The presence of metallic implants introduces bright and dark streaks that appear in computed tomography (CT) images, degrading image quality and interfering with medical diagnosis. To reduce these artifacts, deep learning approaches have been applied for metal-corrupted restoration, which usually requires a large amount of simulated degraded-clean pairs for training. To achieve metal artifact reduction (MAR) without reference images, implicit neural representation (INR) has emerged and shown capabilities for image restoration in an unsupervised manner. However, existing INR methods for MAR usually treat the spatial coordinates independently and ignore their correlation, resulting in detail loss and artifacts remaining. In this paper, we propose an INR-based unsupervised MAR framework and design a High-order Line Attention Network to capture local contextual and geometric representations from X-rays, which maps the spatial coordinates into discrete linear attenuation coefficients of imaged objects for artifact-free CT image reconstruction. The second-order feature interaction can effectively improve the spectral bias problems and fit low and high-frequency details of real signals well. The proposed line-attention module with linear complexity can establish global relationships among spatial point tokens from sampled rays. To provide more local contextual information, a multiple local adjacent ray sampling strategy is adopted to compose several sub-fan beams with more context as a training batch. With the help of these components, the unsupervised MAR framework can approximate the implicit continuous function to estimate measurements and generate artifact-free CT images. Simulated and real experiments indicated that the proposed approach achieved superior MAR performance compared with other state-of-the-art methods.

MR-guided focused ultrasound anterior capsulotomy (MRgFUS-AC) is an incisionless ablative procedure, which has shown reassuring safety and compelling efficacy in the treatment of refractory obsessive-compulsive disorder and major depressive disorder. However, in some patients lesions cannot be reliably generated due to patient-specific skull morphologies and properties. Despite screening patients for MRgFUS-AC using skull density ratio (SDR), up to 25% of cases experience treatment failure. This variability in technical success limits the real-world applicability of an otherwise highly impactful treatment, and a better predictor of success is needed. This study analyzed data from 60 attempted MRgFUS-AC treatments in 57 patients between 2017 and 2024. Treatments were categorized as success or failure based on lesion volume. Preoperative parameters, including SDR, skull thickness, angle of incidence, CSF volume, brain and head volumes, and lesion side, were recorded. Logistic and machine learning models were evaluated to construct a preoperative model to predict the probability of technical success. A total of 157 lesions were treated, of which 31 experienced treatment failure. Higher SDR, thinner skulls, and lower incident angles were significantly associated with successful outcomes (all p < 0.05). The logistic regression model performed the best among the models tested, with an accuracy of 0.81 ± 0.07 and an F1 score of 0.89 ± 0.04. The model was incorporated into a predictive tool to aid in identifying candidates for MRgFUS-AC. SDR, skull thickness, and angle of incidence significantly influenced the likelihood of successful MRgFUS-AC lesioning. Incorporating these three parameters into a predictive tool can dramatically reduce technical failure rates and may be especially informative in patients with an SDR between 0.35 and 0.55.

Abdominal aortic calcification (AAC), a marker of subclinical cardiovascular disease, has previously shown to be associated with low bone mineral density (BMD) and fracture. However, it remains unclear whether AAC is associated with trabecular bone score (TBS), a gray-level textural measure, or whether it predicts fracture risk independent of this measure. Here, we examined the cross-sectional association of AAC scored using a validated machine learning algorithm (ML-AAC24) with TBS, and their simultaneous associations with incident fractures in 7,691 individuals (93.4% women) through the Manitoba BMD Registry (mean age 75.3 years). The association between ML-AAC24 and TBS was tested using generalised linear regression. Cox proportional hazards models tested the simultaneous relationships of ML-AAC24 and TBS with incident fractures. At baseline, 41.3% of the study cohort had low (<2), 32.4% had moderate (2 to <6) and 26.3% had high (≥6) ML-AAC24. Compared to low ML-AAC24, high ML-AAC24 was associated with a 0.81% lower TBS in the multivariable-adjusted model. Independent of each other and multiple established fracture risk factors, ML-AAC24 and TBS were each associated with an increased risk of incident fractures. Specifically, high ML-AAC24 (HR 1.41 95%CI 1.15-1.73, compared to low ML-AAC24) and lower TBS (HR 1.13 95%CI 1.05-1.22, per SD decrease) were associated with increased relative hazards for any incident fracture. High ML-AAC24 and lower TBS were also associated with incident major osteoporotic fracture (HR 1.48 95%CI 1.18-1.87 and HR 1.15 95%CI 1.06-1.25, respectively) and hip fracture (HR 1.56 95%CI 1.05-2.31 and HR 1.25 95%CI 1.08-1.44, respectively). In conclusion, high ML-AAC24 is associated with lower TBS in older adults attending routine osteoporosis screening. Both measures were associated with incident fractures. The findings of this study highlight high ML-AAC24, seen in more than 1 in 4 of the study cohort, and lower TBS provide complementary prognostic information for fracture risk.

The inherent spectral properties of photon-counting computed tomography (PCCT) allow detailed material identification through decomposition techniques, but these methods often amplify image noise and artifacts. Current denoising approaches mainly focus on improving already degraded images, ignoring the fundamental noise caused by random variations in photon detection. To tackle these issues, we combine a physics-based noise analysis with deep learning to control noise during the material decomposition process. Our work has three key parts: (1) A noise analysis model that explains how random photon-count variations in the detector affect the noise levels in different materials after decomposition. This model connects the Poisson-distributed detector noise to material-specific noise patterns. (2) A self-supervised training method that combines the noise model with neural networks using probability-based optimization, allowing the system to learn from limited training data without needing high-quality data. (3) A flexible image improvement system that adapts to different body structures and noise conditions, ensuring reliable results across various scanning scenarios. Tests using real patient scan data show our method better preserves material accuracy and produces cleaner virtual monochromatic images compared to traditional approaches. Importantly, our solution works effectively with small training datasets and can be practically used in hospital settings without slowing down workflows. This research bridges the gap between theoretical noise analysis and clinical medical imaging needs, offering a balanced approach to improving PCCT technology.

This study aimed to develop machine learning-based algorithms to assist physicians in ultrasound-guided localization of the cricoid cartilage (CC), thyroid cartilage (TC), and cricothyroid membrane (CTM) for cricothyroidotomy. Adult female participants presenting to the emergency department with dyspnea or to the obstetrics and gynecology department for a scheduled cesarean section between August 2022 and July 2024 were prospectively recruited. Ultrasonographic images were collected using a wireless handheld ultrasound device connected to an edge computing tablet. Three You Only Look Once (YOLO) model variants-v5n6, v8n, and v10n-were selected for development and evaluation. A total of 608 participants (median age: 58.0 years, interquartile range [IQR]: 40.0-73.0; median body mass index: 23.2 kg/m², IQR: 20.2-26.5) contributed 117,094 ultrasonographic frames. All three YOLO-based models demonstrated high accuracy in detecting CC, TC, and CTM, with area under the receiver operating characteristic curve values exceeding 0.88. In correctly identified frames, the models effectively localized CC (IOU values: YOLOv5n6, 0.713 [95% confidence interval (CI): 0.698-0.726]; YOLOv8n, 0.718 [95% CI: 0.702-0.733]; YOLOv10n, 0.718 [95% CI: 0.701-0.734]; p value: 0.03) and TC (YOLOv5n6, 0.700 [95% CI: 0.683-0.717]; YOLOv8n, 0.706 [95% CI: 0.687-0.725]; YOLOv10n, 0.703 [95% CI: 0.783-0.721] ; p value: 0.037), though localization accuracy was lower for CTM (YOLOv5n6, 0.364 [95% CI: 0.333-0.394]; YOLOv8n, 0.363 [95% CI: 0.331-0.394]; YOLOv10n, 0.354 [95% CI: 0.325-0.381] ; p value: 0.053). The mean frames per second for YOLOv5n6, YOLOv8n, and YOLOv10n were 3.67, 13.83, and 14.13, respectively, when deployed on the handheld ultrasound platform. YOLO-based models demonstrated high accuracy in detecting and localizing CC, TC, and CTM. YOLOv8n and YOLOv10n achieved clinically acceptable real-time imaging performance when deployed on a wireless handheld ultrasound device with an edge computing tablet. Further studies are needed to assess whether this favorable performance translates into actual clinical benefits.

X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR volumetric PCCT reconstruction has been challenged by the large memory burden, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed for a New Zealand clinical trial. Specifically, we design a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and clinical data. Our results in a reader study of 8 patients from the clinical trial demonstrate a great potential to cut the radiation dose to half that of the clinical PCCT standard without compromising image quality and diagnostic value.

To evaluate the clinical utility of machine learning algorithms (MLAs) in diagnosing extra-nodal extension (ENE) using CT imaging in HNSCC. A comprehensive literature search was conducted on MEDLINE (Ovid), EMBASE, Cochrane, Scopus, and Web of Science, from January 1, 2000, to February 12, 2025. Two independent reviewers selected studies reporting the diagnostic accuracy of MLAs in detecting ENE in patients with HNSCC. The review followed PRISMA guidelines. Meta-analysis was performed using MedCalc (23.0.2), with pooled estimates of the area under the curve (AUC) and corresponding 95% confidence intervals (CI) calculated. The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was used to analyze the methodological quality of the included studies. Of 57 articles retrieved, six met inclusion criteria, encompassing 2870 lymph nodes from 1407 patients. MLAs achieved a pooled AUC of 0.92 (95% CI [0.915, 0.923], p < 0.001; fixed-effects) and 0.91 (95% CI [0.882, 0.929], p < 0.001; random-effects), outperforming radiologists who had pooled AUCs of 0.65 (95% CI [0.645-0.654], p < 0.001; fixed-effects) and 0.65 (95% CI [0.591-0.708], p < 0.001; random-effects). Furthermore, MLA achieved a sensitivity ranging from 66.9% to 91.2%, compared to 24% to 96.0% by radiologists. The specificity and accuracy of MLA ranged from 72% to 96.2% and 66% to 92.2%, respectively, compared to that of radiologists, which ranged from 43.0% to 96.0% and 51.5% to 88.6%, respectively. MLAs demonstrate superior diagnostic performance in predicting ENE in HNSCC and may serve as a valuable adjunct to radiologists in clinical practice.

To develop and validate a CT-based radiomic-clinical-dosimetric model to assess the treatment response of lung metastasis following stereotactic body radiation therapy (SBRT). 80 lung metastases treated with SBRT curative intent in a single institution were analyzed. The treatment responses of lung lesions were categorized as a complete responding (CR) group vs. a non-complete responding (NCR) group according to RECIST criteria. For each lesion, 107 features were extracted from the CT planning images. The least absolute shrinkage and selection operator (LASSO) was used for features selection. An eXtreme Gradient Boosting (XGBoost) model was trained and validated. SHAP analysis was used to provide insights into the impact of each variable on the model's predictions. Eight radiomic features, one dosimetric variable and no clinical variables were identified by LASSO and used to build the XGBoost model. The model yielded AUCs of 0.897 (95%CI 0.860-0.935) and 0.864 (95%CI 0.803-0.924) in the training cohort and validation cohort, respectively. Skewness, surface-volume ratio, sphericity and BED10 were the most significant variables in predicting CR. The SHAP plots illustrated the feature's global and local impact to the model, explaining the model output in a clinician-friendly way. The integration of the XGBoost model with the SHAP strategy was able to assess lung lesions CR following SBRT, with the potential to assist clinicians in directing personalized SBRT strategies in an understandable manner. The explanaible radiomics model we propose can better predict the treatment response of lung metastasis after SBRT and provide further guidance for clinical practice.

The aim of this retrospective study was the evaluation of the patient-reported and radiological outcome of intravenous Iloprost therapy in the treatment of spontaneous osteonecrosis of the knee (SONK). 36 patients (age 57.3 ± 8.7 years, 38.9% women, 61.1% men) who received Iloprost between 2018 and 2021 due to SONK (ARCO I and II) were included in this retrospective cohort study. Outcome was evaluated by pre- and postinterventional pain (Numeric Rating Scale - NRS), patient reported outcome (subjective knee value (SKV), Oxford Knee Score (OKS)) at latest follow-up (2.9 months ± 1) as well as quantitative artificial intelligence assisted analysis of bone marrow edema (BME) in Magnetic Resonance Imaging (MRI) before and after 3 months. Radiologically, there was a 71% reduction in edema (pre-intervention: 37.0 cm³±37.7, post-intervention: 10.8 cm³ ± 14.9, p < 0.01). Overall satisfaction was 2.0 ± 1.3, SKV was 83.3%±16.6 and NRS at follow-up was 1.3 ± 1.8. OKS reached 33.6 ± 12.0. No major complications were observed. Rare side effects were dizziness which required premature termination of Ilomedin therapy on day 3. Iloprost treatment seems a safe and promising therapeutic option also in SONK with excellent subjective outcome and reduction of BME of 70% within 3 months after Iloprost infusion.

Experimental evidence supporting the existence of the viscerosomatic reflex highlights an involvement of multiple vertebral levels when renal pathology is present. Further exploration of this reflex, particularly in the context of nephrolithiasis, could offer valuable insights for osteopathic treatments related to this pathology. Open-sourced machine learning datasets provide a valuable source of imaging data for investigating osteopathic phenomena including the viscerosomatic reflex. This study aimed to compare the rotation of vertebrae at levels associated with the viscerosomatic reflex in renal pathology in patients with nephrolithiasis vs. those without kidney stones. A total of 210 unenhanced computed tomography (CT) scans were examined from an open-sourced dataset designed for kidney and kidney stone segmentation. Among these, 166 scans were excluded due to pathologies that could affect analysis (osteophytes, renal masses, etc.). The 44 scans included in the analysis encompassed 292 relevant vertebrae. Of those, 15 scans were of patients with kidney stones in the right kidney, 13 in the left kidney, 7 bilaterally, and 11 without kidney stones. These scans included vertebral levels from T5-L5, with the majority falling within T10-L5. An open-sourced algorithm was employed to segment individual vertebrae, generating models that maintained their orientation in three-dimensional (3D) space. A self-coded 3D slicer module utilizing vertebral symmetry for rotation detection was then applied. Two-way analysis of variance (ANOVA) testing was conducted to assess differences in vertebral rotation between the four possible combinations of kidney stone location (left-sided, right-sided, bilateral, or none) and vertebral levels (T10-L4). Subsequently, the two-way ANOVA analysis was narrowed down to include various combinations of three vertebral levels (T10-L4) to identify the most significant levels. We observed a statistically significant difference in average vertebral rotation (p=0.0038) dependent on kidney stone location. Post-hoc analysis showed an average difference in rotation of -1.38° leftward between scans that contained left kidney stones compared to no kidney stones (p=0.027), as well as an average difference of -1.72° leftward in the scans containing right kidney stones compared to no kidney stone (p=0.0037). The average differences in rotation between the remaining stone location combinations were not statistically significant. Narrowed analysis of three vertebral level combinations showed a single statistically significant combination (T10, T12, and L4) out of a total of 35 combinations (p=0.028). A subsequent post-hoc procedure showed that angular rotation at these levels had the only statistically significant contribution to the difference between scans containing right kidney stones and no kidney stones (p=0.046). This study observed a statistically significant difference in the rotation of vertebrae at the levels associated with the viscerosomatic reflex between patients with unilateral kidney stones and those without kidney stones. The vertebral levels with the highest significance of association with this finding, particularly in right kidney stones, were T10, T12, and L4.