Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study

Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis

PURPOSE

The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data.

METHODS

We retrospectively analyzed scan data of adult patients who underwent body CT during two periods relative to DLR implementation at our facility: a 12-month pre-DLR phase (n = 5553) using hybrid iterative reconstruction and a 12-month post-DLR phase (n = 5494) with routine CT reconstruction transitioning to DLR. To ensure comparability between two groups, we employed propensity score matching 1:1 based on age, sex, and body mass index. Dose data were collected to estimate organ-specific equivalent doses and total effective doses. We assessed the average dose reduction post-DLR implementation and estimated the Lifetime Attributable Risk (LAR) for cancer per CT exam pre- and post-DLR implementation. The number of radiation-induced cancers before and after the implementation of DLR was also estimated.

RESULTS

After propensity score matching, 5247 cases from each group were included in the final analysis. Post-DLR, the total effective body CT dose significantly decreased to 15.5 ± 10.3 mSv from 28.1 ± 14.0 mSv pre-DLR (p < 0.001), a 45% reduction. This dose reduction significantly lowered the radiation-induced cancer risk, especially among younger women, with the estimated annual cancer incidence from 0.247% pre-DLR to 0.130% post-DLR.

CONCLUSION

The implementation of DLR has the possibility to reduce radiation dose by 45% and the risk of radiation-induced cancer from 0.247 to 0.130% as compared with the iterative reconstruction.

KEY POINTS

Question Can implementing deep learning reconstruction (DLR) in routine CT scans significantly reduce radiation dose and the risk of radiation-induced cancer compared to hybrid iterative reconstruction? Findings DLR reduced the total effective body CT dose by 45% (from 28.1 ± 14.0 mSv to 15.5 ± 10.3 mSv) and decreased estimated cancer incidence from 0.247 to 0.130%. Clinical relevance Adopting DLR in clinical practice substantially lowers radiation exposure and cancer risk from CT exams, enhancing patient safety, especially for younger women, and underscores the importance of advanced imaging techniques.

Deep Learning Image Reconstruction (DLIR) Algorithm to Maintain High Image Quality and Diagnostic Accuracy in Quadruple-low CT Angiography of Children with Pulmonary Sequestration: A Case Control Study

RATIONALE AND OBJECTIVES

CT angiography (CTA) is a commonly used clinical examination to detect abnormal arteries and diagnose pulmonary sequestration (PS). Reducing the radiation dose, contrast medium dosage, and injection pressure in CTA, especially in children, has always been an important research topic, but few research is proven by pathology. The current study aimed to evaluate the diagnostic accuracy for children with PS in a quadruple-low CTA (4L-CTA: low tube voltage, radiation, contrast medium, and injection flow rate) using deep learning image reconstruction (DLIR) in comparison with routine protocol CTA with adaptive statistical iterative reconstruction-V (ASIR-V) MATERIALS AND METHODS: 53 patients (1.50±1.36years) suspected with PS were enrolled to undergo chest 4L-CTA using 70kVp tube voltage with radiation dose or 0.90 mGy in volumetric CT dose index (CTDIvol) and contrast medium dose of 0.8 ml/kg injected in 16 s. Images were reconstructed using DLIR. Another 53 patients (1.25±1.02years) with a routine dose protocol was used for comparison, and images were reconstructed with ASIR-V. The contrast-to-noise ratio (CNR) and edge-rise distance (ERD) of the aorta were calculated. The subjective overall image quality and artery visualization were evaluated using a 5-point scale (5, excellent; 3, acceptable). All patients underwent surgery after CT, the sensitivity and specificity for diagnosing PS were calculated.

RESULTS

4L-CTA reduced radiation dose by 51%, contrast dose by 47%, injection flow rate by 44% and injection pressure by 44% compared to the routine CTA (all p<0.05). Both groups had satisfactory subjective image quality and achieved 100% in both sensitivity and specificity for diagnosing PS. 4L-CTA had a reduced CNR (by 27%, p<0.05) but similar ERD, which reflects the image spatial resolution (p>0.05) compared to the routine CTA. 4L-CTA revealed small arteries with a diameter of 0.8 mm.

CONCLUSION

DLIR ensures the realization of 4L-CTA in children with PS for significant radiation and contrast dose reduction, while maintaining image quality, visualization of small arteries, and high diagnostic accuracy.

Radiation dose in 1-60-month children undergoing 64-detector cardiac CT angiography: ECG-gated versus non-gated scans

INTRODUCTION

Investigating the extent to which the exposure dose differs in cardiac computed tomography angiography (CTA) between electrocardiogram (ECG)-gated and non-gated helical scan is crucial. We aimed to investigate the difference in radiation dose with cardiac CTA between ECG-gated and non-gated helical scans in pediatric patients.

METHODS

Between January 2015 and November 2021, for the study cohort for the retrospective cardiac CTA study, we enrolled 63 pediatric patients aged 1-60 months and divided them into non-gated helical (33 patients) and retrospective ECG-gated (30 patients) scans with a 64-detector-row CT. We compared the CT dose-volume index (CTDIvol), dose-length product (DLP), effective dose (ED), CT values of the aorta and pulmonary artery, and image noise between the two groups. We calculated 95 % confidence intervals (CIs).

RESULTS

Compared with the non-gated helical and ECG gated scans, CTDIvol were 0.5 ± 0.1 and 5.7 ± 1.4 mGy (P < 0.01: 95 % CI, from -5.7 to -4.7), DLPs were 8.6 ± 2.8 and 72.6 ± 24.6 mGy・cm (P < 0.01: 95 % CI, from -72.6 to -55.3), and EDs were 0.2 ± 0.1 and 1.9 ± 0.6 mSv (P < 0.01: 95 % CI, from -1.9 to -1.4), respectively. Radiation dose for retrospective ECG-gated scans was approximately 10 times higher than that for non-gated helical scans. There were no significant differences in the CT values and image noise between the retrospective ECG-gated and non-gated helical scans (all p < 0.05).

CONCLUSION

The radiation dose for retrospective ECG-gated scans with cardiac CTA was approximately 10 times higher than that for non-gated helical scans without any reduction in image noise or contrast enhancement.

IMPLICATIONS FOR PRACTICE

The radiation dose should be as low as reasonably achievable, and the selection of the scan method is pivotal in clinical practice.

Potential of artificial intelligence for radiation dose reduction in computed tomography -A scoping review

INTRODUCTION

Artificial intelligence (AI) is now transforming medical imaging, with extensive ramifications for nearly every aspect of diagnostic imaging, including computed tomography (CT). This current work aims to review, evaluate, and summarise the role of AI in radiation dose optimisation across three fundamental domains in CT: patient positioning, scan range determination, and image reconstruction.

METHODS

A comprehensive scoping review of the literature was performed. Electronic databases including Scopus, Ovid, EBSCOhost and PubMed were searched between January 2018 and December 2024. Relevant articles were identified from their titles had their abstracts evaluated, and those deemed relevant had their full text reviewed. Extracted data from selected studies included the application of AI, radiation dose, anatomical part, and any relevant evaluation metrics based on the CT parameter in which AI is applied.

RESULTS

90 articles met the selection criteria. Included studies evaluated the performance of AI for dose optimisation through patient positioning, scan range determination, and reconstruction across various CT scans, including the abdomen, chest, head, neck, and pelvis, as well as CT angiography. A concise overview of the present state of AI in these three domains, emphasising benefits, limitations, and impact on the transformation of dose reduction in CT scanning, is provided.

CONCLUSIONS

AI methods can help minimise positioning offsets and over-scanning caused by manual errors and helped to overcome the limitation associated with low-dose CT settings through deep learning image reconstruction algorithms. Further clinical integration of AI will continue to allow for improvements in optimising CT scan protocols and radiation dose.

IMPLICATIONS FOR PRACTICE

This review underscores the significance of AI in optimizing radiation doses in CT imaging, focusing on three key areas: patient positioning, scan range determination, and image reconstruction.

Photon-counting CT yields superior abdominopelvic image quality at lower radiation and iodinated contrast doses

BACKGROUND

Photon-counting detector (PCD) computed tomography (CT) has been shown to provide better image quality at lower radiation and intravenous contrast doses than energy-integrating detector (EID) CT in adult patients. There is limited data on these benefits for the pediatric population especially for abdominopelvic CT examinations.

OBJECTIVE

This study examines a reduced weight-based iodinated contrast dosing strategy in pediatric abdominopelvic CT on a PCD-CT system compared to standard dosing protocols on EID-CT using 1 mL/kg and 2 mL/kg, respectively. Image quality is assessed using both quantitative and qualitative measures. We also compare the radiation dose profile between the two PCD-CT and EID-CT cohorts.

MATERIALS AND METHODS

This HIPAA-compliant, IRB-approved, retrospective study included pediatric patients (≤18 years of age) who underwent contrast-enhanced CT examinations of the abdomen and pelvis for routine clinical care (01/2022 - 01/2023) in the portal-venous phase on a PCD-CT (NAEOTOM Alpha; Siemens Healthineers). Inclusion criteria included a similar prior examination within 12-months on a dual-source EID-CT scanner from the same vendor. All PCD-CT and EID-CT scans were acquired using weight-based dosing for intravenous contrast media, 1 mL/kg and 2 mL/kg, respectively, based on institutional protocols. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were measured in the aorta, portal vein, liver parenchyma, and skeletal muscle. Three pediatric radiologists qualitatively evaluated each scan for overall image quality, noise, and contrast on a scale of 0-100. Confidence in small structure detection (common bile duct) was also rated on a scale of 0-3. Radiation doses (size-specific dose estimate (SSDE)) were calculated. Statistical analysis included paired t-tests and a mixed linear effects model to account for patient age, sex, and X-ray tube voltage. A P<0.05 indicated statistical significance.

RESULTS

A total of 49 patients were included (24 female; mean [SD] age 9.9 [6.3] years, range 0.6-18 years). Compared to EID-CT, PCD-CT had a higher mean SNR in the portal vein (23.4 [SD=9.3] vs 17.2 [SD=7.4], P<0.001), aorta (23.4 [SD=11.6] vs 17.7 [10.1], P=0.017), hepatic parenchyma (15.2 [SD=5.6] vs 13.2 [5.1], P=0.016), and skeletal muscle (5.7 [SD=3.1] vs 4.5 [SD=3.1], P=0.01). Compared to EID-CT, PCD-CT also had a higher mean CNR in the portal vein (27.5 [SD=9.6] vs 22.1 [SD=21.1], P=0.003), aorta (27.3 [SD=9.6] vs 22.3 [SD=11.8], P=0.004), hepatic parenchyma (20 [SD=6.9] vs 16.9 [SD=8.5], P=0.013), and skeletal muscle (14.6 [4.9] vs 12.1 [5.6], P=0.008). Overall image quality, image noise, and small structure detection confidence scores were higher on PCD-CT than EID-CT (P=0.037, P<0.001, and P=0.006, respectively). Mean SSDE for PCD-CT was lower than EID-CT (9.1 mGy [SD=4.3] vs 11 mGy [5.9], P=0.012).

CONCLUSION

Compared with EID-CT, contrast-enhanced pediatric abdominopelvic CT offers improved subjective and objective image quality, even at lower radiation doses and reduced intravenous contrast medium volumes.

Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment

OBJECTIVES

To evaluate the efficiency of super-resolution deep-learning reconstruction (SR-DLR) optimized for helical body imaging in assessing pancreatic ductal adenocarcinoma (PDAC) using normal-resolution (NR) CT scanner.

METHODS

Fifty patients with PDAC who underwent multiphase pancreas CT on a 320-row NR scanner were retrospectively analyzed. Images were reconstructed using hybrid iterative reconstruction (HIR), normal-resolution deep-learning reconstruction (NR-DLR), and SR-DLR at a 0.5-mm slice thickness. The matrix size was 512 × 512 for HIR and NR-DLR, and 1024 × 1024 for SR-DLR. Image noise and contrast-to-noise ratio (CNR) of pancreas, superior mesenteric artery, portal vein, and PDAC were quantified. Noise power spectrum (NPS) in the liver and edge rise slope (ERS) at the pancreas, artery, and vein were used to quantify noise properties and edge sharpness. Subjective evaluations included rankings of image sharpness, noise magnitude, texture fineness, and delineation of PDAC, pancreas margin, pancreatic duct, peripancreatic vessels, and hepatic lesions (1 = worst; 3 = best among three image series). Overall diagnostic quality was rated on a 5-point scale (1 = undiagnostic, 5 = excellent).

RESULTS

SR-DLR showed significantly lower image noise and higher CNR than HIR and NR-DLR (all, p < 0.001). NPS analysis revealed no significant difference in average spatial frequency between SR-DLR and NR-DLR (p = 0.770), both being higher than HIR (both, p < 0.001). ERS values of all structures were highest with SR-DLR (p < 0.001). SR-DLR received the highest subjective scores for all criteria, with significant differences from HIR and NR-DLR (all, p < 0.001).

CONCLUSION

SR-DLR improved both subjective and objective image quality, enhancing the delineation of all structures relevant to PDAC assessment using NR CT scanner.

Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment

OBJECTIVE

To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) through the application of low tube voltage (60kVp) and a novel deep learning image reconstruction algorithm (ClearInfinity, DLIR-CI).

METHODS

Iodine attenuation curves obtained from a phantom study informed the administration of CM protocols. Non-obese participants undergoing aortic CTA were prospectively allocated into two groups and then obtained three reconstruction groups. The conventional group (100kVp-CV group) underwent imaging at 100kVp and received 210 mg iodine/kg in combination with a hybrid iterative reconstruction algorithm (ClearView, HIR-CV). The experimental group was imaged at 60kVp with 105 mg iodine/kg, while images were reconstructed with HIR-CV (60kVp-CV group) and with DLIR-CI (60kVp-CI group). Student's t-test was used to compare differences in CM protocol and radiation dose. One-way ANOVA compared CT attenuation, image noise, SNR, and CNR among the three reconstruction groups, while the Kruskal-Wallis H test assessed subjective image quality scores. Post hoc analysis was performed with Bonferroni correction for multiple comparisons, and consistency analysis conducted in subjective image quality assessment was measured using Cohen's kappa.

RESULTS

The radiation dose (1.12 ± 0.23mSv vs. 2.03 ± 0.82mSv) and CM dosage (19.04 ± 3.03mL vs. 38.11 ± 6.47mL) provided the reduction of 45% and 50% in the experimental group compared to the conventional group. The CT attenuation, SNR, and CNR of 60kVp-CI were superior to or equal to those of 100kVp-CV. Compared to the 60kVp-CV group, images in 60kVp-CI showed higher SNR and CNR (all P < 0.001). There was no difference between the 60kVp-CI and 100kVp-CV group in terms of the subjective image quality of the aorta in various locations (all P > 0.05), with 60kVp-CI images were deemed diagnostically sufficient across all vascular segments.

CONCLUSION

For non-obese patients, the combined use of 60kVp and DLIR-CI algorithm can be preserving image quality while enabling radiation dose and contrast medium savings for aortic CTA compared to 100kVp using HIR-CV.

Ultra-high-resolution CT of the temporal bone: Technical aspects, current applications and future directions

Temporal bone imaging has historically suffered from spatial resolution issues because the spatial resolution of conventional high-resolution computed tomography (CT) is 0.5 mm, while the smallest structure of the middle ear, the stapes, has very thin components, as thin as 0.19 mm, and small structures, such as small channels containing nerves and arteries, have historically been beyond its spatial resolution. Photon-counting and ultra-high resolution CT allow for improved spatial resolution and reduced radiation dose compared to conventional high-resolution CT. This article provides a technical approach to understanding the technical aspects of these new techniques and an updated description of the middle and inner ear, as well as a practical approach to understanding the normal and pathologic anatomy of the temporal bone in the light of ultra-high resolution imaging techniques.

Cardiac functional assessment using prospectively electrocardiography-triggered computed tomography in children with congenital heart disease: Comparison of radiation dose and image quality between heart rate-dependent single-extended and heart rate-independent dual-focused scans

PURPOSE

To evaluate radiation dose (RD) reduction potential of heart rate-independent dual-focused scan of prospectively electrocardiography (ECG)-triggered computed tomography (CT) for cardiac functional assessment in children with congenital heart disease (CHD), RD and image quality of the scan mode were compared to those of heart rate-dependent single-extended scan.

METHODS

This study encompassed 1,252 prospectively ECG-triggered pediatric cardiothoracic CT examinations, including single-focused (a reference in matched comparisons), single-extended (younger patients), and dual-focused (older patients) scans. Propensity score matching was used to reduce the confounding effect of age and sex in two matched groups (MPs) (younger MP: single-focused vs. single-extended; older MP: single-focused vs. dual-focused). CT RD, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the MPs were compared.

RESULTS

The effective dose of single-extended (1.4 ± 0.5 mSv) and dual-focused (1.1 ± 0.4 mSv) scans were approximately 2.0-3.2 times higher than (depending on heart rate) and approximately 1.8 times (irrespective of heart rate) that of the age- and sex-matched single-focused scans (0.6 ± 0.2 mSv), respectively. Image noise and SNR of single-extended and dual-focused scans were similar to those of the age- and sex-matched single-focused scans (p values > 0.05). The CNR was also comparable between single-focused and single-extended scans (younger MP) (p > 0.05), but a slightly lower CNR of the dual-focused scans compared to single-focused scans was observed in the older MP (p < 0.02).

CONCLUSION

For cardiac functional assessment in children with CHD, heart rate-independent dual-focused prospectively ECG-triggered scan can reduce CT RD, especially at lower heart rates, with comparable image quality, compared to heart rate-dependent single-extended scan.

Current Radiology workforce perspective on the integration of artificial intelligence in clinical practice: A systematic review

INTRODUCTION

Artificial Intelligence (AI) represents the application of computer systems to tasks traditionally performed by humans. The medical imaging profession has experienced a transformative shift through the integration of AI. While there have been several independent primary studies describing various aspects of AI, the current review employs a systematic approach towards describing the perspectives of radiologists and radiographers about the integration of AI in clinical practice. This review provides a holistic view from a professional standpoint towards understanding how the broad spectrum of AI tools are perceived as a unit in medical imaging practice.

METHODS

The study utilised a systematic review approach to collect data from quantitative, qualitative, and mixed-methods studies. Inclusion criteria encompassed articles concentrating on the viewpoints of either radiographers or radiologists regarding the incorporation of AI in medical imaging practice. A stepwise approach was employed in the systematic search across various databases. The included studies underwent quality assessment using the Quality Assessment Tool for Studies with Diverse Designs (QATSSD) checklist. A parallel-result convergent synthesis approach was employed to independently synthesise qualitative and quantitative evidence and to integrate the findings during the discussion phase.

RESULTS

Forty-one articles were included, all of which employed a cross-sectional study design. The main findings were themed around considerations and perspectives relating to AI education, impact on image quality and radiation dose, ethical and medico-legal implications for the use of AI, patient considerations and their perceived significance of AI for their care, and factors that influence development, implementation and job security. Despite varying emphasis, these themes collectively provide a global perspective on AI in medical imaging practice.

CONCLUSION

While expertise levels are varied and different, both radiographers and radiologists were generally optimistic about incorporation of AI in medical imaging practice. However, low levels of AI education and knowledge remain a critical barrier. Furthermore, equipment errors, cost, data security and operational difficulties, ethical constraints, job displacement concerns and insufficient implementation efforts are integration challenges that should merit the attention of stakeholders.

Evaluation of Low-dose Computed Tomography Images Reconstructed Using Artificial Intelligence-based Adaptive Filtering for Denoising: A Comparison with Computed Tomography Reconstructed with Iterative Reconstruction Algorithm

Purpose

Awareness of radiation-induced risk led to the development of various dose optimization techniques in iterative reconstruction (IR) algorithms and deep learning algorithms to improve low-dose image quality. PixelShine (PS) by AlgoMedica Inc., USA, is a vendor-neutral deep learning denoising tool for low-dose studies, and this study analyzed its images.

Aim

The aim of this study was to assess the diagnostic value of PS-reconstructed images obtained at various low doses (LDs).

Materials and Methods

A retrospective study qualitatively and quantitatively evaluated the low-dose PS-reconstructed images by comparing them with other reconstruction methods and standard dose (SD) images. A total of 85 cases were evaluated, of which 32 cases were scanned on a scanner with filtered back projection (FBP) reconstruction with LD scans performed at 70%-50% of SD. The remaining 53 cases were performed on the scanner with IR, 35 of them had LD scan at 50% of SD and 18 cases had LD scan at 33% of SD.

Results

Qualitative image analysis - The quality of low-dose images with PS and IR was almost equivalent in terms of noise magnitude and texture at 50% dose, and PS images were slightly better at 33% dose reduction. Quantitative image analysis - Low-dose PS-reconstructed images and low-dose iterative reconstructed images had similar contrast-to-noise ratio at 50% dose reduction; however, at 33% of the SD, PS-reconstructed images outperformed. The SD FBP images were equivalent to LD PS-reconstructed images (50% dose reduction).

Conclusions

Artificial intelligence-based denoising algorithms produce similar images as IR at 50% dose reduction and outperform it at 33% of the SD.

Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study

OBJECTIVE

The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CAD v ) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR), mode-based iterative reconstruction (MBIR), and deep learning reconstruction (DLR) at a phantom study.

METHODS

A commercially available anthropomorphic abdominal phantom was scanned five times with a 320-detector row CT at 600 mA, 400 mA, 200 mA, and 100 mA and reconstructed by four methods. Signal-to-noise ratios (SNRs) of all lesions within the arterial and portal-venous phase inserts were calculated, and SNR of the lesion phantom was compared with that of all reconstruction methods by means of Tukey's honestly significant difference (HSD) test. Then, tumor volume ( V ) of each nodule was automatically measured using commercially available CAD v software. To compare dose reduction capability for each reconstruction method at both phases, mean differences between measured V and standard references were compared by Tukey's honestly significant difference test among the four different reconstruction methods on CT obtained at each of the four tube currents.

RESULTS

With each of the tube currents, SNRs for MBIR and DLR were significantly higher than those for FBP and hybrid-type IR ( p < 0.05). At the arterial phase, the mean difference in V for the CT protocol obtained at 600 or 100 mA and reconstructed with DLR was significantly smaller than that for others ( p < 0.05). At the portal-venous phase, the mean differences in V for the CT protocol obtained at 100 mA and reconstructed with hybrid-type IR, MBIR, and DLR were significantly smaller than that for FBP ( p < 0.05).

CONCLUSIONS

Findings of our phantom study show that reconstruction method had influence on CAD v merits for abdominal CT with not only standard but also reduced dose examinations and that DLR can potentially yield better image quality and CAD v measurements than FBP, hybrid-type IR, or MBIR in this setting.

Radiation dose reduction in pediatric computed tomography (CT) using deep convolutional neural network denoising

AIM

We evaluated the quality of noncontrast chest computed tomography (CT) for pediatric patients at two dose levels with and without denoising using a deep convolutional neural network (CNN).

MATERIALS AND METHODS

Forty children underwent noncontrast chest CTs for "chronic cough" using a routine dose (RD) protocol. Images were reconstructed using iterative reconstruction (IR). A validated noise insertion method was used to simulate 20% dose (TD) data for each case. A deep CNN model was trained and validated on 10 cases and then applied to the remaining 30 cases. Three certificate of qualification (CAQ)-certified pediatric radiologists evaluated 30 cases under 4 conditions: (1) RD + IR; (2) RD + CNN; (3) TD + IR; and (4) TD + CNN. Likert scales were used to score subjective image quality (1-5, 5 = excellent) and subjective noise artifact (1-4, 4 = no noise). Images were reviewed for specific findings.

RESULTS

For the 30 patients evaluated (14 female, mean age: 10.8 years, range: 0.17-17), the mean effective dose was 0.46 ± 0.21 mSv for the original RD exam, with an effective dose of 0.09 mSv for the TD exam. Both RD + CNN (3.6 ± 1.1, p < 0.001) and TD + CNN (3.4 ± 0.9, p = 0.023) had higher image quality than RD + IR (3.1 ± 0.9). Both RD + CNN (3.2 ± 0.9, p-value = <0.001) and TD + CNN (2.9 ± 0.6, p-value = 0.001) showed significantly lower subjective noise artifact scores than RD + IR (2.7 ± 0.7). There was excellent intrareader (RD + IR-RD + CNN: mean κ = 0.96, RD + IR-TD + CNN = 0.96, RD + IR-TD + IR = 0.98) and moderate inter-reader reliability (RD + IR: mean κ = 0.55, RD + CNN = 0.50, TD + CNN = 0.54, TD + IR = 0.57) on all 4 image reconstructions.

CONCLUSION

CNN denoising outperforms IR as a means of radiation dose reduction in pediatric CT.

Virtual Monochromatic Imaging of Half-Iodine-Load, Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction in Patients with Renal Insufficiency: A Clinical Pilot Study

BACKGROUND

We retrospectively examined image quality (IQ) of thin-slice virtual monochromatic imaging (VMI) of half-iodine-load, abdominopelvic, contrast-enhanced CT (CECT) by dual-energy CT (DECT) with deep learning image reconstruction (DLIR).

METHODS

In 28 oncology patients with moderate-to-severe renal impairment undergoing half-iodine-load (300 mgI/kg) CECT by DECT during the nephrographic phase, we reconstructed VMI at 40-70 keV with a slice thickness of 0.625 mm using filtered back-projection (FBP), hybrid iterative reconstruction (HIR), and DLIR; measured contrast-noise ratio (CNR) of the liver, spleen, aorta, portal vein, and prostate/uterus; and determined the optimal keV to achieve the maximal CNR. At the optimal keV, two independent radiologists compared each organ's CNR and subjective IQ scores among FBP, HIR, and DLIR to subjectively grade image noise, contrast, sharpness, delineation of small structures, and overall IQ.

RESULTS

CNR of each organ increased continuously from 70 to 40 keV using FBP, HIR, and DLIR. At 40 keV, CNR of the prostate/uterus was significantly higher with DLIR than with FBP; however, CNR was similar between FBP and HIR and between HIR and DLIR. The CNR of all other organs increased significantly from FBP to HIR to DLIR (P < 0.05). All IQ scores significantly improved from FBP to HIR to DLIR (P < 0.05) and were acceptable in all patients with DLIR only.

CONCLUSIONS

The combination of 40 keV and DLIR offers the maximal CNR and a subjectively acceptable IQ for thin-slice VMI of half-iodine-load CECT.

CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View Positron Emission Tomography Using Synthetic CT from Emission Data

Background/Objectives: Paediatric PET/CT imaging is crucial in oncology but poses significant radiation risks due to children's higher radiosensitivity and longer post-exposure life expectancy. This study aims to minimize radiation exposure by generating synthetic CT (sCT) images from emission PET data, eliminating the need for attenuation correction (AC) CT scans in paediatric patients. Methods: We utilized a cohort of 128 paediatric patients, resulting in 195 paired PET and CT images. Data were acquired using Siemens Biograph Vision 600 and Long Axial Field-of-View (LAFOV) Siemens Vision Quadra PET/CT scanners. A 3D parameter transferred conditional GAN (PT-cGAN) architecture, pre-trained on adult data, was adapted and trained on the paediatric cohort. The model's performance was evaluated qualitatively by a nuclear medicine specialist and quantitatively by comparing sCT-derived PET (sPET) with standard PET images. Results: The model demonstrated high qualitative and quantitative performance. Visual inspection showed no significant (19/23) or minor clinically insignificant (4/23) differences in image quality between PET and sPET. Quantitative analysis revealed a mean SUV relative difference of -2.6 ± 5.8% across organs, with a high agreement in lesion overlap (Dice coefficient of 0.92 ± 0.08). The model also performed robustly in low-count settings, maintaining performance with reduced acquisition times. Conclusions: The proposed method effectively reduces radiation exposure in paediatric PET/CT imaging by eliminating the need for AC CT scans. It maintains high diagnostic accuracy and minimises motion-induced artifacts, making it a valuable alternative for clinical application. Further testing in clinical settings is warranted to confirm these findings and enhance patient safety.

Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial

OBJECTIVES

To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT.

METHODS

This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n = 74) or 80-kVp protocol (360 mgL/kg, n = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale.

RESULTS

SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR (p ≥ 0.38).

CONCLUSION

Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT.

CLINICAL RELEVANCE STATEMENT

Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT.

KEY POINTS

Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental. The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses. This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.

Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease

PURPOSE

Lifelong re-examination of CT enterography (CTE) in patients with inflammatory bowel disease (IBD) may be necessary, and reducing radiation exposure during CT examinations is crucial. We investigated the potential application of deep learning reconstruction (DLR) in CTE to reduce radiation dose and improve image quality in IBD.

METHODS

Thirty-six patients with known or suspected IBD were prospectively recruited to the low-dose CTE (LDCTE) group, while forty patients were retrospectively selected from previous clinical standard-dose CTE (STDCTE) scans as controls. STDCTE images were reconstructed with hybrid-IR (adaptive iterative dose reduction 3-dimensional [AIDR3D], standard setting); LDCTE images were reconstructed with AIDR3D and DLR (Advanced Intelligence ClearIQ Engine [AiCE], Body mild/standard/strong, Sharp Body mild/standard/strong setting). The effective radiation dose (ED), image noise, signal-to-noise ratio (SNR), overall image quality, subjective image noise, and diagnostic effectiveness were compared between the LDCTE and STDCTE groups.

RESULTS

Compared with STDCTE, the ED of LDCTE was lower by 54.1% (p<0.001). Compared with STDCTE-AIDR3D, LDCTE-AIDR3D reconstruction objective image noise and SNR were greater (p<0.05), the subjective overall image quality was lower (p<0.05), and the diagnostic efficiency was lower (AUC=0.52, p<0.05). The SNRs of reconstructedimages of LDCTE-AiCE Body Strong and LDCTE-AiCE Body Sharp standard/strong groups were greater than that of STDCTE-AIDR3D group (all p<0.05), and the diagnostic performance was better than or comparable to that of STDCTE; the AUCs were 0.83, 0.76 and 0.76, respectively CONCLUSION: Compared with STDCTE with AIDR3D, LDCTE with DLR effectively reduced the radiation dose and improve image quality in IBD patients.

Comparison of the equivalent doses of the eye lenses, thyroid, and mammary gland among three pediatric and one adult anthropomorphic phantom during the chest CT examinations using a 40 mm volume helical scan

Equivalent doses for the eye lenses, thyroid, and mammary glands were measured and compared between one adult and three pediatric anthropomorphic phantoms during chest computed tomography (CT) using 40 mm volume helical scan on the Aquilion ONE GENESIS Edition CT equipment. Placing an optically stimulated luminescence dosemeter (OSLD) on the eye lenses, thyroid, and mammary gland, we measured and compared the equivalent dose of OSLD among different phantoms during chest CT using a helical scan. Compared with adults, the equivalent doses to the eye lens, thyroid, and mammary glands were ~81%, 77%, and 63% lower in newborns, 1-year-olds, and 5-year-olds using comparable image noise during chest CT.

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF50%, d' values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3 mGy, while the lowest noise magnitude and highest d' were attained at 8.8 mGy. SR-DLR at 4.3 mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d' values, and visual rank compared to the other reconstructions at 8.8 mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning.

Optimal image quality and radiation doses with optimal tube voltages/currents for pediatric anthropomorphic phantom brains

OBJECTIVE

Using pediatric anthropomorphic phantoms (APs), we aimed to determine the scanning tube voltage/current combinations that could achieve optimal image quality and avoid excessive radiation exposure in pediatric patients.

MATERIALS AND METHODS

A 64-slice scanner was used to scan a standard test phantom to determine the volume CT dose indices (CTDIvol), and three pediatric anthropomorphic phantoms (APs) with highly accurate anatomy and tissue-equivalent materials were studied. These specialized APs represented the average 1-year-old, 5-year-old, and 10-year-old children, respectively. The physical phantoms were constructed with brain tissue-equivalent materials having a density of ρ = 1.07 g/cm3, comprising 22 numbered 2.54-cm-thick sections for the 1-year-old, 26 sections for the 5-year-old, and 32 sections for the 10-year-old. They were scanned to acquire brain CT images and determine the standard deviations (SDs), effective doses (EDs), and contrast-to noise ratios (CNRs). The APs were scanned by 21 combinations of tube voltages/currents (80, 100, or 120 kVp/10, 40, 80, 120, 150, 200, or 250 mA) and rotation time/pitch settings of 1 s/0.984:1.

RESULTS

The optimal tube voltage/current combinations yielding optimal image quality were 80 kVp/80 mA for the 1-year-old AP; 80 kVp/120 mA for the 5-year-old AP; and 80 kVp/150 mA for the 10-year-old AP. Because these scanning tube voltages/currents yielded SDs, respectively, of 12.81, 13.09, and 12.26 HU, along with small EDs of 0.31, 0.34, and 0.31 mSv, these parameters and the induced values were expediently defined as optimal.

CONCLUSIONS

The optimal tube voltages/currents that yielded optimal brain image quality, SDs, CNRs, and EDs herein are novel and essentially important. Clinical translation of these optimal values may allow CT diagnosis with low radiation doses to children's heads.

Updates in pediatric upper extremity imaging

Minimizing radiation exposure is crucial for both patient and provider safety. The harmful effects of ionizing radiation are well-documented. Further research is necessary to effectively decrease these risks. The present study compiles the most recent data available from orthopaedic surgery and radiology literature, with a focus on pediatric upper extremity imaging. The purpose of this study is to give a comprehensive update in order to improve patient and provider safety and guide future research.Radiographs are the most commonly employed imaging modality in the upper extremity, and there is a wealth of articles focusing on optimizing its use in pediatric patients. Recommendations include utilizing in-room fluoroscopy for final imaging after closed forearm fracture reduction in the emergency department and foregoing formal post-reduction radiographs. Additionally, literature supports that early postoperative radiographs and radiographs after pin removal in patients who have undergone closed reduction and percutaneous pinning of supracondylar humerus fractures do not change management. Similarly, pediatric patients who have been treated for musculoskeletal infection do not require follow-up radiographs, in the absence of clinical concern. Other imaging modalities, such as ultrasound, computerized tomography (CT), and magnetic resonance imaging (MRI) have expanded their indications in pediatric upper extremity injuries in recent years. This includes ultrasound for diagnosing fractures and tendon pathologies, new CT technology that decreases radiation exposure, and MRI scans with potentially safer contrast agents.In summary, research has been expanding our understanding of radiation exposure and exploring ways to minimize this during pediatric upper extremity imaging. Further research is necessary to facilitate safer diagnostic tests in pediatric patients.

Key Concepts

(1)Fluoroscopy should be utilized as definitive post-reduction imaging after closed reduction of pediatric forearm fractures.(2)Radiographs do not need to be obtained in the early-postoperative setting or after pin removal in patients who have undergone closed reduction and percutaneous pinning of supracondylar humerus fractures.(3)Only obtain follow-up radiographs if there is a clinical concern in pediatric patients who have been treated for a musculoskeletal infection.(4)The application of ultrasound, CT scan, and MRI are continuing to expand and improve in pediatric upper extremity pathologies.

Novel Deep Learning Denoising Enhances Image Quality and Lowers Radiation Exposure in Interventional Bronchial Artery Embolization Cone Beam CT

OBJECTIVES

In interventional bronchial artery embolization (BAE), periprocedural cone beam CT (CBCT) improves guiding and localization. However, a trade-off exists between 6-second runs (high radiation dose and motion artifacts, but low noise) and 3-second runs (vice versa). This study aimed to determine the efficacy of an advanced deep learning denoising (DLD) technique in mitigating the trade-offs related to radiation dose and image quality during interventional BAE CBCT.

MATERIALS AND METHODS

This study included BMI-matched patients undergoing 6-second and 3-second BAE CBCT scans. The dose-area product values (DAP) were obtained. All datasets were reconstructed using standard weighted filtered back projection (OR) and a novel DLD software. Objective image metrics were derived from place-consistent regions of interest, including CT numbers of the Aorta and lung, noise, and contrast-to-noise ratio. Three blinded radiologists performed subjective assessments regarding image quality, sharpness, contrast, and motion artifacts on all dataset combinations in a forced-choice setup (-1 = inferior, 0 = equal; 1 = superior). The points were averaged per item for a total score. Statistical analysis ensued using a properly corrected mixed-effects model with post hoc pairwise comparisons.

RESULTS

Sixty patients were assessed in 30 matched pairs (age 64 ± 15 years; 10 female). The mean DAP for the 6 s and 3 s runs was 2199 ± 185 µGym² and 1227 ± 90 µGym², respectively. Neither low-dose imaging nor the reconstruction method introduced a significant HU shift (p ≥ 0.127). The 3 s-DLD presented the least noise and superior contrast-to-noise ratio (CNR) (p < 0.001). While subjective evaluation revealed no noticeable distinction between 6 s-DLD and 3 s-DLD in terms of quality (p ≥ 0.996), both outperformed the OR variants (p < 0.001). The 3 s datasets exhibited fewer motion artifacts than the 6 s datasets (p < 0.001).

CONCLUSIONS

DLD effectively mitigates the trade-off between radiation dose, image noise, and motion artifact burden in regular reconstructed BAE CBCT by enabling diagnostic scans with low radiation exposure and inherently low motion artifact burden at short examination times.

Pediatric evaluations for deep learning CT denoising

BACKGROUND

Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL denoising methods, are used in both adult and pediatric populations. Pediatric body habitus and size can differ significantly from adults and vary dramatically from newborns to adolescents. Ensuring that pediatric subgroups of different body sizes are not disadvantaged by DL methods requires evaluations capable of assessing performance in each subgroup.

PURPOSE

To assess DL CT denoising in pediatric and adult-sized patients, we built a framework of computer simulated image quality (IQ) control phantoms and evaluation methodology.

METHODS

The computer simulated IQ phantoms in the framework featured pediatric-sized versions of standard CatPhan 600 and MITA-LCD phantoms with a range of diameters matching the mean effective diameters of pediatric patients ranging from newborns to 18 years old. These phantoms were used in simulating CT images that were then inputs for a DL denoiser to evaluate performance in different sized patients. Adult CT test images were simulated using standard-sized phantoms scanned with adult scan protocols. Pediatric CT test images were simulated with pediatric-sized phantoms and adjusted pediatric protocols. The framework's evaluation methodology consisted of denoising both adult and pediatric test images then assessing changes in image quality, including noise, image sharpness, CT number accuracy, and low contrast detectability. To demonstrate the use of the framework, a REDCNN denoising model trained on adult patient images was evaluated. To validate that the DL model performance measured with the proposed pediatric IQ phantoms was representative of performance in more realistic patient anatomy, anthropomorphic pediatric XCAT phantoms of the same age range were also used to compare noise reduction performance.

RESULTS

Using the proposed pediatric-sized IQ phantom framework, size differences between adult and pediatric-sized phantoms were observed to substantially influence the adult trained DL denoising model's performance. When applied to adult images, the DL model achieved a 60% reduction in noise standard deviation without substantial loss in sharpness in mid or high spatial frequencies. However, in smaller phantoms the denoising performance dropped due to different image noise textures resulting from the smaller field of view (FOV) between adult and pediatric protocols. In the validation study, noise reduction trends in the pediatric-sized IQ phantoms were found to be consistent with those found in anthropomorphic phantoms.

CONCLUSION

We developed a framework of using pediatric-sized IQ phantoms for pediatric subgroup evaluation of DL denoising models. Using the framework, we found the performance of an adult trained DL denoiser did not generalize well in the smaller diameter phantoms corresponding to younger pediatric patient sizes. Our work suggests noise texture differences from FOV changes between adult and pediatric protocols can contribute to poor generalizability in DL denoising and that the proposed framework is an effective means to identify these performance disparities for a given model.

Potential benefits of photon counting detector computed tomography in pediatric imaging

Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows "always-on" dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography

OBJECTIVES

To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA).

METHODS

Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms. For each image series, image noise, and contrast-to-noise ratio (CNR) at the left main trunk, right coronary artery, left anterior descending artery, and left circumflex artery were quantified. Blooming artifacts from calcified plaques were measured. Image sharpness, noise magnitude, noise texture, edge smoothness, overall quality, and delineation of the coronary wall, calcified and noncalcified plaques, cardiac muscle, and valves were subjectively ranked on a 4-point scale (1, worst; 4, best). The quantitative parameters and subjective scores were compared among the four reconstructions. Task-based image quality was assessed with a physical evaluation phantom. The detectability index for the objects simulating the coronary lumen, calcified plaques, and noncalcified plaques was calculated from the noise power spectrum (NPS) and task-based transfer function (TTF).

RESULTS

SR-DLR yielded significantly lower image noise and blooming artifacts with higher CNR than HIR, MBIR, and NR-DLR (all p < 0.001). The best subjective scores for all the evaluation criteria were attained with SR-DLR, with significant differences from all other reconstructions (p < 0.001). In the phantom study, SR-DLR provided the highest NPS average frequency, TTF50%, and detectability for all task objects.

CONCLUSION

SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms.

CLINICAL RELEVANCE STATEMENT

The novel SR-DLR algorithm has the potential to facilitate accurate assessment of coronary artery disease on CCTA by providing excellent image quality in terms of spatial resolution, noise characteristics, and object detectability.

KEY POINTS

• SR-DLR designed for CCTA improved image sharpness, noise property, and delineation of cardiac structures with reduced blooming artifacts from calcified plaques relative to HIR, MBIR, and NR-DLR. • In the task-based image-quality assessments, SR-DLR yielded better spatial resolution, noise property, and detectability for objects simulating the coronary lumen, coronary calcifications, and noncalcified plaques than other reconstruction techniques. • The image reconstruction times of SR-DLR were shorter than those of MBIR, potentially serving as a novel standard-of-care reconstruction technique for CCTA performed on a 320-row CT scanner.

Phenomic Imaging

Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1-158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments

ABSTRACT

Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction

PURPOSE

To evaluate the image quality and visibility of pancreatic ductal adenocarcinoma (PDAC) in 80-kVp pancreatic CT protocol and compare them between hybrid-iterative reconstruction (IR) and deep-learning image reconstruction (DLIR) algorithms.

METHOD

A total of 56 patients who underwent 80-kVp pancreatic protocol CT for pancreatic disease evaluation from January 2022 to July 2022 were included in this retrospective study. Among them, 20 PDACs were observed. The CT raw data were reconstructed using 40% adaptive statistical IR-Veo (hybrid-IR group) and DLIR at medium- and high-strength levels (DLIR-M and DLIR-H groups, respectively). The CT attenuation of the abdominal aorta, pancreas, and PDAC (if present) at the pancreatic phase and those of the portal vein and liver at the portal venous phase; background noise; signal-to-noise ratio (SNR) of these anatomical structures; and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. The confidence scores for the image noise, overall image quality, and visibility of PDAC were qualitatively assigned using a five-point scale. Quantitative and qualitative parameters were compared among the three groups using Friedman test.

RESULTS

The CT attenuation of all anatomical structures were comparable among the three groups (P = .26-.86), except that of the pancreas (P = .001). Background noise was lower (P <.001) and SNRs (P <.001) and tumor-to-pancreas CNR (P <.001) were higher in the DLIR-H group than those in the other two groups. The image noise, overall image quality, and visibility of PDAC were better in the DLIR-H group than in the other two groups (P <.001-.003).

CONCLUSION

In 80-kVp pancreatic CT protocol, DLIR at a high-strength level improved image quality and visibility of PDAC.

Deep learning-based reconstruction can improve the image quality of low radiation dose head CT

OBJECTIVES

To evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images.

METHODS

This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured.

RESULTS

The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively.

CONCLUSION

DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time.

KEY POINTS

• For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. • The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s). • Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.