Citations

PurposeThe aim of this study was to demonstrate that a low helical pitch causes increased photon starvation artifacts at ultra‐low‐dose CT.MethodsA cylindrical water phantom with a diameter of 30 cm was scanned on two different generation CT scanners: a 64‐slice scanner (Sensation 64, Siemens Healthcare) and a 192‐slice scanner (Somatom Force, Siemens Healthcare) at multiple effective mAs levels (mAs/pitch = 200, 100, 50, 25, and 12). The corresponding CTDIvol values were 4.1, 2.0, 1.0, 0.5 mGy, on the 64‐slice scanner and 3.8, 1.9, 1.0, 0.5 mGy on the 192‐slice scanner, for the selected effective mAs values. For each dose setting, the scan was repeated at four helical pitches: 1.2, 0.9, 0.6, and the lowest achievable pitch on each scanner. The tube current was automatically adjusted by the scanner so that the effective mAs, and thus CTDIvol, were kept the same for different pitches. All CT data sets were reconstructed with a slice thickness of 3mm and a medium smooth kernel. Images acquired at the same dose level but different helical pitches were visually inspected to assess photon starvation artifacts and noise levels.ResultsAt the same radiation dose, image noise increased with the decreasing helical pitch. The increase was more severe on the old‐generation 64‐slice scanner. Photon starvation artifacts were evident at 200 effective mAs on the 64‐slice scanner at 80 kV. On the 192‐slice scanner there was no visible photon starvation artifacts at both 200 and 50 effective mAs (CTDIvol = 4.1 mGy and 1.0 mGy, respectively); nor was there a visible impact from the lower helical pitch. Only when the dose was lowered to be extremely low (~0.26 mGy, achievable at 70 kV), did photon starvation artifacts become evident.ConclusionsA low helical pitch may increase image noise and photon starvation artifacts compared to a higher pitch for the same dose level, particularly at ultra‐low dose CT.

Objective The aim of this study was to determine if computed tomography (CT) angiography using an individualized transition delay (CTA-ID) would facilitate reductions in injection rate and iodine dose. Methods The CTA-ID was performed in 20 patients with routine injection rate and iodine dose; 20 patients with injection rate lowered by 1 mL/s; and 40 patients with injection rate lowered by 1 mL/s with 29% less iodine. Routine CTAs in the same or size-matched patients served as controls. Diagnostic image quality and intra-arterial CT numbers were assessed. Results The median transition delay between aortic threshold and CTA-ID image acquisition was significantly longer than with conventional bolus tracking (mean increase, 13.3 seconds; P < 0.0001), with image quality being the same or better. Intra-arterial CT numbers were 200 Hounsfield units or greater for 80 of 80 CTA-ID, but not for 6 of 49 (12%) internal control or for 11 of 80 (14%) size-matched control patients. Conclusion The CTA-ID bolus-tracking software alters transition delays to permit diagnostic CTA examinations despite slower injection rate and less iodine.

PurposePhoton‐counting‐detector‐computed tomography (PCD‐CT) allows separation of multiple, simultaneously imaged contrast agents, such as iodine (I), gadolinium (Gd), and bismuth (Bi). However, PCDs suffer from several technical limitations such as charge sharing, K‐edge escape, and pulse pile‐up, which compromise spectral separation of multi‐energy data and degrade multi‐contrast imaging performance. The purpose of this work was to determine the performance of a dual‐source (DS) PCD‐CT relative to a single‐source (SS) PCD‐CT for the separation of simultaneously imaged I, Gd, and Bi contrast agents.MethodsPhantom experiments were performed using a research whole‐body PCD‐CT and head/abdomen‐sized phantoms containing vials of different I, Gd, Bi concentrations. To emulate a DS‐PCD‐CT, the phantoms were scanned twice on the SS‐PCD‐CT using different tube potentials for each scan. A tube potential of 80 kV (energy thresholds = 25/50 keV) was used for low‐energy tube, while the high‐energy tube used Sn140 kV (Sn indicates tin filter) and thresholds of 25/90 keV. The same phantoms were scanned also on the SS‐PCD‐CT using the chess acquisition mode. In chess mode, the 4 × 4 subpixels within a macro detector pixel are split into two sets based on a chess‐board pattern. With each subpixel set having two energy thresholds, chess mode allows four energy‐bin data sets, which permits simultaneous multi‐contrast imaging. Because of this design, only 50% area of each detector pixel is configured to receive photons of a pre‐defined threshold, leading to 50% dose utilization efficiency. To compensate for this dose inefficiency, the radiation dose for this scan was doubled compared to DS‐PCD‐CT. A 140 kV tube potential and thresholds = 25/50/75/90 keV were used. These settings were determined based on the K‐edges of Gd, and Bi, and were found to yield good differentiation of I/Gd/Bi based on phantom experiments and other literature. The energy‐bin images obtained from each scan (scan pair) were used to generate I‐, Gd‐, Bi‐specific image via material decomposition. Root‐mean‐square‐error (RMSE) between the known and measured concentrations was calculated for each scenario. A 20‐cm water cylinder phantom was scanned on both systems, which was used for evaluating the magnitude of noise, and noise power spectra (NPS) of I/Gd/Bi‐specific images.ResultsPhantom results showed that DS‐PCD‐CT reduced noise in material‐specific images for both head and body phantoms compared to SS‐PCD‐CT. The noise level of SS‐PCD was reduced from 2.55 to 0.90 mg/mL (I), 1.97 to 0.78 mg/mL (Gd), and 0.85 to 0.74 mg/mL (Bi) using DS‐PCD. NPS analysis showed that the noise texture of images acquired on both systems is similar. For the body phantom, the RMSE for SS‐PCD‐CT was reduced relative to DS‐PCD‐CT from 10.52 to 2.76 mg/mL (I), 7.90 to 2.01 mg/mL (Gd), and 1.91 to 1.16 mg/mL (Bi). A similar trend was observed for the head phantom: RMSE reduced from 2.59 (SS‐PCD) to 0.72 (DS‐PCD) mg/mL (I), 2.02 to 0.58 mg/mL (Gd), and 0.85 to 0.57 mg/mL (Bi).ConclusionWe demonstrate the feasibility of performing simultaneous imaging of I, Gd, and Bi materials on DS‐PCD‐CT. Under the condition without cross scattering, DS‐PCD reduced the RMSE for quantification of material concentration in relative to a SS‐PCD‐CT system using chess mode.

Significance Statement Kidney stone recurrence can manifest with symptoms from stone passage or with asymptomatic changes in kidney stone burden revealed by radiography. In a multicenter, prospective cohort study, the authors demonstrate that substantially more asymptomatic and self-managed symptomatic kidney stone recurrence occur in stone formers than has been appreciated, as many studies consider only symptomatic recurrence that results in clinical care. Among first-time symptomatic stone formers, 54% had an asymptomatic kidney stone; half of those with such a stone passed it within 5 years (but only half who did so experienced symptoms). The findings also show that when a more comprehensive definition of recurrence that includes all symptomatic and radiological manifestations is used, risk factors better predict kidney stone recurrence, thus supporting a comprehensive assessment of recurrence in clinical trials. Background Meaningful interpretation of changes in radiographic kidney stone burden requires understanding how radiographic recurrence relates to symptomatic recurrence and how established risk factors predict these different manifestations of recurrence. Methods We recruited first-time symptomatic stone formers from the general community in Minnesota and Florida. Baseline and 5-year follow-up study visits included computed tomography scans, surveys, and medical record review. We noted symptomatic recurrence detected by clinical care (through chart review) or self-report, and radiographic recurrence of any new stone, stone growth, or stone passage (comparing baseline and follow-up scans). To assess the prediction of different manifestations of recurrence, we used the Recurrence of Kidney Stone (ROKS) score, which sums multiple baseline risk factors. Results Among 175 stone formers, 19% had symptomatic recurrence detected by clinical care and 25% detected by self-report; radiographic recurrence manifested as a new stone in 35%, stone growth in 24%, and stone passage in 27%. Among those with a baseline asymptomatic stone (54%), at 5 years, 51% had radiographic evidence of stone passage (accompanied by symptoms in only 52%). Imaging evidence of a new stone or stone passage more strongly associated with symptomatic recurrence detected by clinical care than by self-report. The ROKS score weakly predicted one manifestation—symptomatic recurrence resulting in clinical care ( c -statistic, 0.63; 95% confidence interval, 0.52 to 0.73)—but strongly predicted any manifestation of symptomatic or radiographic recurrence (5-year rate, 67%; c -statistic, 0.79; 95% confidence interval, 0.72 to 0.86). Conclusions Recurrence after the first stone episode is both more common and more predictable when all manifestations of recurrence (symptomatic and radiographic) are considered.

PurposeThis work aims to develop a new framework of image quality assessment using deep learning‐based model observer (DL‐MO) and to validate it in a low‐contrast lesion detection task that involves CT images with patient anatomical background.MethodsThe DL‐MO was developed using the transfer learning strategy to incorporate a pretrained deep convolutional neural network (CNN), a partial least square regression discriminant analysis (PLS‐DA) model and an internal noise component. The CNN was previously trained to achieve the state‐of‐the‐art classification accuracy over a natural image database. The earlier layers of the CNN were used as a deep feature extractor, with the assumption that similarity exists between the CNN and the human visual system. The PLSR model was used to further engineer the deep feature for the lesion detection task in CT images. The internal noise component was incorporated to model the inefficiency and variability of human observer (HO) performance, and to generate the ultimate DL‐MO test statistics. Seven abdominal CT exams were retrospectively collected from the same type of CT scanners. To compare DL‐MO with HO, 12 experimental conditions with varying lesion size, lesion contrast, radiation dose, and reconstruction types were generated, each condition with 154 trials. CT images of a real liver metastatic lesion were numerically modified to generate lesion models with four lesion sizes (5, 7, 9, and 11 mm) and three contrast levels (15, 20, and 25 HU). The lesions were inserted into patient liver images using a projection‐based method. A validated noise insertion tool was used to synthesize CT exams with 50% and 25% of routine radiation dose level. CT images were reconstructed using the weighted filtered back projection algorithm and an iterative reconstruction algorithm. Four medical physicists performed a two‐alternative forced choice (2AFC) detection task (with multislice scrolling viewing mode) on patient images across the 12 experimental conditions. DL‐MO was operated on the same datasets. Statistical analyses were performed to evaluate the correlation and agreement between DL‐MO and HO.ResultsA statistically significant positive correlation was observed between DL‐MO and HO for the 2AFC low‐contrast detection task that involves patient liver background. The corresponding Pearson product moment correlation coefficient was 0.986 [95% confidence interval (0.950, 0.996)]. Bland–Altman agreement analysis did not indicate statistically significant differences.ConclusionsThe proposed DL‐MO is highly correlated with HO in a low‐contrast detection task that involves realistic patient liver background. This study demonstrated the potential of the proposed DL‐MO to assess image quality directly based on patient images in realistic, clinically relevant CT tasks.