Citations

PurposeThe purpose of this study was to estimate the radiation dose to the lung and breast as well as the effective dose from tube current modulated (TCM) lung cancer screening (LCS) scans across a range of patient sizes.MethodsMonte Carlo (MC) methods were used to calculate lung, breast, and effective doses from a low‐dose LCS protocol for a 64‐slice CT that used TCM. Scanning parameters were from the protocols published by AAPM's Alliance for Quality CT. To determine lung, breast, and effective doses from lung cancer screening, eight GSF/ICRP voxelized phantom models with all radiosensitive organs identified were used to estimate lung, breast, and effective doses. Additionally, to extend the limited size range provided by the GSF/ICRP phantom models, 30 voxelized patient models of thoracic anatomy were generated from LCS patient data. For these patient models, lung and breast were semi‐automatically segmented. TCM schemes for each of the GSF/ICRP phantom models were generated using a validated method wherein tissue attenuation and scanner limitations were used to determine the TCM output as a function of table position and source angle. TCM schemes for voxelized patient models were extracted from the raw projection data. The water equivalent diameter, Dw, was used as the patient size descriptor. Dw was estimated for the GSF/ICRP models. For the thoracic patient models, Dw was extracted from the DICOM header of the CT localizer radiograph. MC simulations were performed using the TCM scheme for each model. Absolute organ doses were tallied and effective doses were calculated using ICRP 103 tissue weighting factors for the GSF/ICRP models. Metrics of scanner radiation output were determined based on each model's TCM scheme, including CTDIvol, dose length product (DLP), and CTDIvol, Low Att, a previously described regional metric of scanner output covering most of the lungs and breast. All lung and breast doses values were normalized by scan‐specific CTDIvol and CTDIvol, Low Att. Effective doses were normalized by scan‐specific CTDIvol and DLP. Absolute and normalized doses were reported as a function of Dw.ResultsLung doses normalized by CTDIvol, Low Att were modeled as an exponential relationship with respect to Dw with coefficients of determination (R2) of 0.80. Breast dose normalized by CTDIvol, Low Att was modeled with an exponential relationship to Dw with an R2 of 0.23. For all eight GSF/ICRP phantom models, the effective dose using TCM protocols was below 1.6 mSv. Effective doses showed some size dependence but when normalized by DLP demonstrated a constant behavior.ConclusionLung, breast, and effective doses from LCS CT exams with TCM were estimated with respect to patient size. Normalized lung dose can be reasonably estimated with a measure of a patient size such as Dw and regional metric of CTDIvol covering the thorax such as CTDIvol, Low Att, while normalized breast dose can also be estimated with a regional metric of CTDIvol but with a larger degree of variability than observed for lung. Effective dose normalized by DLP can be estimated with a constant multiplier.

Purpose: The aim of this study was to evaluate the ability of computer-aided detection (CAD) and human readers to detect pulmonary nodules ≥5 mm using 100 kV ultra–low-dose computed tomography (ULDCT) utilizing a tin filter. Materials and Methods: After informed consent, 55 patients prospectively underwent standard-dose chest CT (SDCT) using 120 kV followed by ULDCT using 100 kV/tin. Reference nodules ≥5 mm were identified by a thoracic radiologist using SDCT. Four thoracic radiologists marked detected nodules on SDCT and ULDCT examinations using a dedicated computer workstation. After a 6-month memory extinction, readers were shown the same ULDCT cases with all CAD markings as well as their original detections, and characterized CAD detections as true positive or false positive. Results: Volume CT Dose index (CTDIvol) for SDCT and ULDCT were 5.3±2 and 0.4±0.2 mGy (P<0.0001), respectively. Forty-five reference nodules were detected in 30 patients. Reader sensitivity varied widely but similarly for SDCT (ranging from 45% to 87%) and ULDCT (45% to 83%). CAD sensitivity was 76% (34/45) for SDCT and 71% (32/45) for ULDCT. After CAD, reader sensitivity substantially improved by 19% and 18% for 2 readers, and remained nearly unchanged for the other 2 readers (0% and 2%), despite reader perception that many more nodules were identified with CAD. There was a mean of 2 false-positive CAD detections/case. Conclusions: ULDCT with 100 kV/tin reduced patient dose by over 90% without compromising pulmonary nodule detection sensitivity. CAD can substantially improve nodule detection sensitivity at ULDCT for some readers, maintaining interobserver performance.

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). Patients were scanned using a conventional energy-integrating detector scanner and the photon-counting detector scanner using the same radiation dose. In first patient images, anatomic details were qualitatively evaluated to demonstrate potential clinical impact. Results Sharp and UHR modes had a 69% and 87% improvement in in-plane spatial resolution, respectively, compared with Macro mode (10% modulation-translation-function values of 16.05, 17.69, and 9.48 lp/cm, respectively). The cutoff spatial frequency of the UHR mode (32.4 lp/cm) corresponded to a limiting spatial resolution of 150 μm. The full-width-at-half-maximum values of the section sensitivity profiles were 0.41, 0.44, and 0.67 mm for the thinnest image thickness for each mode (0.25, 0.25, and 0.5 mm, respectively). At the same in-plane spatial resolution, Sharp and UHR images had up to 15% lower noise than Macro images. Patient images acquired in Sharp mode demonstrated better delineation of fine anatomic structures compared with Macro mode images. Conclusions Phantom studies demonstrated superior resolution and noise properties for the Sharp and UHR modes relative to the standard Macro mode and patient images demonstrated the potential benefit of these scan modes for clinical practice.

AbstractObjectivesBoth projection and dual‐energy (DE)‐based methods have been used for metal artifact reduction (MAR) in CT. The two methods can also be combined. The purpose of this work was to evaluate these three MAR methods using phantom experiments for five types of metal implants.Materials and MethodsFive phantoms representing spine, dental, hip, shoulder, and knee were constructed with metal implants. These phantoms were scanned using both single‐energy (SE) and DE protocols with matched radiation output. The SE data were processed using a projection‐based MAR (iMAR, Siemens) algorithm, while the DE data were processed to generate virtual monochromatic images at high keV (Mono+, Siemens). In addition, the DE images after iMAR were used to generate Mono+ images (DE iMAR Mono+). Artifacts were quantitatively evaluated using CT numbers at different regions of interest. Iodine contrast‐to‐noise ratio (CNR) was evaluated in the spine phantom. Three musculoskeletal radiologists and two neuro‐radiologists independently ranked the artifact reduction.ResultsThe DE Mono+ at high keV resulted in reduced artifacts but also lower iodine CNR. The iMAR method alone caused missing tissue artifacts in dental phantom. DE iMAR Mono+ caused wrong CT numbers in close proximity to the metal prostheses in knee and hip phantoms. All musculoskeletal radiologists ranked SE iMAR > DE iMAR Mono+ > DE Mono+ for knee and hip, while DE iMAR Mono+ > SE iMAR > DE Mono+ for shoulder. Both neuro‐radiologists ranked DE iMAR Mono+ > DE Mono+ > SE iMAR for spine and DE Mono+ > DE iMAR Mono+ > SE iMAR for dental.ConclusionsThe SE iMAR was the best choice for the hip and knee prostheses, while DE Mono+ at high keV was best for dental implants and DE iMAR Mono+ was best for spine and shoulder prostheses. Artifacts were also introduced by MAR algorithms.

Objectives The ability to determine the severity of renal fibrosis, which is involved in most chronic kidney diseases, may be beneficial for monitoring disease progression and management. The aim of this study was to assess a new method involving gold nanoparticles conjugated to an anti-collagen-I antibody (Co-I-AuNPs) as a computed tomography (CT) imaging contrast for the evaluation of renal fibrosis in situ. Materials and Methods Gold nanoparticles conjugated to an anti-collagen-I antibody were prepared using gold chloride reduction with sodium citrate and coated with polyethylene glycol (PEG), and their size was determined by electron microscopy and nanoparticle tracking analysis. Anti-collagen-I antibody was then conjugated to PEG-SH/COOH on the AuNP surface. The success of antibody conjugation was tested in vitro using collagen-coated plate and mouse stenotic kidney sections and in vivo using micro-CT and multidetector CT imaging. Results Bare AuNPs were 18.7 ± 0.6 nm and PEG-coated AuNPs were 45.3 ± 0.1 nm in size. In vitro, Co-I-AuNPs specifically bound to both a collagen-coated plate and mouse fibrotic kidneys. Furthermore, the stenotic mouse kidney showed increased Co-I-AuNPs retention compared with the contralateral kidney (59.3 ± 5.1 vs 45.1 ± 1.7 HU, P = 0.05), which correlated with its collagen deposition. Micro-CT also detected gold signals in situ in the Co-I-AuNP-injected kidney, which colocalized with histological trichrome staining. Conclusion Gold nanoparticles conjugated to an anti-collagen-I antibody are able to visualize kidney fibrosis in vitro and in situ and may be useful for nondestructive quantification of tissue fibrosis.

Background Dental hardware produces streak artifacts on computed tomography (CT) images reconstructed with the standard weighted filtered back projection (wFBP) method. Purpose To perform a preliminary evaluation of an iterative metal artifact reduction (IMAR) technique to assess its ability to improve anatomic visualization over wFBP in patients with dental amalgam or other hardware. Material and Methods CT images from patients with dental hardware were reconstructed using wFBP and IMAR software and soft-tissue or bone window/level settings. The anatomy most affected by metal artifacts was identified. Two neuroradiologists determined subjective and objective imaging features, including overall metal artifact score (1 = severe artifacts, 5 = no artifacts), soft-tissue visualization score of the most-compromised structure, and artifact length along the skin surface. CT numbers were used to quantify artifact severity. Results Twenty-four patients were included. IMAR improved overall metal artifact score in 18/24 cases (median =2 ± 0.9 vs. 1 ± 0.6, P < 0.001). Mean CT number in the most-affected anatomical structure significantly improved with IMAR (94.6 vs. 219 HU, P = 0.002) and length of affected skin surface decreased (40.4 mm vs. 118.7 mm, P < 0.001). However, osseous/dental artifactual defects were found in 22/24 cases with IMAR vs. 11/24 with wFBP. Conclusion IMAR software reduced metal artifact both subjectively and objectively and improved visualization of adjacent soft tissues. However, it produced a higher rate of artifactual defects in the teeth and bones than wFBP. Our findings support the use of IMAR as a valuable complement to, but not a replacement for, standard wFBP image reconstruction.