The volumetric data obtained from the cardiac CT scan of congenital heart disease patients is important for defining patient\'s status and making decision for proper management. The objective of this study is to evaluate the intra-observer, inter-observer, and interstudy reproducibility of left ventricular (LV) and right ventricular (RV) or functional single-ventricle (FSV) volume. And compared those between manual and using semi-automated segmentation tool. Total of 127 patients (56 female, 71 male; mean age 82.1 months) underwent pediatric protocol cardiac CT from January 2020 to December 2022. The volumetric data including both end-systolic and -diastolic volume and calculated EF were derived from both conventional semiautomatic region growing algorithms (CM, TeraRecon, TeraRecon, Inc., San Mateo, CA, USA) and deep learning-based annotation program (DLS, Medilabel, Ingradient, Inc., Seoul, Republic of Korea) by three readers, who have different background knowledge or experience of radiology or image extraction before. The reproducibility was compared using intra- and inter-observer agreements. And the usability was measured using time for reconstruction and number of tests that were reconfigured before the reconfiguration time was reduced to less than 5 min. Inter- and intra-observer agreements showed better agreements degrees in DLS than CM in all analyzers. The time used for reconstruction showed significantly shorter in DLS compared with CM. And significantly small numbers of tests before the reconfiguration is needed in DLS than CM. Deep learning-based annotation program can be more accurate way for measurement of volumetric data for congenital heart disease patients with better reproducibility than conventional method.

UNASSIGNED: Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions.
UNASSIGNED: We conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta.
UNASSIGNED: There were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants\' body mass index and waist circumference.
UNASSIGNED: The optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.

To evaluate the usefulness of low-keV multiphasic computed tomography (CT) with deep learning image reconstruction (DLIR) in improving the delineation of pancreatic ductal adenocarcinoma (PDAC) compared to conventional hybrid iterative reconstruction (HIR). Thirty-five patients with PDAC who underwent multiphasic CT were retrospectively evaluated. Raw data were reconstructed with two energy levels (40 keV and 70 keV) of virtual monochromatic imaging (VMI) using HIR (ASiR-V50%) and DLIR (TrueFidelity-H). Contrast-to-noise ratio (CNRtumor) was calculated from the CT values within regions of interest in tumor and normal pancreas in the pancreatic parenchymal phase images. Lesion conspicuity of PDAC in pancreatic parenchymal phase on 40-keV HIR, 40-keV DLIR, and 70-keV DLIR images was qualitatively rated on a 5-point scale, using 70-keV HIR images as reference (score 1 = poor; score 3 = equivalent to reference; score 5 = excellent) by two radiologists. CNRtumor of 40-keV DLIR images (median 10.4, interquartile range (IQR) 7.8-14.9) was significantly higher than that of the other VMIs (40 keV HIR, median 6.2, IQR 4.4-8.5, P < 0.0001; 70-keV DLIR, median 6.3, IQR 5.1-9.9, P = 0.0002; 70-keV HIR, median 4.2, IQR 3.1-6.1, P < 0.0001). CNRtumor of 40-keV DLIR images were significantly better than those of the 40-keV HIR and 70-keV HIR images by 72 ± 22% and 211 ± 340%, respectively. Lesion conspicuity scores on 40-keV DLIR images (observer 1, 4.5 ± 0.7; observer 2, 3.4 ± 0.5) were significantly higher than on 40-keV HIR (observer 1, 3.3 ± 0.9, P < 0.0001; observer 2, 3.1 ± 0.4, P = 0.013). DLIR is a promising reconstruction method to improve PDAC delineation in 40-keV VMI at the pancreatic parenchymal phase compared to conventional HIR.

UNASSIGNED: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotchy appearance and decreased resolution for subtle contrasts. The deep-learning image reconstruction (DLIR) algorithm, which integrates a convolutional neural network (CNN) into the reconstruction process, generates high-quality images with minimal noise. Hence, the objective of this study was to assess the IQ of the Precise Image (DLIR) and the IR technique (iDose 4) for the NCCT brain.
UNASSIGNED: This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose 4. Qualitative IQ analysis parameters, such as overall image quality (OQ), subjective image noise (SIN), and artifacts, were measured. Quantitative IQ analysis parameters such as Computed Tomography (CT) attenuation (HU), image noise (IN), posterior fossa index (PFI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in the basal ganglia (BG) and centrum-semiovale (CSO) were measured. Paired t-tests were performed for qualitative and quantitative IQ analyses between the iDose 4 and DLIR-standard. Kappa statistics were used to assess inter-observer agreement for qualitative analysis.
UNASSIGNED: Quantitative IQ analysis showed significant differences (p<0.05) in IN, SNR, and CNR between the iDose 4 and DLIR-standard at the BG and CSO levels. IN was reduced (41.8-47.6%), SNR (65-82%), and CNR (68-78.8%) were increased with DLIR-standard. PFI was reduced (27.08%) the DLIR-standard. Qualitative IQ analysis showed significant differences (p<0.05) in OQ, SIN, and artifacts between the DLIR standard and iDose 4. The DLIR standard showed higher qualitative IQ scores than the iDose 4.
UNASSIGNED: DLIR standard yielded superior quantitative and qualitative IQ compared to the IR technique (iDose4). The DLIR-standard significantly reduced the IN and artifacts compared to iDose 4 in the NCCT brain.

UNASSIGNED: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations.
UNASSIGNED: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.
UNASSIGNED: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations.
UNASSIGNED: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pairing basic information, 210 patients were enrolled in this study: Group A, 0.7mL/kg (n = 70); Group B, 0.6mL/kg (n = 70); Group C, 0.5mL/kg (n = 70). All patients were examined via a prospective ECG-triggered scan protocol within one heartbeat. A high level DLIR (DLIR-H) algorithm was used for image reconstruction with a thickness and interval of 0.625mm. The CT values of ascending aorta (AA), descending aorta (DA), three main coronary arteries, pulmonary artery (PA), and superior vena cava (SVC) were measured and analyzed for objective assessment. Two radiologists assessed the image quality and diagnostic confidence using a 5-point Likert scale. The CM doses were 46.81 ± 6.41mL, 41.96 ± 7.51mL and 34.65 ± 5.38mL for Group A, B and C, respectively. The objective assessments on AA, DA and the three main coronary arteries and the overall subjective scoring showed no significant difference among the three groups (all p > 0.05). The subjective assessment proved that excellent CCTA images can be obtained from the three different contrast media protocols. There were no significant differences in intracoronary attenuation values between the higher HR subgroup and the lower HR subgroup among three groups. CCTA reconstructed with DLIR could be realized with adequate enhancement in coronary arteries, excellent image quality and diagnostic confidence at low contrast dose of a 0.5mL/kg. The use of lower tube voltages may further reduce the contrast dose requirement.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable.
METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models.
RESULTS: Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR.
CONCLUSIONS: We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

OBJECTIVE: To verify the optimal imaging conditions for coronary computed tomography angiography (CCTA) examinations when using high-definition (HD) mode and deep learning image reconstruction (DLIR) in combination.
METHODS: A chest phantom and an in-house phantom using 3D printer were scanned with a 256-row detector CT scanner. The scan parameters were as follows - acquisition mode: ON (HD mode) and OFF (normal resolution [NR] mode), rotation time: 0.28 s/rotation, beam coverage width: 160 mm, and the radiation dose was adjusted based on CT-AEC. Image reconstruction was performed using ASiR-V (Hybrid-IR), TrueFidelity Image (DLIR), and HD-Standard (HD mode) and Standard (NR mode) reconstruction kernels. The task-based transfer function (TTF) and noise power spectrum (NPS) were measured for image evaluation, and the detectability index (d\') was calculated. Visual evaluation was also performed on an in-house coronary phantom.
RESULTS: The in-plane TTF was better for the HD mode than for the NR mode, while the z-axis TTF was lower for DLIR than for Hybrid-IR. The NPS values in the high-frequency region were higher for the HD mode compared to those for the NR mode, and the NPS was lower for DLIR than for Hybrid-IR. The combination of HD mode and DLIR showed the best value for in-plane d\', whereas the combination of NR mode and DLIR showed the best value for z-axis d\'. In the visual evaluation, the combination of NR mode and DLIR showed the best values from a noise index of 45 HU.
CONCLUSIONS: The optimal combination of HD mode and DLIR depends on the image noise level, and the combination of NR mode and DLIR was the best imaging condition under noisy conditions.

OBJECTIVE: To investigate the influence of the deep learning image reconstruction (DLIR) on the image quality and quantitative analysis of pulmonary nodules under ultra-low dose lung CT conditions.
METHODS: This was a prospective study with patient consent and included 56 patients with suspected pulmonary nodules. Patients were examined by both standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with adaptive statistical iterative reconstruction-V 40% (ASIR-V40%) (group A), while ULDCT images were reconstructed using ASIR-V40% (group B) and high-strength DLIR (DLIR-H) (group C). The three image sets were analyzed using a commercial computer aided diagnosis (CAD) software. Parameters such as nodule length, width, density, volume, risk, and classification were measured. The CAD quantitative data of different nodule types (solid, calcified, and subsolid nodules) and nodule image quality scores evaluated by two physicians on a 5-point scale were compared.
RESULTS: The radiation dose in ULDCT was 0.25 ± 0.08mSv, 7.2% that of the 3.48 ± 1.08mSv in SDCT (P < 0.001). 104 pulmonary nodules were detected (51/53 solid, 26/24 calcified and 27/27 subsolid in Groups A and (B&C), respectively). Group B had lower density for solid, calcified nodules, and lower volume and risk for subsolid nodules than Group A, while Group C had lower density for calcified nodules (P < 0.05), There were no significant differences in other parameters among the three groups (P > 0.05). Group A and C had similar image quality for nodules and were higher than Group B (P < 0.05).
CONCLUSIONS: DLIR-H significantly improves image quality than ASIR-V40% and maintains similar nodule detection and characterization with CAD in ULDCT compared to SDCT.

UNASSIGNED: The high-definition standard (HD-standard) scan mode has been proven to display stents better than the standard (STND) scan mode but with more image noise. Deep learning image reconstruction (DLIR) is capable of reducing image noise. This study examined the impact of HD-standard scan mode with DLIR algorithms on stent and coronary artery image quality in coronary computed tomography angiography (CCTA) via a comparison with conventional STND scan mode and adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithms.
UNASSIGNED: The data of 121 patients who underwent HD-standard mode scans (group A: N=47, with coronary stent) or STND mode scans (group B: N=74, without coronary stent) were retrospectively collected. All images were reconstructed with ASIR-V at a level of 50% (ASIR-V50%) and a level of 80% (ASIR-V80%) and with DLIR at medium (DLIR-M) and high (DLIR-H) levels. The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), artifact index (AI), and in-stent diameter were measured as objective evaluation parameters. Subjective assessment involved a 5-point scale for overall image quality, image noise, stent appearance, stent artifacts, vascular sharpness, and diagnostic confidence. Diagnostic confidence was evaluated based on the presence or absence of significant stenosis (≥50% lumen reduction). Both subjective and objective evaluations were conducted by two radiologists independently, with kappa and intraclass correlation statistics being used to test the interobserver agreement.
UNASSIGNED: There were 76 evaluable stents in group A, and the DLIR-H algorithm significantly outperformed other algorithms, demonstrating the lowest noise (41.6±7.1/41.3±7.2) and AI (32.4±8.9/31.2±10.1), the highest SNR (14.6±3.5/15.0±3.5) and CNR (13.6±3.8/13.9±3.8), and the largest in-stent diameter (2.18±0.61/2.19±0.61) in representing true stent diameter (all P values <0.01), as well as the highest score in each subjective evaluation parameter. In group B, a total of 296 coronary arteries were evaluated, and the DLIR-H algorithm provided the best objective image quality, with statistically superior noise, SNR, and CNR compared with the other algorithms (all P values <0.05). Moreover, the HD-standard mode scan with DLIR provided better image quality and a lower radiation dose than did the STND mode scan with ASIR-V (P<0.01).
UNASSIGNED: HD-standard scan mode with DLIR-H improves image quality of both stents and coronary arteries on CCTA under a lower radiation dose.