Accurate fetal growth evaluation is crucial for monitoring fetal health, with crown-rump length (CRL) being the gold standard for estimating gestational age and assessing growth during the first trimester. To enhance CRL evaluation accuracy and efficiency, we developed an artificial intelligence (AI)-based model (3DCRL-Net) using the 3D U-Net architecture for automatic landmark detection to achieve CRL plane localization and measurement in 3D ultrasound. We then compared its performance to that of experienced radiologists using both 2D and 3D ultrasound for fetal growth assessment. This prospective consecutive study collected fetal data from 1,326 ultrasound screenings conducted at 11-14 weeks of gestation (June 2021 to June 2023). Three experienced radiologists performed fetal screening using 2D video (2D-RAD) and 3D volume (3D-RAD) to obtain the CRL plane and measurement. The 3DCRL-Net model automatically outputs the landmark position, CRL plane localization and measurement. Three specialists audited the planes achieved by radiologists and 3DCRL-Net as standard or non-standard. The performance of CRL landmark detection, plane localization, measurement and time efficiency was evaluated in the internal testing dataset, comparing results with 3D-RAD. In the external dataset, CRL plane localization, measurement accuracy, and time efficiency were compared among the three groups. The internal dataset consisted of 126 cases in the testing set (training: validation: testing = 8:1:1), and the external dataset included 245 cases. On the internal testing set, 3DCRL-Net achieved a mean absolute distance error of 1.81 mm for the nine landmarks, higher accuracy in standard plane localization compared to 3D-RAD (91.27% vs. 80.16%), and strong consistency in CRL measurements (mean absolute error (MAE): 1.26 mm; mean difference: 0.37 mm, P = 0.70). The average time required per fetal case was 2.02 s for 3DCRL-Net versus 2 min for 3D-RAD (P < 0.001). On the external testing dataset, 3DCRL-Net demonstrated high performance in standard plane localization, achieving results comparable to 2D-RAD and 3D-RAD (accuracy: 91.43% vs. 93.06% vs. 86.12%), with strong consistency in CRL measurements, compared to 2D-RAD, which showed an MAE of 1.58 mm and a mean difference of 1.12 mm (P = 0.25). For 2D-RAD vs. 3DCRL-Net, the Pearson correlation and R² were 0.96 and 0.93, respectively, with an MAE of 0.11 ± 0.12 weeks. The average time required per fetal case was 5 s for 3DCRL-Net, compared to 2 min for 3D-RAD and 35 s for 2D-RAD (P < 0.001). The 3DCRL-Net model provides a rapid, accurate, and fully automated solution for CRL measurement in 3D ultrasound, achieving expert-level performance and significantly improving the efficiency and reliability of first-trimester fetal growth assessment.

To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± standard deviation, 63 ± 14 years; 100 men) were included. Standard-dose scanning was performed with a tube voltage of 100 kVp, automatic tube current modulation, and IR reconstruction, whereas reduced-dose scanning was performed with a tube voltage of 120 kVp, a SilverBeam filter, and DLR. Additionally, a contrast-enhanced (CE)-boost image was obtained for reduced-dose scanning. Radiation dose, objective, and subjective image analyses were performed in each body mass index (BMI) category. The radiation dose for SilverBeam with DLR was significantly lower than that of standard dose with IR, with an average reduction in the effective dose of 59.0% (1.87 vs. 4.57 mSv). Standard dose with IR (10.59 ± 1.75) and SilverBeam with DLR (10.60 ± 1.08) showed no significant difference in image noise (p = 0.99). In the obese group (BMI > 25 kg/m²), there were no significant differences in SNRs of the liver, pancreas, and spleen between standard dose with IR and SilverBeam with DLR. SilverBeam with DLR + CE-boost demonstrated significantly better SNRs and CNRs, compared with standard dose with IR and SilverBeam with DLR. DLR combined with silver filter is effective for routine abdominopelvic CT, achieving a clearly reduced radiation dose while providing image quality that is non-inferior to standard dose with IR.

Satisfied reduction of fracture is hard to achieve. The purpose of this study is to develop a virtual fracture reduction technique using conditional GAN (Generative Adversarial Network), and evaluate its performance in simulating and guiding reduction of femoral neck fracture, which is hard to reduce. We compared its reduction quality with manual reduction performed by orthopedic surgeons. It is a pilot study for augmented reality assisted femoral neck fracture surgery. To establish the gold standard of reduction, we invited an orthopedic surgeon to perform virtual reduction registration with reference to the healthy proximal femur. The invited orthopedic surgeon also performed manual reduction by Mimics software to represent the capability of human doctor. Then we trained conditional GAN models on our dataset, which consisted 208 images from 208 different patients. For displaced femoral neck fractures, it is not easy to measure the accurate angles, like Pauwels angle, of the fracture line. However, the fracture lines would be clearer after reduction. We compared the results of manual reduction, conditional GAN models and registration by Pauwels angle, Garden index and satisfied reduction rate. We tried different number of downsampling (α) to optimize the performance of conditional GAN models. There were 208 pre-surgical CT scans from 208 patients included in our study (age 69.755 ± 13.728, including 88 men). The Pauwles angles of conditional GAN model(α = 0) was 38.519°, which was significantly more stable than manual reduction (44.647°, p < 0.001). The Garden indices of conditional GAN model(α = 0) was 176.726°, which was also significantly more stable than manual reduction (163.590°, p = 0.002). The satisfied reduction rate of conditional GAN model(α = 0) was 88.372%, significantly higher than manual reduction (53.488%, p < 0.001). The Pauwels angles, Garden indices and satisfied reduction rate of conditional GAN model(α = 0) showed no difference to registration. Conditional GAN model(α = 0) can achieve better performance in the virtual reduction of femoral neck fracture than orthopedic surgeon.

This prospective study aimed to assess the feasibility of a half-Fourier single-shot turbo spin echo sequence (HASTE) with deep learning (DL) reconstruction for ultrafast imaging of the bladder with reduced susceptibility to motion artifacts. 50 patients underwent pelvic T2w imaging at 3 Tesla using the following MR sequences in sagittal orientation without antiperistaltic premedication: T2-TSE (time of acquisition [TA]: 2.03-4.00 min), standard HASTE (TA: 0.65-1.10 min), and DL-HASTE (TA: 0.25-0.47 min), with a slice thickness of 3 mm and a varying number of slices (25-45). Three radiologists evaluated the image quality of the three sequences quantitatively and qualitatively. Overall image quality of DL-HASTE (average score: 5) was superior to HASTE and T2-TSE (p < .001). DL-HASTE provided the clearest bladder wall delineation, especially in the apical part of the bladder (p < .001). SNR (36.3 ± 6.3) and CNR (50.3 ± 19.7) were the highest on DL-HASTE, followed by T2-TSE (33.1 ± 6.3 and 44.3 ± 21.0, respectively; p < .05) and HASTE (21.7 ± 5.4 and 35.8 ± 17.5, respectively; p < .01). A limitation of DL-HASTE and HASTE was the susceptibility to urine flow artifact within the bladder, which was absent or only minimal on T2-TSE. Diagnostic confidence in assessment of the bladder was highest with the combination of DL-HASTE and T2-TSE (p < .05). DL-HASTE allows for ultrafast imaging of the bladder with high image quality and is a promising addition to T2-TSE.

Magnetic resonance imaging (MRI) with a proton density fat fraction (PDFF) sequence is the most accurate, noninvasive method for assessing hepatic steatosis. However, manual measurement on the PDFF map is time-consuming. This study aimed to validate automated whole-liver fat quantification for assessing hepatic steatosis with MRI-PDFF. In this prospective study, 80 patients were enrolled from August 2020 to January 2023. Baseline MRI-PDFF and magnetic resonance spectroscopy (MRS) data were collected. The analysis of MRI-PDFF included values from automated whole-liver segmentation (autoPDFF) and the average value from measurements taken from eight segments (avePDFF). Twenty patients with ≥10% autoPDFF values who received 24 weeks of exercise training were also collected for the chronologic evaluation. The correlation and concordance coefficients (r and ρ) among the values and differences were calculated. There were strong correlations between autoPDFF versus avePDFF, autoPDFF versus MRS, and avePDFF versus MRS (r=0.963, r=0.955, and r=0.977, all p<0.001). The autoPDFF values were also highly concordant with the avePDFF and MRS values (ρ=0.941 and ρ=0.942). The autoPDFF, avePDFF, and MRS values consistently decreased after 24 weeks of exercise. The change in autoPDFF was also highly correlated with the changes in avePDFF and MRS (r=0.961 and r=0.870, all p<0.001). Automated whole-liver fat quantification might be feasible for clinical trials and practice, yielding values with high correlations and concordance with the time-consuming manual measurements from the PDFF map and the values from the highly complex processing of MRS (ClinicalTrials.gov identifier: NCT04463667).

To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasopharyngeal carcinoma (NPC) and evaluate its feasibility and clinical performance. Patients with NPC scheduled for MSI RT workflow were prospectively enrolled. This workflow integrates RT procedures from computed tomography (CT) scan to beam delivery, all performed with the patient on the treatment couch. Workflow performance, tumor response, patient-reported acute toxicities, and quality of life were evaluated. From March 2022 to October 2023, 120 newly diagnosed, nonmetastatic patients with NPC were enrolled. Of these, 117 completed the workflow with a median duration of 23.2 minutes (range, 16.3-45.8). Median translation errors were 0.2 mm (from CT scan to planning approval) and 0.1 mm (during beam delivery). AI-generated contours required minimal revision for the high-risk clinical target volume and organs at risk, minor revision for the involved cervical lymph nodes and low-risk clinical target volume (median Dice similarity coefficients (DSC), 0.98 and 0.94), and more revision for the gross tumor at the primary site and the involved retropharyngeal lymph nodes (median DSC, 0.84). Of 117 AI-generated plans, 108 (92.3%) passed after the first optimization, with ≥97.8% of target volumes receiving ≥100% of the prescribed dose. Dosimetric constraints were met for most organs at risk, except the thyroid and submandibular glands. One hundred and fifteen patients achieved a complete response at week 12 post-RT, while 14 patients reported any acute toxicity as "very severe" from the start of RT to week 12 post-RT. AI-empowered MSI RT workflow for patients with NPC is clinically feasible in a single institutional setting compared with standard, human-based RT workflow.

Enhance mammography quality to increase cancer detection by implementing continuous AI-driven feedback mechanisms, ensuring reliable, consistent, and high-quality screening by the 'Perfect', 'Good', 'Moderate', and 'Inadequate' (PGMI) criteria. To assess the impact of the AI software 'b-box^TM' on mammography quality, we conducted a comparative analysis of PGMI scores. We evaluated scores 50 days before (A) and after the software's implementation in 2021 (B), along with assessments made in the first week of August 2022 (C1) and 2023 (C2), comparing them to evaluations conducted by two readers. Except for postsurgical patients, we included all diagnostic and screening mammograms from one tertiary hospital. A total of 4577 mammograms from 1220 women (mean age: 59, range: 21-94, standard deviation: 11.18) were included. 1728 images were obtained before (A) and 2330 images after the 2021 software implementation (B), along with 269 images in 2022 (C1) and 250 images in 2023 (C2). The results indicated a significant improvement in diagnostic image quality (p < 0.01). The percentage of 'Perfect' examinations rose from 22.34% to 32.27%, while 'Inadequate' images decreased from 13.31% to 5.41% in 2021, continuing the positive trend with 4.46% and 3.20% 'inadequate' images in 2022 and 2023, respectively (p < 0.01). Using a reliable software platform to perform AI-driven quality evaluation in real-time has the potential to make lasting improvements in image quality, support radiographers' professional growth, and elevate institutional quality standards and documentation simultaneously. Question How can AI-powered quality assessment reduce inadequate mammographic quality, which is known to impact sensitivity and increase the risk of interval cancers? Findings AI implementation decreased 'inadequate' mammograms from 13.31% to 3.20% and substantially improved parenchyma visualization, with consistent subgroup trends. Clinical relevance By reducing 'inadequate' mammograms and enhancing imaging quality, AI-driven tools improve diagnostic reliability and support better outcomes in breast cancer screening.

To assess the prevalence of motion artifacts and the factors associated with them in a cohort of suspected stroke patients, and to determine their impact on diagnostic accuracy for both AI and radiologists. This retrospective cross-sectional study included brain MRI scans of consecutive adult suspected stroke patients from a non-comprehensive Danish stroke center between January and April 2020. An expert neuroradiologist identified acute ischemic, hemorrhagic, and space-occupying lesions as references. Two blinded radiology residents rated MRI image quality and motion artifacts. The diagnostic accuracy of a CE-marked deep learning tool was compared to that of radiology reports. Multivariate analysis examined associations between patient characteristics and motion artifacts. 775 patients (68 years ± 16, 420 female) were included. Acute ischemic, hemorrhagic, and space-occupying lesions were found in 216 (27.9%), 12 (1.5%), and 20 (2.6%). Motion artifacts were present in 57 (7.4%). Increasing age (OR per decade, 1.60; 95% CI: 1.26, 2.09; p < 0.001) and limb motor symptoms (OR, 2.36; 95% CI: 1.32, 4.20; p = 0.003) were independently associated with motion artifacts in multivariate analysis. Motion artifacts significantly reduced the accuracy of detecting hemorrhage. This reduction was greater for the AI tool (from 88 to 67%; p < 0.001) than for radiology reports (from 100 to 93%; p < 0.001). Ischemic and space-occupying lesion detection was not significantly affected. Motion artifacts are common in suspected stroke patients, particularly in the elderly and patients with motor symptoms, reducing accuracy for hemorrhage detection by both AI and radiologists. Question Motion artifacts reduce the quality of MRI scans, but it is unclear which factors are associated with them and how they impact diagnostic accuracy. Findings Motion artifacts occurred in 7% of suspected stroke MRI scans, associated with higher patient age and motor symptoms, lowering hemorrhage detection by AI and radiologists. Clinical relevance Motion artifacts in stroke brain MRIs significantly reduce the diagnostic accuracy of human and AI detection of intracranial hemorrhages. Elderly patients and those with motor symptoms may benefit from a greater focus on motion artifact prevention and reduction.

Phase-sensitive inversion recovery late gadolinium enhancement (LGE) improves tissue contrast, however it is challenging to combine with a free-breathing acquisition. Deep-learning (DL) algorithms have growing applications in cardiac magnetic resonance imaging (CMR) to improve image quality. We compared a novel combination of a free-breathing single-shot phase-sensitive LGE with respiratory triggering (FB-PS) sequence with DL noise reduction reconstruction algorithm to a conventional segmented phase-sensitive LGE acquired during breath holding (BH-PS). 61 adult subjects (29 male, age 51 ± 15) underwent clinical CMR (1.5 T) with the FB-PS sequence and the conventional BH-PS sequence. DL noise reduction was incorporated into the image reconstruction pipeline. Qualitative metrics included image quality, artifact severity, diagnostic confidence. Quantitative metrics included septal-blood border sharpness, LGE sharpness, blood-myocardium apparent contrast-to-noise ratio (CNR), LGE-myocardium CNR, LGE apparent signal-to-noise ratio (SNR), and LGE burden. The sequences were compared via paired t-tests. 27 subjects had positive LGE. Average time to acquire a slice for FB-PS was 4-12 s versus ~32-38 s for BH-PS (including breath instructions and break time in between breath hold). FB-PS with medium DL noise reduction had better image quality (FB-PS 3.0 ± 0.7 vs. BH-PS 1.5 ± 0.6, p < 0.0001), less artifact (4.8 ± 0.5 vs. 3.4 ± 1.1, p < 0.0001), and higher diagnostic confidence (4.0 ± 0.6 vs. 2.6 ± 0.8, p < 0.0001). Septum sharpness in FB-PS with DL reconstruction versus BH-PS was not significantly different. There was no significant difference in LGE sharpness or LGE burden. FB-PS had superior blood-myocardium CNR (17.2 ± 6.9 vs. 16.4 ± 6.0, p = 0.040), LGE-myocardium CNR (12.1 ± 7.2 vs. 10.4 ± 6.6, p = 0.054), and LGE SNR (59.8 ± 26.8 vs. 31.2 ± 24.1, p < 0.001); these metrics further improved with DL noise reduction. A FB-PS sequence shortens scan time by over 5-fold and reduces motion artifact. Combined with a DL noise reduction algorithm, FB-PS provides better or similar image quality compared to BH-PS. This is a promising solution for patients who cannot hold their breath.

This study aims to evaluate the effectiveness of a deep learning (DL)-enhanced four-fold parallel acquisition technique (P4) in improving prostate MR image quality while optimizing scan efficiency compared to the traditional two-fold parallel acquisition technique (P2). Patients undergoing prostate MRI with DL-enhanced acquisitions were analyzed from January 2024 to July 2024. The participants prospectively received T2-weighted sequences in all imaging planes using both P2 and P4. Three independent readers assessed image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR). Significant differences in contrast and gray-level properties between P2 and P4 were identified through radiomics analysis (p <.05). A total of 51 participants (mean age 69.4 years ± 10.5 years) underwent P2 and P4 imaging. P4 demonstrated higher CNR and SNR values compared to P2 (p <.001). P4 was consistently rated superior to P2, demonstrating enhanced image quality and greater diagnostic precision across all evaluated categories (p <.001). Furthermore, radiomics analysis confirmed that P4 significantly altered structural and textural differentiation in comparison to P2. The P4 protocol reduced T2w scan times by 50.8%, from 11:48 min to 5:48 min (p <.001). In conclusion, P4 imaging enhances diagnostic quality and reduces scan times, improving workflow efficiency, and potentially contributing to a more patient-centered and sustainable radiology practice.