McCollough, Cynthia H.

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.

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.