Impact of CT reconstruction algorithm on auto-segmentation performance.

Model-based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity-based tasks such as auto-segmentation. This work evaluates the sensitivity of an auto-contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP).

Improving Bone Mineral Density Assessment Using Spectral Detector CT.

Introduction: Bone mineral density (BMD) analysis by Dual-Energy x-ray Absorptiometry (DXA) can have some false negatives due to overlapping structures in the projections. Spectral Detector CT (SDCT) can overcome these limitations by providing volumetric information. We investigated its performance for BMD assessment and compared it to DXA and phantomless volumetric bone mineral density (PLvBMD), the latter known to systematically underestimate BMD.

Impact of a novel exponential weighted 4DCT reconstruction algorithm.

Purpose: This work characterizes a novel exponential 4DCT reconstruction algorithm (EXPO), in phantom and patient, to determine its impact on image quality as compared to the standard cosine-squared weighted 4DCT reconstruction.

Methods: A motion platform translated objects in the superior-inferior (S-I) direction at varied breathing rates (8-20 bpm) and couch pitches (0.06-0.

Machine learning-based dual-energy CT parametric mapping.

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z), relative electron density (ρ ), mean excitation energy (I ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes.

Accuracy of electron density, effective atomic number, and iodine concentration determination with a dual-layer dual-energy computed tomography system.

Purpose: This study aimed to quantitate the accuracy of the determination of electron density (ED), effective atomic number (Z ), and iodine concentration, directed for more accurate radiation therapy planning, with a new dual-layer dual-energy computed tomography (DL-DECT) system. The dependence of the accuracy of these values on the scan and reconstruction parameters, as well as on the phantom size, was also examined.

Methods: Measurements were performed on a commercial DECT system with a DL detector (IQon Spectral CT, Philips Healthcare), using phantoms with various tissue-equivalent inserts as well as iodine and calcium inserts of different concentrations.