Impact of the automatic tube current modulation (ATCM) system on virtual monoenergetic image quality for dual-source CT: A phantom study.

PURPOSE: To assess the impact of the automatic tube current modulation (ATCM) on virtual monoenergetic images (VMIs) quality in dual-source CT(DSCT). MATERIALS AND METHODS: Acquisitions were performed on DSCT using the Mercury phantom. The acquisition parameters for an abdomen-pelvic examination with single-energy CT(SECT) and dual-energy CT(DECT) imaging were used. Acquisitions were performed for each imaging mode using fixed mAs and ATCM. The mAs value was set to obtain a volume CT dose index of 11 mGy in fixed mAs acquisitions. This value was used as the reference mAs in ATCM acquisitions. The noise power spectrum and task-based transfer function at 40,50,60 and 70 keV levels were computed on VMIs and SECT images. The detectability index (d') was calculated for a lesion with an iodine concentration of 10 mg/mL. RESULTS: The noise magnitude on VMIs was higher with the ATCM system than with fixed mAs for all energy levels and section diameters of 21,26 and 31 cm. The noise texture and spatial resolution were similar between the fixed mAs and ATCM acquisitions for both imaging modes. The d' values were lower for all energy levels with ATCM than with fixed mAs acquisitions for 21 and 26 cm diameters by -39.82 ± 9.32%, similar at 31 cm diameter -4.13 ± 0.24% and higher at 36 cm diameter 10.40 ± 6.69%. It was higher on VMIs at all energy levels compared to SECT images. CONCLUSIONS: The ATCM system could be used with DECT imaging to optimize patient exposure without changing the noise texture and spatial resolution of VMIs compared to fixed mAs and SECT.

Brain image quality according to beam collimation width and image reconstruction algorithm: A phantom study.

PURPOSE: To compare quantitatively and qualitatively brain image quality acquired in helical and axial modes on two wide collimation CT systems according to the dose level and algorithm used. METHODS: Acquisitions were performed on an image quality and an anthropomorphic phantoms at three dose levels (CTDIvol: 45/35/25 mGy) on two wide collimation CT systems (GE Healthcare and Canon Medical Systems) in axial and helical modes. Raw data were reconstructed using iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms. The noise power spectrum (NPS) was computed on both phantoms and the task-based transfer function (TTF) on the image quality phantom. The subjective quality of images from an anthropomorphic brain phantom was evaluated by two radiologists including overall image quality. RESULTS: For the GE system, noise magnitude and noise texture (average NPS spatial frequency) were lower with DLR than with IR. For the Canon system, noise magnitude values were lower with DLR than with IR for similar noise texture but the opposite was true for spatial resolution. For both CT systems, noise magnitude was lower with the axial mode than with the helical mode for similar noise texture and spatial resolution. Radiologists rated the overall quality of all brain images as "satisfactory for clinical use", whatever the dose level, algorithm or acquisition mode. CONCLUSIONS: Using 16-cm axial acquisition reduces image noise without changing the spatial resolution and image texture compared to helical acquisitions. Axial acquisition can be used in clinical routine for brain CT examinations with an explored length of less than 16 cm.

First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT.

The study's aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDIvol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (-26 ± 10%; p < 0.01) and from Smooth to Smoother (-37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.

Life expectancy of patients with hepatocellular carcinoma according to the upfront treatment: A nationwide analysis.

PURPOSE: The purpose of this study was to update the life expectancy of patients with hepatocellular carcinoma (HCC) in an exhaustive nationwide population according to the upfront treatment performed. MATERIALS AND METHODS: From the French Program for the Medicalization of Information System database, all patients older than 18 years diagnosed with a de novo HCC from January 2011 to December 2018 were retrospectively selected. Five-year survival rates (95% confidence intervals [CI]) were computed according to the first surgical or interventional radiology procedures performed. RESULTS: A total of 63,996 patients (80% men) with a median age of 68 years (Q1, Q3: 61, 77) were selected, including 24,007 patients who underwent at least one procedure (5-year survival of 45.5%; (95% CI: 44.8-46.2), and 39,989 with none (5-year survival, 9.6%; (95% CI: 9.3-10.0). Only 20.5% (13,101/63,996) of patients could undergo an upfront curative procedure. Liver transplantation achieved the best outcome, whether performed upfront (n = 791; 5-year survival, 79.0% [95% CI: 76.1-82.1]) or during subsequent steps (n = 2217; 5-year survival 80.9% [95% CI: 79.2-82.7]). Tumor ablation (n = 5306), open resection (n = 5171), and minimally-invasive resection (n = 1833) achieved 5-year survival rates of 53.8% (95% CI: 52.3-55.4), 54.1% (95% CI: 52.6-55.6), and 66.2% (95% CI: 63.7-68.7), respectively, with more patients with cirrhosis and subsequent procedures in the tumor ablation group. Patients with upfront transarterial (chemo)embolization (n = 10,247) and selective internal radiation therapy (n = 659) had 5-year survival rates of 31.3% (95% CI: 30.3-32.4) and 18.5% (95% CI: 15.2-22.5). CONCLUSION: While HCC remains mostly diagnosed at an advanced stage associated with a poor prognosis, all the curative options provide 5-year survival rates above 50%.

Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study.

OBJECTIVES: To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications. METHODS: Acquisitions on phantoms were performed at 5 dose levels (CTDIvol: 13/11/9/6/1.8 mGy). Raw data were reconstructed using level 4 of iDose4 (i4) and 3 levels of AI-DLR (Smoother/Smooth/Standard). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed: d' modelled detection of a liver metastasis (LM) and hepatocellular carcinoma at portal (HCCp) and arterial (HCCa) phases. Image quality was subjectively assessed on an anthropomorphic phantom by 2 radiologists. RESULTS: From Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d') of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d' values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level. CONCLUSION: Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality. KEY POINTS: • Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality.

Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study.

PURPOSE: The purpose of this study was to assess the impact of the new artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and radiation dose compared with iterative reconstruction algorithm in lumbar spine computed tomography (CT) examination. MATERIALS AND METHODS: Acquisitions on phantoms were performed using a tube current modulation system for four DoseRight Indexes (DRI) (i.e., 26/23/20/15). Raw data were reconstructed using the Level 4 of iDose4 (i4) and three levels of AI-DLR (Smoother/Smooth/Standard) with a bone reconstruction kernel. The Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed (d' modeled detection of a lytic and a sclerotic bone lesions). Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists. RESULTS: The Noise magnitude was lower with AI-DLR than i4 and decreased from Standard to Smooth (-31 ± 0.1 [SD]%) and Smooth to Smoother (-48 ± 0.1 [SD]%). The average NPS spatial frequency was similar with i4 (0.43 ± 0.01 [SD] mm-1) and Standard (0.42 ± 0.01 [SD] mm-1) but decreased from Standard to Smoother (0.36 ± 0.01 [SD] mm-1). TTF values at 50% decreased as the dose decreased but were similar with i4 and all AI-DLR levels. For both simulated lesions, d' values increased from Standard to Smoother levels. Higher detectabilities were found with a DRI at 15 and Smooth and Smoother levels than with a DRI at 26 and i4. The images obtained with these dose and AI-DLR levels were rated satisfactory for clinical use by the radiologists. CONCLUSION: Using Smooth and Smoother levels with CT allows a significant dose reduction (up to 72%) with a high detectability of lytic and sclerotic bone lesions and a clinical overall image quality.

Retrospective analysis of dose delivered to the uterus during CT examination in pregnant women.

PURPOSE: The purpose of this study was to analyze the dose to the uterus (UD) calculated for pregnant women per computed tomography (CT) acquisition and per CT examination in our Institution. MATERIALS AND METHODS: Consecutive pregnant women who underwent CT examination from June 2014 to February 2022 and for whom UD calculation was performed by a medical physicist were retrospectively included. UDs were computed per CT acquisition using the CT Expo 2.4 software and were summed up to obtain the total UD per CT examination. The CTDIvol and dose-length product (DLP) values were retrieved from the dose report and compared with those calculated by the software. RESULTS: A total of 256 pregnant women with a mean age of 29.4 ± 5.5 (SD) years (range: 18-48) at 24.5 ± 10.4 (SD) weeks of amenorrhea (range: 1-40) were included. UDs were computed for 339 CT acquisitions. The CTDIvol and DLP computed by the software were significantly greater than those retrieved from the dose reports (P < 0.05). The greatest UDs were reported for the abdomen-pelvis (10.93 ± 5.74 [SD] mGy; range: 1.2-24.1), chest-abdomen-pelvis (9.79 ± 7.09 [SD] mGy; range: 3.9-22.1), pelvis (18.50 ± 17.96 [SD] mGy; range: 5.8-31.2) and lumbar spine (10.24 ± 11.38 [SD] mGy; range: 2.3-29.6) CT examinations. The total UDs per CT examination were > 20 mGy for 10 pregnant women and the maximum total UD was 52.3 mGy. CONCLUSION: Greatest UDs during CT examinations are observed when the pelvis is directly exposed to X-rays. With current dose levels and in optimized practices, UDs per CT acquisition and CT examination are always below 100 mGy. UD calculations cannot be performed for CT examinations that do not directly expose the pelvis (i.e., those < 1 mGy).