Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study.

BACKGROUND: To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform. METHODS: Two phantoms were scanned with a rapid kV-switching CT using abdomen-pelvic CT examination parameters at dose of 12.6 mGy. Images were reconstructed using two versions of DL spectral reconstruction algorithms (DLSR V1 and V2) for three reconstruction levels. The noise power spectrum (NSP) and task-based transfer function at 50% (TTF50) were computed at 40/50/60/70 keV. A detectability index (d') was calculated for enhanced lesions at low iodine concentrations: 2, 1, and 0.5 mg/mL. RESULTS: The noise magnitude was significantly lower with DLSR V2 compared to DLSR V1 for energy levels between 40 and 60 keV by -36.5% ± 1.4% (mean ± standard deviation) for the standard level. The average NPS frequencies increased significantly with DLSR V2 by 23.7% ± 4.2% for the standard level. The highest difference in TTF50 was observed at the mild level with a significant increase of 61.7% ± 11.8% over 40-60 keV energy with DLSR V2. The d' values were significantly higher for DLSR V2 versus DLSR V1. CONCLUSIONS: The DLSR V2 improves image quality and detectability of low iodine concentrations in VMIs compared to DLSR V1. This suggests a great potential of DLSR V2 to reduce iodined contrast doses.

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.

Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

BACKGROUND: Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels. PURPOSE: To assess the impact of an artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications. METHODS: Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU-41 phantoms were performed at five dose levels (CTDIvol : 9.5/7.5/6/2.5/0.4 mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose4 levels 4 (i4) and 7 (i7)), and five levels of AI-DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task-based transfer function, and detectability index (d') were computed: d'-modeled detection of a soft tissue mediastinal nodule (low-contrast soft tissue chest nodule within the mediastinum [LCN]), ground-glass opacity (GGO), or high-contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four- or five-point scale. RESULTS: From Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: -66.3% ± 0.5% for mediastinal images and -63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: -35.3% ± 2.2% for mediastinal images and -13.3% ± 2.2% for parenchymal images), and the detectability (d') of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low-contrast polyethylene insert and the opposite for the high-contrast air insert. Compared to the i4 used in clinical practice, d' values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required. CONCLUSION: With AI-DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

Phantom task-based image quality assessment of three generations of rapid kV-switching dual-energy CT systems on virtual monoenergetic images.

PURPOSE: To compare the spectral performance of three rapid kV switching dual-energy CT (DECT) systems on virtual monoenergetic images (VMIs) at low-energy levels on abdominal imaging. METHODS: A multi-energy phantom was scanned on three DECT systems equipped with three different gemstone spectral imaging (GSI) platforms: GSI (1st generation, GSI-1st), GSI-Pro (2nd generation, GSI-2nd ), and GSI-Xtream (3rd generation, GSI-3rd). Acquisitions on the phantom were performed with a CTDIvol close to 11mGy. For all platforms, raw data were reconstructed using filtered-back projection (FBP) and a hybrid iterative reconstruction algorithm (ASIR-V at 50%; AV50). A deep-learning image reconstruction (DLR) algorithm (TrueFidelity) was used only for the GSI-3rd. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 keV of VMIs. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions according to the keV level used. RESULTS: For all GSI platforms, the noise magnitude decreased from 40 to 70 keV, and using AV50 compared to FBP. The average NPS spatial frequency (fav ) and spatial resolution (TTF50% ) were similar from 40 to 70 keV and decreased with AV50 compared to FBP. Compared to AV50, using DLR reduced the noise magnitude (-27% ± 3%) and improved fav values (10% ± 0%) and altering spatial resolution (2% ± 5%). For the two lesions, d' values peaked at 70 keV for GSI-1st and GSI-2nd platforms and at 40/50 keV for GSI-3rd, for all reconstruction algorithms. The highest d' values were found for the GSI-3rd with DLR. CONCLUSION: Differences in image quality were found between the GSI platforms for VMIs at low keV. The new DLR algorithm on the GSI-3rd platform reduced noise and improved spatial resolution and detectability without changing the noise texture for VMIs at low keV. The choice of the best energy level in VMIs depends on the platform and the reconstruction algorithm.

Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction.

BACKGROUND: To assess the spectral performance of rapid kV switching dual-energy CT (KVSCT-Canon) equipped with a Deep-Learning spectral reconstruction algorithm on virtual-monoenergetic images at low-energy levels and to compare its performances with four other dual-energy CT (DECT) platforms equipped with iterative reconstruction algorithms. METHODS: Two CT phantoms were scanned on five DECT platforms: KVSCT-Canon, fast kV-switching CT (KVSCT-GE), split filter CT, dual-source CT (DSCT), and dual-layer CT (DLCT). The classical parameters of abdomen-pelvic examinations were used for all phantom acquisitions, and a CTDIvol close to 10 mGy. For KVSCT-Canon, virtual-monoenergetic images were reconstructed with a clinical slice thickness of 0.5 and 1.5 mm to be close to other platforms. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 keV of virtual-monoenergetic images. A detectability index (d') was computed to model the detection task of two contrast-enhanced lesions as function of keV. RESULTS: For KVSCT-Canon, the noise magnitude and average NPS spatial frequency (fav) decreased from 40 to 70 keV and increased thereafter. Similar noise magnitude outcomes were found for KVSCT-GE but the opposite for fav. For the other DECT platforms, the noise magnitude decreased as the keV increased. For split filter CT, DSCT and DLCT, the fav values increased from 40 to 80 keV. For all DECT platforms, TTF at 50% (f50) decreased as the keV increased, decreasing spatial resolution. For KVSCT-Canon, d' values peaked at 60 and 70 keV for both simulated lesions and from 50 to 70 keV for KVSCT-GE. d' decreased between 40 and 70 keV for DSCT, DLCT and split filter CT. For KVSCT-Canon, the increase in slice thickness decreases noise magnitude, fav and f50 and increases d' values. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV. CONCLUSIONS: For KVSCT-Canon, the detectability of contrast-enhanced lesions was highest at 60 keV. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV.