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The purpose of this study was to investigate the frequency and impact of vertical mis‐centering on organ doses in computed tomography (CT) exams and evaluate the effect of a commercially available positioning compensation system (PCS). Mis‐centering frequency and magnitude was retrospectively measured in 300 patients examined with chest‐abdomen‐pelvis CT. Organ doses were measured in three postmortem subjects scanned on a CT scanner at nine different vertical table positions (maximum shift ± 4 cm). Organ doses were measured with optically stimulated luminescent dosimeters inserted within organs. Regression analysis was performed to determine the correlation between organ doses and mis‐centering. Methods were repeated using a PCS that automatically detects the table offset to adjust tube current output accordingly. Clinical mis‐centering was >1 cm in 53% and 21% of patients in the vertical and lateral directions, respectively. The 1‐cm table shifts resulted in organ dose differences up to 8%, while 4‐cm shifts resulted in organ dose differences up to 35%. Organ doses increased linearly with superior table shifts for the lung, colon, uterus, ovaries, and skin (R2 = 0.73–0.99, P < 0.005). When the PCS was utilized, organ doses decreased with superior table shifts and dose differences were lower (average 5%, maximum 18%) than scans performed without PCS (average 9%, maximum 35%) at all table shifts. Mis‐centering occurs frequently in the clinic and has a significant effect on patient dose. While accurate patient positioning remains important for maintaining optimal imaging conditions, a PCS has been shown to reduce the effects of patient mis‐centering.

This study assesses the accuracy of effective atomic number (Z eff ) and electron density measurements acquired from dual energy CT and characterizes the response to clinically relevant variables representative of challenges in patient imaging, including: phantom size, material position within the phantom, variation over time, off-center positioning, and large cone beam angle. Methods: The Gammex Multi-Energy CT head and body phantoms were used to measure Z eff and electron density from 35 rod inserts that mimic tissues and varying concentrations of iodine and calcium. Scans were performed on a Canon Aquilion ONE Genesis CT scanner over a period of 6 months using default dual energy protocols appropriate for each phantom size. Theoretical Z eff and electron density values were calculated using data provided by the phantom manufacturer and compared to the measurements. Sources of variance were separated and quantified to identify the influences of random photon statistics, ROI placement, and variation over time. A subset of measurements were repeated with the phantom shifted in the vertical and horizontal directions, and over all slices in the volumetric scan. Results: All measurements showed strong correlation (r > 0.98) with their corresponding theoretical values; however, the system did demonstrate a bias of −0.58 atomic units in the body phantom and 0.28 atomic units in the head phantom for Z eff measurements. The mean absolute percent error (MAPE) was 6.3% for the body phantom and 3.2% for the head phantom. Electron density measurements of the body and head phantoms gave MAPE values of 4.6% and 1.0%, respectively. Z eff and electron density measurements significantly varied within the solid water background, showing a positional dependence within the phantom that dominated the total standard deviation in measurements. Z eff values dropped by 0.2 atomic units when the phantom was off-center; electron density measurements were less affected by phantom position. Along the z-axis, the accuracy drops off markedly at more than 50-60 mm from the central slice. Conclusion:The Canon dual energy system offers an accurate way of measuring the Z eff and electron density of clinically relevant materials. Accuracy could be improved further by calibration to remove bias, careful attention to centering within the FOV, and avoiding measurements at the edges of the cone beam.

Purpose:We compare the effect of tube current modulation (TCM) and fixed tube current (FTC) on size-specific dose estimates (SSDE) and image quality in lung cancer screening with low-dose CT (LDCT) for patients of all sizes.Methods: Initially, 107 lung screening examinations were performed using FTC, which satisfied the Centers for Medicare & Medicaid Services' volumetric CT dose index (CTDI vol ) limit of 3.0 mGy for standard-sized patients. Following protocol modification, 287 examinations were performed using TCM. Patient size and examination parameters were collected and waterequivalent diameter (D w ) and SSDE were determined for each patient. Regression models were used to correlate CTDI vol and SSDE with D w . Objective and subjective image quality were measured in 20 patients who had consecutive annual screenings with both FTC and TCM.Results: CTDI vol was 2.3 mGy for all FTC scans and increased exponentially with D w (range = 0.96-4.50 mGy, R 2 = 0.73) for TCM scans. As patient D w increased, SSDE decreased for FTC examinations (R 2 = 1) and increased for TCM examinations (R 2 = 0.54). Image quality measurements were superior with FTC for smaller sized patients and with TCM for larger sized patients (R 2 > 0.5, P < 0.005). Radiologist graded all images acceptable for diagnostic evaluation of lung cancer screening.Conclusion: Although FTC protocol offered a consistently low CTDI vol for all patients, it yielded unnecessarily high SSDE for small patients and increased image noise for large patients. Lung cancer screening with LDCT using TCM produces radiation doses that are appropriately reduced for small patients and increased for large patients with diagnostic image quality for all patients. K E Y W O R D S image quality, lung cancer screening CT, radiation dose, size-specific dose estimates, tube current modulation ---

Modern CT iterative reconstruction algorithms are transitioning from a statistical-based to model-based approach. However, increasing complexity does not ensure improved image quality for all indications, and thorough characterization of new algorithms is important to understand their potential clinical impacts. This study performs both quantitative and qualitative analyses of image quality to compare Canon’s statistical-based Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm to its model-based algorithm, Forward-projected model-based Iterative Reconstruction SoluTion(FIRST). A phantom was used to measure the task-specific modulation transfer function (MTFTask), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNRLO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTFTask was measured at four contrast levels, and CNRLO was measured for two object sizes. Lastly, three radiologists participated in a preference study to compare clinical image quality for three study types: non-contrast abdomen, pulmonary embolism (PE), and lung screening. Nine questions related to the appearance of anatomical features or image quality characteristics were scored for twenty exams of each type. The behavior of both algorithms depended strongly on the kernel selected. Phantom measurements suggest that FIRST should be beneficial over AIDR 3D for abdomen imaging, but do not suggest a clear overall benefit to FIRST for lung or brain imaging; metrics suggest performance may be equivalent to or slightly favor AIDR 3D, depending on the size of the object being imaged and whether spatial resolution or low-contrast resolution is more important for the task at hand. Overall, radiologists strongly preferred AIDR 3D for lung screening, slightly preferred AIDR 3D for non-contrast abdomen, and had no preference for PE. FIRST was superior for the reduction of metal artifacts. Radiologist preference may be influenced by changes to noise texture.

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A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.