Narrow beam breast CT: proof-of-concept.

Background: In breast CT, scattered photons form a large portion of the acquired signal, adversely impacting image quality throughout the frequency response of the imaging system. Prior studies provided evidence for a new image acquisition design, dubbed Narrow Beam Breast CT (NB-bCT), in preventing scatter acquisition.

Purpose: Here, we report the design, implementation, and initial characterization of the first NB-bCT prototype.

Hyperia: A novel methodology of developing anthropomorphic breast phantoms for X-ray imaging modalities - Part I: Concept and initial findings.

Purpose: To introduce a novel methodology for developing anthropomorphic breast phantoms for use in X-ray-based imaging modalities.

Methods: "Hyperization" is a quasi-stippling mapping operation in which regions of varying grayscale values in a 2D image are transformed into regions of varying holes on a surface. The holes can be cut or engraved on the sheet of paper using a high-resolution laser cutter/engraver.

A novel hardware duo of beam modulation and shielding to reduce scatter acquisition and dose in cone-beam breast CT.

Purpose: In cone-beam breast CT, scattered photons form a large portion of the acquired signal, adversely impacting image quality throughout the frequency response of the imaging system. Prior simulation studies provided proof of concept for utilization of a hardware solution to prevent scatter acquisition. Here, we report the design, implementation, and characterization of an auxiliary apparatus of fluence modulation and scatter shielding that does indeed lead to projections with a reduced level of scatter.

Reduction of scatter in breast CT yields improved microcalcification visibility.

The inadequate visibility of microcalcifications-small calcium deposits that cue radiologists to early stages of cancer-is a major limitation in current designs of dedicated breast computed tomography (bCT). This limitation has previously been attributed to the constituent components, spatial resolution, and utilized dose. Scattered radiation has been considered an occurrence with low-frequency impacts that can be compensated for in post-processing.

Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT.

Purpose: Clinical use of dedicated breast computed tomography (bCT) requires relatively short scan times necessitating systems with high frame rates. This in turn impacts the x-ray tube operating range. We characterize the effects of tube voltage, beam filtration, dose, and object size on contrast and noise properties related to soft tissue and iodine contrast agents as a way to optimize imaging protocols for soft tissue and iodine contrast at high frame rates.

A fluence modulation and scatter shielding apparatus for dedicated breast CT: Theory of operation.

Purpose: To introduce an auxiliary apparatus of fluence modulation and scatter shielding for dedicated breast computed tomography (bCT) and the corresponding patient-specific method of image acquisition.

Methods: The apparatus is composed of two assemblies, referred to herein as the "Dynamic Fluence Gate" (FG) and "Scatter Shield" (SS), that work in synchrony to form a narrow beam sweeping the entire fan angle coverage of the imaging system during a projection. The apparatus, as a whole, is referred to as FG-SS.

Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.

Purpose: The purpose of this work was twofold: (a) To provide a robust and accurate method for image segmentation of dedicated breast CT (bCT) volume data sets, and (b) to improve Hounsfield unit (HU) accuracy in bCT by means of a postprocessing method that uses the segmented images to correct for the low-frequency shading artifacts in reconstructed images.

Methods: A sequential and iterative application of image segmentation and low-order polynomial fitting to bCT volume data sets was used in the interleaved correction (IC) method. Image segmentation was performed through a deep convolutional neural network (CNN) with a modified U-Net architecture.