Imaging of fiber-like structures in digital breast tomosynthesis.

Fiber-like features are an important aspect of breast imaging. Vessels and ducts are present in all breast images, and spiculations radiating from a mass can indicate malignancy. Accordingly, fiber objects are one of the three types of signals used in the American College of Radiology digital mammography (ACR-DM) accreditation phantom.

Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification.

Purpose: Many computer aided diagnosis (CADx) tools for breast cancer begin by fully or semiautomatically segmenting a given breast lesion and then classifying the lesion's likelihood of malignancy using quantitative features extracted from the image. It is often assumed that better segmentation will result in better classification. However, this has not been thoroughly evaluated.

Inter-laboratory comparison of channelized hotelling observer computation.

Purpose: The task-based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well-established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise.

Neutrosophic segmentation of breast lesions for dedicated breast computed tomography.

We proposed the neutrosophic approach for segmenting breast lesions in breast computed tomography (bCT) images. The neutrosophic set considers the nature and properties of neutrality (or indeterminacy). We considered the image noise as an indeterminate component while treating the breast lesion and other breast areas as true and false components.

Analysis of Volume Overestimation Artifacts in the Breast Outline Segmentation in Tomosynthesis.

In digital breast tomosynthesis (DBT), the reconstruction is calculated from x-ray projection images acquired over a small range of angles. One step in the reconstruction process is to identify the pixels that fall outside the shadow of the breast, to segment the breast from the background (air). In each projection, rays are back-projected from these pixels to the focal spot.

Portable abdomen radiography: moving to thickness-based protocols.

Background: Default pediatric protocols on many digital radiography systems are configured based on patient age. However, age does not adequately characterize patient size, which is the principal determinant of proper imaging technique. Use of default pediatric protocols by inexperienced technologists can result in patient overexposure, inadequate image quality, or repeated examinations.

Clinical Significance of US Artifacts.

Artifacts are frequently encountered at clinical US, and while some are unwanted, others may reveal valuable information related to the structure and composition of the underlying tissue. They are essential in making ultrasonography (US) a clinically useful imaging modality but also can lead to errors in image interpretation and can obscure diagnoses. Many of these artifacts can be understood as deviations from the assumptions made in generating the image.

Lack of agreement between radiologists: implications for image-based model observers.

We tested the agreement of radiologists' rankings of different reconstructions of breast computed tomography images based on their diagnostic (classification) performance and on their subjective image quality assessments. We used 102 pathology proven cases (62 malignant, 40 benign), and an iterative image reconstruction (IIR) algorithm to obtain 24 reconstructions per case with different image appearances. Using image feature analysis, we selected 3 IIRs and 1 clinical reconstruction and 50 lesions.

Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT.

Purpose: The purpose of this study is to determine the optimal representative reconstruction and quantitative image feature set for a computer-aided diagnosis (CADx) scheme for dedicated breast computer tomography (bCT).

Method: We used 93 bCT scans that contain 102 breast lesions (62 malignant, 40 benign). Using an iterative image reconstruction (IIR) algorithm, we created 37 reconstructions with different image appearances for each case.

Unified Database for Rejected Image Analysis Across Multiple Vendors in Radiography.

Reject rate analysis has been part of radiography departments' quality control since the days of screen-film radiography. In the era of digital radiography, one might expect that reject rate analysis is easily facilitated because of readily available information produced by the modality during the examination procedure. Unfortunately, this is not always the case.