Dependence of Water-equivalent Diameter and Size-specific Dose Estimates on CT Tube Potential.

Background The size-specific dose estimate (SSDE) is a patient-focused CT dose metric. However, published size-dependent conversion factors (fsize) used to calculate SSDE were determined primarily by using phantoms; only eight to 15 patient data sets were used, all at 120 kV. Purpose To determine the effect of different tube potentials on the water-equivalent diameter (WED) and SSDE for patient CT scans of the head, chest, and abdomen. Materials and Methods This retrospective study used 250 noncontrast CT scans acquired between March 2013 and June 2017. Bony structures were segmented, and their CT numbers were modified to reflect bone attenuation at 70, 90, 110, 130, and 150 kV. Soft-tissue CT numbers were unchanged because of negligible energy dependence. fsize was measured in anthropomorphic phantoms for each tube potential and fit to an exponential function. WED and SSDE were determined for each patient at all tube potentials, regression analysis was performed relative to the WED and SSDE at 120 kV, and mean differences relative to 120 kV were calculated. Results In 250 patients (median age, 21.5 years; interquartile range, 44 years; 130 women), WED for all tube potentials was linearly related to the WED at 120 kV in all body regions (R2 = 0.995-1.000). The effect of tube potential on WED was negligible for torso examinations (Cohen d < 0.05). In the head, a medium effect size was observed at 70 kV; however, the mean absolute difference in WED was small (-0.49 cm ± 0.08 [standard deviation]; P < .001). For commonly used combinations of tube potential and patient size, the mean differences in SSDE at alternative tube potentials relative to SSDE at 120 kV were less than 5%. Conclusion At noncontrast CT, published size-dependent conversion factors accurately determined size-specific dose estimates on 250 patient scans at five tube potentials other than 120 kV. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Boone in this issue.

Automated radiomic analysis of CT images to predict likelihood of spontaneous passage of symptomatic renal stones.

PURPOSE: To evaluate the ability of a semi-automated radiomic analysis software in predicting the likelihood of spontaneous passage of urinary stones compared with manual measurements. METHODS: Symptomatic patients visiting the emergency department with suspected stones in either kidney or ureters who underwent a CT scan were included. Patients were followed for up to 6 months for the outcome of a trial of passage. Maximum stone diameters in axial and coronal images were measured manually. Stone length, width, height, max diameter, volume, the mean and standard deviation of the Hounsfield units, and morphologic features were also measured using automated radiomic analysis software. Multivariate models were developed using these data to predict subsequent spontaneous stone passage, with results expressed as the area under a receiver operating curve (AUC). RESULTS: One hundred eighty-four patients (69 females) with a median age of 56 years were included. Spontaneous stone passage occurred in 114 patients (62%). Univariate analysis demonstrated an AUC of 0.83 and 0.82 for the maximum stone diameter determined manually in the axial and coronal planes, respectively. Multivariate models demonstrated an AUC of 0.82 for a model including manual measurement of maximum stone diameter in axial and coronal planes. The same AUC was found for a model including automatic measurement of maximum height and diameter of the stone. Further addition of morphological parameters measured automatically did not increase AUC beyond 0.83. CONCLUSION: The semi-automated radiomic analysis of urinary stones shows similar accuracy compared with manual measurements for predicting urinary stone passage. Further studies are needed to predict clinical impacts of reporting the likelihood of urinary stone passage and improving inter-observer variation using automatic radiomic analysis software.

Low-dose CT image and projection dataset.

PURPOSE: To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. ACQUISITION AND VALIDATION METHODS: The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise-insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. DATA FORMAT AND USAGE NOTES: Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). POTENTIAL APPLICATIONS: This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.

Reducing radiation dose for multi-phase contrast-enhanced dual energy renal CT: pilot study evaluating prior iterative reconstruction.

PURPOSE: Prior iterative reconstruction (PIR) uses spatial information from one phase of enhancement to reduce image noise in other phases. We sought to determine if PIR could reduce radiation dose while preserving observer performance and CT number at multi-phase dual energy (DE) renal CT. METHODS: CT projection data from multi-phase DE renal CT examinations were collected. Images corresponding to 40% radiation dose were reconstructed using validated noise insertion and PIR. Three genitourinary radiologists examined routine and 40% dose PIR images. Probability of malignancy was assessed [from 0 to 100] with malignancy assumed at probability ≥ 75. Observer performance was compared on a per patient and per lesion level. CT number accuracy was measured. RESULTS: Twenty-three patients had 49 renal lesions (11 solid renal neoplasms). CT number was nearly identical between techniques (mean CT number difference: unenhanced 2 ± 2 HU; enhanced 4 ± 4 HU). AUC for malignancy was similar between multi-phase routine dose DE and lower dose PIR images [per patient: 0.950 vs. 0.916 (p = 0.356); per lesion: 0.931 vs. 0.884 (p = 0.304)]. Per patient sensitivity was also similar (78% routine dose vs. 82% lower dose [p ≥ 0.99]), as was specificity (91% routine dose vs. 93% lower dose PIR [p > 0.99]), with similar findings on a per lesion level. Subjective image quality was also similar (p = 0.34). CONCLUSIONS: Prior iterative reconstruction is a new reconstruction method for multi-phase CT examinations that promises to facilitate radiation dose reduction by over 50% for multi-phase DE renal CT exams without compromising CT number or observer performance.