Citations

AbstractIn the last decade or so, a number of disruptive technological advances have taken place in x-ray computed tomography, making possible new clinical applications. Changes in scanner design have included the use of two x-ray sources and two detectors or the use of large detector arrays that provide 16 cm of longitudinal coverage in one gantry rotation. These advances have allowed images of the entire heart to be acquired in just one heartbeat, lowering the effective dose from cardiac computed tomography from ~15 mSv to <1 mSv. Dual-energy computed tomography is now in widespread clinical use, enabling the assessment of material composition and concentration, as well as a range of new clinical applications. An emerging technology known as photon-counting detector computed tomography directly measures the energies of detected photons and is capable of simultaneously acquiring more than two energy data sets. Photon-counting detector computed tomography also provides advantages such as the ability to reject electronic noise, better iodine contrast-to-noise for a given dose, and spatial resolution as fine as 150 μm. Optimized x-ray tube potential selection has allowed reduction in radiation and contrast doses. Finally, wide adoption of iterative reconstruction and noise-reduction techniques has occurred. In all, body computed tomography doses have fallen dramatically, for example, by over a factor of 3 from the early 1980s. All of these advances increase the medical benefit and decrease the potential radiation risk associated with computed tomography. However, care must be taken to ensure that doses are not lowered to the level at which the clinical task is compromised.

PurposeSize‐specific dose estimates (SSDE) conversion factors have been determined byAAPMReport 204 to adjustCTDIvolto account for patient size but were limited to bodyCTexaminations. The purpose of this work was to determine conversion factors that could be used for anSSDEfor helical, headCTexaminations for patients of different sizes.MethodsValidated Monte Carlo (MC) simulation methods were used to estimate dose to the center of the scan volume from a routine, helical head examination for a group of patient models representing a range of ages and sizes. Ten GSF/ICRP voxelized phantom models and five pediatric voxelized patient models created from CT image data were used in this study. CT scans were simulated using a Siemens multidetector row CT equivalent source model. Scan parameters were taken from the AAPM Routine Head protocols for a fixed tube current (FTC), helical protocol, and scan lengths were adapted to the anatomy of each patient model. MC simulations were performed using mesh tallies to produce voxelized dose distributions for the entire scan volume of each model. Three tally regions were investigated: (1) a small 0.6 cc volume at the center of the scan volume, (2) 0.8–1.0 cm axial slab at the center of the scan volume, and (3) the entire scan volume. Mean dose to brain parenchyma for all three regions was calculated. Mean bone dose and a mass‐weighted average dose, consisting of brain parenchyma and bone, were also calculated for the slab in the central plane and the entire scan volume. All dose measures were then normalized by CTDIvolfor the 16 cm phantom (CTDIvol,16). Conversion factors were determined by calculating the relationship between normalized doses and water equivalent diameter (Dw).ResultsCTDIvol,16‐normalized mean brain parenchyma dose values within the 0.6 cc volume, 0.8–1.0 cm central axial slab, and the entire scan volume, when parameterized byDw, had an exponential relationship with a coefficient of determination (R2) of 0.86, 0.84, and 0.88, respectively. There was no statistically significant difference between the conversion factors resulting from these three different tally regions. Exponential relationships between CTDIvol,16‐normalized mean bone doses hadR2values of 0.83 and 0.87 for the central slab and for the entire scan volume, respectively. CTDIvol,16‐normalized mass‐weighted average doses hadR2values of 0.39 and 0.51 for the central slab and for the entire scan volume, respectively.ConclusionsConversion factors that describe the exponential relationship between CTDIvol,16‐normalized mean brain dose and a size metric (Dw) for helical head CT examinations have been reported for two different interpretations of the center of the scan volume. These dose descriptors have been extended to describe the dose to bone in the center of the scan volume as well as a mass‐weighted average dose to brain and bone. These may be used, when combined with other efforts, to develop an SSDE dose coefficients for routine, helical head CT examinations.

Objectives The aim of this study was to investigate the impact on metal artifacts and dose efficiency of using a tin filter in combination with high-energy threshold (TH) images of a photon-counting detector (PCD) computed tomography (CT) system. Materials and Methods A 3D-printed spine with pedicle screws was scanned on a PCD-CT system with and without tin filtration. Image noise and severity of artifacts were measured for low-energy threshold (TL) and TH images. In a prospective, institutional review board–approved, Health Insurance Portability and Accountability Act-compliant study, 20 patients having a clinical energy-integrating detector (EID) CT were scanned on a PCD-CT system using tin filtration. Images were reviewed by 3 radiologists to evaluate visualization of anatomic structures, diagnostic confidence, and image preference. Artifact severity and image noise were measured. Wilcoxon signed rank was used to test differences between PCD-CT TH and EID-CT images. Results Phantom TH images with tin filtration reduced metal artifacts and had comparable noise (32 HU) to TL images (29 HU) acquired without tin filtration. Visualization scores for the cortex, trabeculae, and implant-trabecular interface from PCD-CT TH images (4.4 ± 0.9, 4.4 ± 1.0, and 4.4 ± 1.0) were significantly higher (P < 0.0001) than EID-CT images (3.3 ± 1.3, 3.3 ± 1.2, and 3.3 ± 1.6). A strong preference was shown for PCD-CT TH images due to improved diagnostic confidence and decreased artifact severity. Noise in PCD-CT TH images (93 ± 41 HU) was significantly lower than that in EID-CT images (133 ± 92 HU, P < 0.05). Conclusions Threshold high images acquired with tin filtration on PCD-CT demonstrated a substantial decrease in metal artifacts and an increase in dose efficiency compared with EID-CT.

Objectives The aim of this study was to evaluate if a high-resolution photon-counting detector computed tomography (PCD-CT) system with a 1024×1024 matrix reconstruction can improve the visualization of fine structures in the lungs compared with conventional high-resolution CT (HRCT). Materials and Methods Twenty-two adult patients referred for clinical chest HRCT (mean CTDI vol, 13.58 mGy) underwent additional dose-matched PCD-CT (mean volume CT dose index, 13.37 mGy) after written informed consent. Computed tomography images were reconstructed at a slice thickness of 1.5 mm and an image increment of 1 mm with our routine HRCT reconstruction kernels (B46 and Bv49) at 512 and 1024 matrix sizes for conventional energy-integrating detector (EID) CT scans. For PCD-CT, routine B46 kernel and an additional sharp kernel (Q65, unavailable for EID) images were reconstructed at 1024 matrix size. Two thoracic radiologists compared images from EID and PCD-CT noting the highest level bronchus clearly identified in each lobe of the right lung, and rating bronchial wall conspicuity of third- and fourth-order bronchi. Lung nodules were also compared with the B46/EID/512 images using a 5-point Likert scale. Statistical analysis was performed using a Wilcoxon signed rank test with a P < 0.05 considered significant. Results Compared with B46/EID/512, readers detected higher-order bronchi using B46/PCD/1024 and Q65/PCD/1024 images for every lung lobe (P < 0.0015), but in only the right middle lobe for B46/EID/1024 (P = 0.007). Readers were able to better identify bronchial walls of the third- and fourth-order bronchi better using the Q65/PCD/1024 images (mean Likert scores of 1.1 and 1.5), which was significantly higher compared with B46/EID/1024 or B46/PCD/1024 images (mean difference, 0.8; P < 0.0001). The Q65/PCD/1024 images had a mean nodule score of 1 ± 1.3 for reader 1, and −0.1 (0.9) for reader 2, with one reader having improved nodule evaluation scores for both PCD kernels (P < 0.001), and the other reader not identifying any increased advantage over B46/EID/1024 (P = 1.0). Conclusions High-resolution lung PCD-CT with 1024 image matrix reconstruction increased radiologists' ability to visualize higher-order bronchi and bronchial walls without compromising nodule evaluation compared with current chest CT, creating an opportunity for radiologists to better evaluate airway pathology.

Purpose Photon counting detectors (PCDs) are being introduced in advanced x‐ray computed tomography (CT) scanners. From a single PCD‐CT acquisition, multiple images can be reconstructed, each based on only a part of the original x‐ray spectrum. In this study, we investigated whether PCD‐CT can be used to estimate stopping power ratios (SPRs) for proton therapy treatment planning, both by comparing to other SPR methods proposed for single energy CT (SECT) and dual energy CT (DECT) as well as to experimental measurements. Methods A previously developed DECT‐based SPR estimation method was adapted to PCD‐CT data, by adjusting the estimation equations to allow for more energy spectra. The method was calibrated directly on noisy data to increase the robustness toward image noise. The new PCD SPR estimation method was tested in theoretical calculations as well as in an experimental setup, using both four and two energy bin PCD‐CT images, and through comparison to two other SPR methods proposed for SECT and DECT. These two methods were also evaluated on PCD‐CT images, full spectrum (one‐bin) or two‐bin images, respectively. In a theoretical framework, we evaluated the effect of patient‐specific tissue variations (density and elemental composition) and image noise on the SPR accuracy; the latter effect was assessed by applying three different noise levels (low, medium, and high noise). SPR estimates derived using real PCD‐CT images were compared to experimentally measured SPRs in nine organic tissue samples, including fat, muscle, and bone tissues. Results For the theoretical calculations, the root‐mean‐square error (RMSE) of the SPR estimation was 0.1% for the new PCD method using both two and four energy bins, compared to 0.2% and 0.7% for the DECT‐ and SECT‐based method, respectively. The PCD method was found to be very robust toward CT image noise, with a RMSE of 2.7% when high noise was added to the CT numbers. Introducing tissue variations, the RMSE only increased to 0.5%; even when adding high image noise to the changed tissues, the RMSE stayed within 3.1%. In the experimental measurements, the RMSE over the nine tissue samples was 0.8% when using two energy bins, and 1.0% for the four‐bin images. Conclusions In all tested cases, the new PCD method produced similar or better results than the SECT‐ and DECT‐based methods, showing an overall improvement of the SPR accuracy. This study thus demonstrated that PCD‐CT scans will be a qualified candidate for SPR estimations.