AI-based MRI reconstruction techniques improve efficiency by reducing acquisition times whilst maintaining or improving image quality. Recent recommendations from professional bodies suggest centres should perform quality assessments on AI tools. However, monitoring long-term performance presents challenges, due to model drift or system updates. Radiologist-based assessments are resource-intensive and may be subjective, highlighting the need for efficient quality control (QC) measures. This study explores using image quality metrics (IQMs) to assess AI-based reconstructions. 58 patients undergoing standard-of-care rectal MRI were imaged using AI-based and conventional T2-weighted sequences. Paired and unpaired IQMs were calculated. Sensitivity of IQMs to detect retrospective perturbations in AI-based reconstructions was assessed using control charts, and statistical comparisons between the four MR systems in the evaluation were performed. Two radiologists evaluated the image quality of the perturbed images, giving an indication of their clinical relevance. Paired IQMs demonstrated sensitivity to changes in AI-reconstruction settings, identifying deviations outside ± 2 standard deviations of the reference dataset. Unpaired metrics showed less sensitivity. Paired IQMs showed no difference in performance between 1.5 T and 3 T systems (p > 0.99), whilst minor but significant (p < 0.0379) differences were noted for unpaired IQMs. IQMs are effective for QC of AI-based MR reconstructions, offering resource-efficient alternatives to repeated radiologist evaluations. Future work should expand this to other imaging applications and assess additional measures.

To investigate whether the deep learning reconstruction (DLR) combined with contrast-enhancement-boost (CE-boost) technique can improve the diagnostic quality of CT pulmonary angiography (CTPA) at low radiation and contrast doses, compared with routine CTPA using hybrid iterative reconstruction (HIR). This prospective two-center study included 130 patients who underwent CTPA for suspected pulmonary embolism. Patients were randomly divided into two groups: the routine CTPA group, reconstructed using HIR; and the dual-low dose CTPA group, reconstructed using HIR and DLR, additionally combined with the CE-boost to generate HIR-boost and DLR-boost images. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary arteries were quantitatively assessed. Two experienced radiologists independently ordered CT images (5, best; 1, worst) based on overall image noise and vascular contrast. Diagnostic performance for PE detection was calculated for each dataset. Patient demographics were similar between groups. Compared to HIR images of the routine group, DLR-boost images of the dual-low dose group were significantly better at qualitative scores (p < 0.001). The CT values of pulmonary arteries between the DLR-boost and the HIR images were comparable (p > 0.05), whereas the SNRs and CNRs of pulmonary arteries in the DLR-boost images were the highest among all five datasets (p < 0.001). The AUCs of DLR, HIR-boost, and DLR-boost were 0.933, 0.924, and 0.986, respectively (all p > 0.05). DLR combined with CE-boost technique can significantly improve the image quality of CTPA with reduced radiation and contrast doses, facilitating a more accurate diagnosis of pulmonary embolism. Question The dual-low dose protocol is essential for detecting pulmonary emboli (PE) in follow-up CT pulmonary angiography (PA), yet effective solutions are still lacking. Findings Deep learning reconstruction (DLR)-boost with reduced radiation and contrast doses demonstrated higher quantitative and qualitative image quality than hybrid-iterative reconstruction in the routine CTPA. Clinical relevance DLR-boost based low-radiation and low-contrast-dose CTPA protocol offers a novel strategy to further enhance the image quality and diagnosis accuracy for pulmonary embolism patients.

Accurate differentiation of the histologic invasiveness of early-stage lung adenocarcinoma is crucial for determining surgical strategies. This study aimed to investigate the potential of [⁶⁸Ga]Ga-FAPI-46 PET/CT in assessing the invasiveness of early lung adenocarcinoma presenting as ground-glass nodules (GGNs) and identifying imaging features with strong predictive potential. This prospective study (NCT04588064) was conducted between July 2020 and July 2022, focusing on GGNs that were confirmed postoperatively to be either invasive adenocarcinoma (IAC), minimally invasive adenocarcinoma (MIA), or precursor glandular lesions (PGL). A total of 45 patients with 53 pulmonary GGNs were included in the study: 19 patients with GGNs associated with PGL-MIA and 34 with IAC. Lung nodules were segmented using the Segment Anything Model in Medical Images (MedSAM) and the PET Tumor Segmentation Extension. Clinical characteristics, along with conventional and high-throughput radiomics features from High-resolution CT (HRCT) and PET scans, were analysed. The predictive performance of these features in differentiating between PGL or MIA (PGL-MIA) and IAC was assessed using 5-fold cross-validation across six machine learning algorithms. Model validation was performed on an independent external test set (n = 11). The Chi-squared, Fisher's exact, and DeLong tests were employed to compare the performance of the models. The maximum standardised uptake value (SUVmax) derived from [⁶⁸Ga]Ga-FAPI-46 PET was identified as an independent predictor of IAC. A cut-off value of 1.82 yielded a sensitivity of 94% (32/34), specificity of 84% (16/19), and an overall accuracy of 91% (48/53) in the training set, while achieving 100% (12/12) accuracy in the external test set. Radiomics-based classification further improved diagnostic performance, achieving a sensitivity of 97% (33/34), specificity of 89% (17/19), accuracy of 94% (50/53), and an area under the receiver operating characteristic curve (AUC) of 0.97 [95% CI: 0.93-1.00]. Compared with the CT-based radiomics model and the PET-based model, the combined PET/CT radiomics model did not show significant improvement in predictive performance. The key predictive feature was [⁶⁸Ga]Ga-FAPI-46 PET log-sigma-7-mm-3D_firstorder_RootMeanSquared. The SUVmax derived from [⁶⁸Ga]Ga-FAPI-46 PET/CT can effectively differentiate the invasiveness of early-stage lung adenocarcinoma manifesting as GGNs. Integrating high-throughput features from [⁶⁸Ga]Ga-FAPI-46 PET/CT images can considerably enhance classification accuracy. NCT04588064; URL: https://clinicaltrials.gov/study/NCT04588064 .

<b>Background and purpose:</b> This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Image Reconstruction (DLIR)-ability to improve image quality and reduce radiation dose compared to the established standard of Adaptive Statistical Iterative Reconstruction-V (ASIR-V), in unenhanced head computed tomography (CT). <b>Materials and methods:</b> A retrospective analysis of 163 consecutive unenhanced head CTs was conducted. Image quality assessment was computed on the objective parameters of Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), derived from 5 regions of interest (ROI). The evaluation of DLIR dose reduction abilities was based on the analysis of the PACS derived parameters of dose length product and computed tomography dose index volume (CTDIvol). <b>Results:</b> Following the application of rigorous criteria, the study comprised 35 patients. Significant image quality improvement was achieved with the implementation of DLIR, as evidenced by up to a 145% and 160% increase in SNR in supra- and infratentorial regions, respectively. CNR measurements further confirmed the superiority of DLIR over ASIR-V, with an increase of 171.5% in the supratentorial region and a 59.3% increase in the infratentorial region. Despite the signal improvement and noise reduction DLIR facilitated radiation dose reduction of up to 44% in CTDIvol. <b>Conclusion:</b> Implementation of DLIR in head CT scans enables significant image quality improvement and dose reduction abilities compared to standard ASIR-V. However, the dose reduction feature was proven insufficient to counteract the lack of gantry angulation in wide-detector scanners.

BackgroundThis study aims to present the Segmentation-based Myocardial Advanced Refinement Tracking (SMART) system, a novel artificial intelligence (AI)-based framework for transthoracic echocardiography (TTE) that incorporates motion tracking and left ventricular (LV) myocardial segmentation for automated LV mass (LVM) and global longitudinal strain (LVGLS) assessment. MethodsThe SMART system demonstrates LV speckle tracking based on motion vector estimation, refined by structural information using endocardial and epicardial segmentation throughout the cardiac cycle. This approach enables automated measurement of LVMSMART and LVGLSSMART. The feasibility of SMART is validated in 111 hypertrophic cardiomyopathy (HCM) patients (median age: 58 years, 69% male) who underwent TTE and cardiac magnetic resonance imaging (CMR). ResultsLVGLSSMART showed a strong correlation with conventional manual LVGLS measurements (Pearsons correlation coefficient [PCC] 0.851; mean difference 0 [-2-0]). When compared to CMR as the reference standard for LVM, the conventional dimension-based TTE method overestimated LVM (PCC 0.652; mean difference: 106 [90-123]), whereas LVMSMART demonstrated excellent agreement with CMR (PCC 0.843; mean difference: 1 [-11-13]). For predicting extensive myocardial fibrosis, LVGLSSMART and LVMSMART exhibited performance comparable to conventional LVGLS and CMR (AUC: 0.72 and 0.66, respectively). Patients identified as high-risk for extensive fibrosis by LVGLSSMART and LVMSMART had significantly higher rates of adverse outcomes, including heart failure hospitalization, new-onset atrial fibrillation, and defibrillator implantation. ConclusionsThe SMART technique provides a comparable LVGLS evaluation and a more accurate LVM assessment than conventional TTE, with predictive values for myocardial fibrosis and adverse outcomes. These findings support its utility in HCM management.

As the integration of artificial intelligence (AI) into radiology workflows continues to evolve, establishing standardized processes for the evaluation and deployment of AI models is crucial to ensure success. This paper outlines the creation of a Radiology AI Council at a large academic center and subsequent development of framework in the form of a rubric to formalize the evaluation of radiology AI models and onboard them into clinical workflows. The rubric aims to address the challenges faced during the deployment of AI models, such as real-world model performance, workflow implementation, resource allocation, return on investment (ROI), and impact to the broader health system. Using this comprehensive rubric, the council aims to ensure that the process for selecting AI models is both standardized and transparent. This paper outlines the steps taken to establish this rubric, its components, and initial results from evaluation of 13 models over an 8-month period. We emphasize the importance of holistic model evaluation beyond performance metrics, and transparency and objectivity in AI model evaluation with the goal of improving the efficacy and safety of AI models in radiology.

For patients with colorectal liver metastases (CRLM), total tumor volume (TTV) is prognostic. A deep-learning segmentation model for CRLM to assess TTV called COlorectal cAncer Liver metastases Assessment (COALA) has been developed. This study evaluated COALA's performance and practical utility in the radiological picture archiving and communication system (PACS). A secondary aim was to provide lessons for future researchers on the implementation of artificial intelligence (AI) models. Patients discussed between January and December 2023 in a multidisciplinary meeting for CRLM were included. In those patients, CRLM was automatically segmented in portal-venous phase CT scans by COALA and integrated with PACS. Eight expert abdominal radiologists completed a questionnaire addressing segmentation accuracy and PACS integration. They were also asked to write down general remarks. In total, 57 patients were evaluated. Of those patients, 112 contrast-enhanced portal-venous phase CT scans were analyzed. Of eight radiologists, six (75%) evaluated the model as user-friendly in their radiological workflow. Areas of improvement of the COALA model were the segmentation of small lesions, heterogeneous lesions, and lesions at the border of the liver with involvement of the diaphragm or heart. Key lessons for implementation were a multidisciplinary approach, a robust method prior to model development and organizing evaluation sessions with end-users early in the development phase. This study demonstrates that the deep-learning segmentation model for patients with CRLM (COALA) is user-friendly in the radiologist's PACS. Future researchers striving for implementation should have a multidisciplinary approach, propose a robust methodology and involve end-users prior to model development. Many segmentation models are being developed, but none of those models are evaluated in the (radiological) workflow or clinically implemented. Our model is implemented in the radiological work system, providing valuable lessons for researchers to achieve clinical implementation. Developed segmentation models should be implemented in the radiological workflow. Our implemented segmentation model provides valuable lessons for future researchers. If implemented in clinical practice, our model could allow for objective radiological evaluation.

Artificial Intelligence (AI) is increasingly being integrated into the field of musculoskeletal (MSK) radiology, from research methods to routine clinical practice. Within the field of fracture detection, AI is allowing for precision and speed previously unimaginable. Yet, AI's decision-making processes are sometimes wrought with deficiencies, undermining trust, hindering accountability, and compromising diagnostic precision. To make AI a trusted ally for radiologists, we recommend incorporating clinical history, rationalizing AI decisions by explainable AI (XAI) techniques, increasing the variety and scale of training data to approach the complexity of a clinical situation, and active interactions between clinicians and developers. By bridging these gaps, the true potential of AI can be unlocked, enhancing patient outcomes and fundamentally transforming radiology through a harmonious integration of human expertise and intelligent technology. In this article, we aim to examine the factors contributing to AI inaccuracies and offer recommendations to address these challenges-benefiting both radiologists and developers striving to improve future algorithms.

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. 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. 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.

High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent role in the future. As medical imaging advances, accurately segmenting anatomical and pathological structures is increasingly used to obtain quantitative data and valuable insights. Segmentation and volumetric analysis could enable more precise diagnosis, treatment planning, and patient monitoring. These guidelines aim to improve segmentation accuracy and consistency, allowing for better decision-making in both research and clinical environments. Practical advice on planning and organization is provided, focusing on quality, precision, and communication among clinical teams. Additionally, tips and strategies for improving segmentation practices in radiology and radiation oncology are discussed, as are potential pitfalls to avoid. KEY POINTS: As AI continues to advance, volumetry will become more integrated into clinical practice, making it essential for radiologists to stay informed about its applications in diagnosis and treatment planning. There is a significant lack of practical guidelines and resources tailored specifically for radiologists on technical topics like segmentation and volumetric analysis. Establishing clear rules and best practices for segmentation can streamline volumetric assessment in clinical settings, making it easier to manage and leading to more accurate decision-making for patient care.