Comparison of enhancement quantification from virtual unenhanced images to true unenhanced images in multiphase renal Dual-Energy computed tomography: A phantom study.

Multiphase computed tomography (CT) exams are a commonly used imaging technique for the diagnosis of renal lesions and involve the acquisition of a true unenhanced (TUE) series followed by one or more postcontrast series. The difference in CT number of the mass in pre- and postcontrast images is used to quantify enhancement, which is an important criterion used for diagnosis. This study sought to assess the feasibility of replacing TUE images with virtual unenhanced (VUE) images derived from Dual-Energy CT datasets in renal CT exams. Eliminating TUE image acquisition could reduce patient dose and improve clinical efficiency. A rapid kVp-switching CT scanner was used to assess enhancement accuracy when using VUE compared to TUE images as the baseline for enhancement calculations across a wide range of clinical scenarios simulated in a phantom study. Three phantoms were constructed to simulate small, medium, and large patients, each with varying lesion size and location. Nonenhancing cystic lesions were simulated using distilled water. Intermediate (10-20 HU [Hounsfield units]) and positively enhancing masses (≥20 HU) were simulated by filling the spherical inserts in each phantom with varied levels of iodinated contrast mixed with a blood surrogate. The results were analyzed using Bayesian hierarchical models. Posterior probabilities were used to classify enhancement measured using VUE compared to TUE images as significantly less, not significantly different, or significantly higher. Enhancement measured using TUE images was considered the ground truth in this study. For simulation of nonenhancing renal lesions, enhancement values were not significantly different when using VUE versus TUE images, with posterior probabilities ranging from 0.23-0.56 across all phantom sizes and an associated specificity of 100%. However, for simulation of intermediate and positively enhancing lesions significant differences were observed, with posterior probabilities < 0.05, indicating significantly lower measured enhancement when using VUE versus TUE images. Positively enhancing masses were categorized accurately, with a sensitivity of 91.2%, when using VUE images as the baseline. For all scenarios where iodine was present, VUE-based enhancement measurements classified lesions with a sensitivity of 43.2%, a specificity of 100%, and an accuracy of 78.1%. Enhancement calculated using VUE images proved to be feasible for classifying nonenhancing and highly enhancing lesions. However, differences in measured enhancement for simulation of intermediately enhancing lesions demonstrated that replacement of TUE with VUE images may not be advisable for renal CT exams.

Evaluation of a deterministic grid-based Boltzmann solver (GBBS) for voxel-level absorbed dose calculations in nuclear medicine.

To evaluate the 3D Grid-based Boltzmann Solver (GBBS) code ATTILA (®) for coupled electron and photon transport in the nuclear medicine energy regime for electron (beta, Auger and internal conversion electrons) and photon (gamma, x-ray) sources. Codes rewritten based on ATTILA are used clinically for both high-energy photon teletherapy and (192)Ir sealed source brachytherapy; little information exists for using the GBBS to calculate voxel-level absorbed doses in nuclear medicine. We compared DOSXYZnrc Monte Carlo (MC) with published voxel-S-values to establish MC as truth. GBBS was investigated for mono-energetic 1.0, 0.1, and 0.01 MeV electron and photon sources as well as (131)I and (90)Y radionuclides. We investigated convergence of GBBS by analyzing different meshes ([Formula: see text]), energy group structures ([Formula: see text]) for each radionuclide component, angular quadrature orders ([Formula: see text], and scattering order expansions ([Formula: see text]-[Formula: see text]); higher indices imply finer discretization. We compared GBBS to MC in (1) voxel-S-value geometry for soft tissue, lung, and bone, and (2) a source at the interface between combinations of lung, soft tissue, and bone. Excluding Auger and conversion electrons, MC agreed within ≈5% of published source voxel absorbed doses. For the finest discretization, most GBBS absorbed doses in the source voxel changed by less than 1% compared to the next finest discretization along each phase space variable indicating sufficient convergence. For the finest discretization, agreement with MC in the source voxel ranged from -3% to -20% with larger differences at lower energies (-3% for 1 MeV electron in lung to -20% for 0.01 MeV photon in bone); similar agreement was found for the interface geometries. Differences between GBBS and MC in the source voxel for (90)Y and (131)I were -6%. The GBBS ATTILA was benchmarked against MC in the nuclear medicine regime. GBBS can be a viable alternative to MC for voxel-level absorbed doses in nuclear medicine. However, reconciliation of the differences between GBBS and MC at lower energies requires further investigation of energy deposition cross-sections.

Cone Beam Breast CT with a Flat Panel Detector- Simulation, Implementation and Demonstration.

This paper describes our experiences in the simulation, implementation and application of a flat panel detector based cone beam computed tomography (CT) imaging system for dedicated 3-D breast imaging. In our simulation study, the breast was analytically modeled as a cylinder of breast tissue loosely molded into cylindrical shape with embedded soft tissue masses and calcifications. Attenuation coefficients for various types of breast tissue, soft tissue masses and calcifications were estimated for various kVp's to generate simulated image signals. Projection images were computed to incorporate x-ray attenuation, geometric magnification, x-ray detection, detector blurring, image pixelization and digitization. Based on the x-ray kVp/filtration used, transmittance through the phantom, detective quantum efficiency (DQE), exposure level, and imaging geometry, the photon fluence was estimated and used to compute the quantum noise level on a pixel-by-pixel basis for various dose levels at the isocenter. This estimated noise level was then used with a random number generator to generate and add a fluctuation component to the noiseless transmitted image signal. The noise carrying projection images were then convolved with a Gaussian-like kernel, computed from measured 1-D line spread function (LSF) to simulate detector blurring. Additional 2-D Gaussian filtering was applied to the projection images and tested for improving the detection of soft tissue masses and calcifications in the reconstructed images. Reconstruction was performed using the Feldkamp filtered backprojection algorithm. All simulations were performed on a 24 PC (2.4 GHz Dual-Xeon CPU) cluster with MPI parallel programming.