Application of emerging technologies in ischemic stroke: from clinical study to basic research.

Stroke is a primary cause of noncommunicable disease-related death and disability worldwide. The most common form, ischemic stroke, is increasing in incidence resulting in a significant burden on patients and society. Urgent action is thus needed to address preventable risk factors and improve treatment methods. This review examines emerging technologies used in the management of ischemic stroke, including neuroimaging, regenerative medicine, biology, and nanomedicine, highlighting their benefits, clinical applications, and limitations. Additionally, we suggest strategies for technological development for the prevention, diagnosis, and treatment of ischemic stroke.

Report of the HIMSS-SIIM Enterprise Imaging Community Data Standards Evaluation Workgroup: Anatomic Ontology Assessment.

Previously, the lack of a standard body part ontology has been identified as a critical deficiency needed to enable enterprise imaging. This whitepaper aims to provide a comprehensive assessment of anatomical ontologies with the aim of facilitating enterprise imaging. It offers an overview of the process undertaken by the Health Information Management Systems Society (HIMSS) and Society for Imaging Informatics in medicine (SIIM) Enterprise Imaging Community Data Standards Evaluation workgroup to assess the viability of existing ontologies for supporting cross-disciplinary medical imaging workflows. The report analyzes the responses received from representatives of three significant ontologies: SNOMED CT, LOINC, and ICD, and delves into their suitability for the complex landscape of enterprise imaging. It highlights the strengths and limitations of each ontology, ultimately concluding that SNOMED CT is the most viable solution for standardizing anatomy terminology across the medical imaging community.

Low-Cost Histopathological Mitosis Detection for Microscope-acquired Images.

Cancer outcomes are poor in resource-limited countries owing to high costs and insufficient pathologist-population ratio. The advent of digital pathology has assisted in improving cancer outcomes, however, Whole Slide Image scanners are expensive and not affordable in low-income countries. Microscope-acquired images on the other hand are cheap to collect and can be more viable for automation of cancer detection. In this study, we propose LCH-Network, a novel method to identify the cancer mitotic count from microscope-acquired images. We introduced Label Mix, and also synthesized images using GANs to handle data imbalance. Moreover, we applied progressive resolution to handle different image scales for mitotic localization. We achieved F1-Score of 0.71 and outperformed other existing techniques. Our findings enable mitotic count estimation from microscopic images with a low-cost setup. Clinically, our method could help avoid presumptive treatment without a confirmed cancer diagnosis.

Quantitative radiomics and qualitative LI-RADS imaging descriptors for non-invasive assessment of ß-catenin mutation status in hepatocellular carcinoma.

PURPOSE: Gain-of-function mutations in CTNNB1, gene encoding for ß-catenin, are observed in 25-30% of hepatocellular carcinomas (HCCs). Recent studies have shown ß-catenin activation to have distinct roles in HCC susceptibility to mTOR inhibitors and resistance to immunotherapy. Our goal was to develop and test a computational imaging-based model to non-invasively assess ß-catenin activation in HCC, since liver biopsies are often not done due to risk of complications. METHODS: This IRB-approved retrospective study included 134 subjects with pathologically proven HCC and available ß-catenin activation status, who also had either CT or MR imaging of the liver performed within 1 year of histological assessment. For qualitative descriptors, experienced radiologists assessed the presence of imaging features listed in LI-RADS v2018. For quantitative analysis, a single biopsy proven tumor underwent a 3D segmentation and radiomics features were extracted. We developed prediction models to assess the ß-catenin activation in HCC using both qualitative and quantitative descriptors. RESULTS: There were 41 cases (31%) with ß-catenin mutation and 93 cases (69%) without. The model's AUC was 0.70 (95% CI 0.60, 0.79) using radiomics features and 0.64 (0.52, 0.74; p = 0.468) using qualitative descriptors. However, when combined, the AUC increased to 0.88 (0.80, 0.92; p = 0.009). Among the LI-RADS descriptors, the presence of a nodule-in-nodule showed a significant association with ß-catenin mutations (p = 0.015). Additionally, 88 radiomics features exhibited a significant association (p < 0.05) with ß-catenin mutations. CONCLUSION: Combination of LI-RADS descriptors and CT/MRI-derived radiomics determine ß-catenin activation status in HCC with high confidence, making precision medicine a possibility.

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery.

Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <2% residual error margin. Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

Exploring pre-MRI imaging tests: patient survey reveals potential implications for healthcare efficiency in Israel.

BACKGROUND: Medical imaging tests are vital in healthcare but can be costly, impacting national health expenditures. Magnetic resonance imaging (MRI) is a crucial diagnostic tool for assessing medical conditions. However, the rising demand for MRI scans has frequently strained available resources. This study aimed to estimate the prevalence of different imaging tests in individuals who eventually had an MRI, in the Israeli public health system. METHODS: An online survey of patient experience of scheduling an MRI was conducted in January-February 2023, among 557 Israeli adults, representing all four health maintenance organizations (HMOs). All participants had undergone an MRI in the public health system within the past year. RESULTS: Results showed that 60% of participants underwent other imaging tests before their MRI scan. Of those, computed tomography (CT) scans (43%), X-rays (39%), and ultrasounds (32%) were the most common additional imaging procedures. In addition, of the 60% of participants, 23% had undergone more than one prior imaging examination. CONCLUSIONS: These findings highlight the high prevalence of preliminary imaging tests prior to MRI, with many patients undergoing multiple tests for the same problem. The health system may need to evaluate whether current clinical guidelines defining the use of various imaging tests are cost-effective.

Virtual Hemodynamic Assessment of Coronary Lesions: The Advent of Functional Angiography and Coronary Imaging.

The fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive and invasive imaging modalities. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy. METHODS: Search of the bibliographic database for articles published from 2011 to 2023 using the following search terms: invasive FFR and non-invasive FFR. Pooled analysis of the sensitivity and specificity, with the corresponding confidence intervals from 32% to 94%. In addition, the summary processing times were determined. RESULTS: In total, 24 studies published between 2011 and 2023 were included, with a total of 13,591 patients and 3345 vessels. The diagnostic accuracy of the invasive and non-invasive techniques at the per-patient level was 89% (95% CI, 85-92%) and 76% (95% CI, 61-80%), respectively, while on the per-vessel basis, it was 92% (95% CI, 82-88%) and 81% (95% CI, 75-87%), respectively. CONCLUSION: These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice.

Enhancing YOLO5 for the Assessment of Irregular Pelvic Radiographs with Multimodal Information.

Developmental dysplasia of the hip (DDH) is one of the most common orthopedic disorders in infants and young children. Accurate identification and localization of anatomical landmarks are prerequisites for the diagnosis of DDH. In recent years, various works have employed deep learning algorithms on radiography images for DDH diagnosis. However, none of these works have considered the incorporation of multimodal information. The pelvis exhibits distinct structures at different developmental stages, and there are also gender-based differences. In light of this, this study proposes a method to enhance the performance of deep learning models in diagnosing DDH by incorporating age and gender information into the channels. The study utilizes YOLO5 to construct a deep learning network for detecting hip joint landmarks. Moreover, a comprehensive dataset of 7750 pelvic X-ray images is established, covering ages from 4 months to 16 years and encompassing various conditions, such as deformities and post-operative cases, which authentically capture the temporal diversity and pathological complexities of DDH. Experimental results show that the YOLO5 model with integrated multimodal information achieves a mAP0.5-0.95 of 83.1% and a diagnostic accuracy of 86.7% in test dataset. The F1 scores for diagnosing cases of normal (NM), suspected dislocation (SD), mild dislocation (MD), and heavily dislocation (HD) are 90.9%, 79.8%, 63.5%, and 97.4%, respectively. Furthermore, experiments conducted on datasets of different sizes and networks of different sizes demonstrate the beneficial impact of multimodal information in improving the effectiveness of deep learning in diagnosing DDH.

Unreimbursed Costs of Multidisciplinary Conferences to a Radiology Department: A Prospective Analysis at an Academic Medical Center.

PURPOSE: Multidisciplinary conferences (MDCs) are important for clinical care but are unreimbursed and can be time-consuming for radiologists to prepare for and present. The purpose of this single-center, prospective, survey-based study is to measure the per-conference time and total time radiologists devote to MDCs at a single academic medical center. Secondary objectives are to determine the source of radiologist preparation time, and calculate the per conference and overall radiology departmental costs of MDC participation. METHODS: A prospective survey was performed to capture all radiology preparation and presentation time for MDCs in a 3-month period, which was then annualized. Total cost was calculated on the basis of Association of Administrators in Academic Radiology survey data for nonchair academic radiologist compensation plus a 30% fringe-benefit rate. RESULTS: The survey response rate was 86.9%. A total of 3,358 hours were devoted annually to MDCs, which represents time equivalent to 1.9 full-time equivalents or $1,155,152 in unreimbursed radiology departmental costs. Per-MDC total preparation and presentation time was 2.7 hours, at an annual cost of $46,440 for each weekly MDC. Radiologists used a combination of personal time (49.7%), academic time (42%), and/or clinical time (35.4%) to prepare for MDCs. Radiologists devoted a mean of 47.9 hours (1.2 weeks) of time per annum to MDCs. CONCLUSIONS: Radiologist time devoted to MDCs at the survey institution was substantial, and preparation time was drawn disproportionately from personal and academic time, which may have negative implications for burnout, recruitment and retention, and academic productivity unless it is effectively mitigated.

Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound.

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.

A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data.

In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial quantities of high-quality data, a resource often lacking in numerous medical imaging scenarios. One way to overcome this deficiency is to artificially generate such images. Therefore, in this comparative study we train five generative models to artificially increase the amount of available data in such a scenario. This synthetic data approach is evaluated on a a downstream classification task, predicting four causes for pneumonia as well as healthy cases on 1082 chest X-ray images. Quantitative and medical assessments show that a Generative Adversarial Network (GAN)-based approach significantly outperforms more recent diffusion-based approaches on this limited dataset with better image quality and pathological plausibility. We show that better image quality surprisingly does not translate to improved classification performance by evaluating five different classification models and varying the amount of additional training data. Class-specific metrics like precision, recall, and F1-score show a substantial improvement by using synthetic images, emphasizing the data rebalancing effect of less frequent classes. However, overall performance does not improve for most models and configurations, except for a DreamBooth approach which shows a +0.52 improvement in overall accuracy. The large variance of performance impact in this study suggests a careful consideration of utilizing generative models for limited data scenarios, especially with an unexpected negative correlation between image quality and downstream classification improvement.

A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.

BACKGROUND: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV. OBJECTIVE: We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City. METHODS: CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. Variables in the EMR included clinical and laboratory data routinely collected in the inpatient setting. The final fusion model gave a prediction likelihood for the need of intubation within 24 hours as well. RESULTS: At a prediction probability threshold of 0.5, the fusion model provided 78.9% (95% CI 59%-96%) sensitivity, 83% (95% CI 76%-89%) specificity, 0.509 (95% CI 0.34-0.67) F1-score, 0.874 (95% CI 0.80-0.94) area under the receiver operating characteristic curve (AUROC), and 0.497 (95% CI 0.32-0.65) area under the precision recall curve (AUPRC) on the holdout set. Compared to the image classifier alone, which had an AUROC of 0.577 (95% CI 0.44-0.73) and an AUPRC of 0.206 (95% CI 0.08-0.38), the fusion model showed significant improvement (P<.001). The most important predictor variables were respiratory rate, C-reactive protein, oxygen saturation, and lactate dehydrogenase. The imaging probability score ranked 15th in overall feature importance. CONCLUSIONS: We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19.

Patch-wise 3D segmentation quality assessment combining reconstruction and regression networks.

Purpose: General deep-learning (DL)-based semantic segmentation methods with expert level accuracy may fail in 3D medical image segmentation due to complex tissue structures, lack of large datasets with ground truth, etc. For expeditious diagnosis, there is a compelling need to predict segmentation quality without ground truth. In some medical imaging applications, maintaining the quality of segmentation is crucial to the localized regions where disease is prevalent rather than just globally maintaining high-average segmentation quality. We propose a DL framework to identify regions of segmentation inaccuracies by combining a 3D generative adversarial network (GAN) and a convolutional regression network. Approach: Our approach is methodologically based on the learned ability to reconstruct the original images identifying the regions of location-specific segmentation failures, in which the reconstruction does not match the underlying original image. We use conditional GAN to reconstruct input images masked by the segmentation results. The regression network is trained to predict the patch-wise Dice similarity coefficient (DSC), conditioned on the segmentation results. The method relies directly on the extracted segmentation related features and does not need to use ground truth during the inference phase to identify erroneous regions in the computed segmentation. Results: We evaluated the proposed method on two public datasets: osteoarthritis initiative 4D (3D + time) knee MRI (knee-MR) and 3D non-small cell lung cancer CT (lung-CT). For the patch-wise DSC prediction, we observed the mean absolute errors of 0.01 and 0.04 with the independent standard for the knee-MR and lung-CT data, respectively. Conclusions: This method shows promising results in localizing the erroneous segmentation regions that may aid the downstream analysis of disease diagnosis and prognosis prediction.

Quantitative assessment of spasticity: a narrative review of novel approaches and technologies.

Spasticity is a complex neurological disorder, causing significant physical disabilities and affecting patients' independence and quality of daily lives. Current spasticity assessment methods are questioned for their non-standardized measurement protocols, limited reliabilities, and capabilities in distinguishing neuron or non-neuron factors in upper motor neuron lesion. A series of new approaches are developed for improving the effectiveness of current clinical used spasticity assessment methods with the developing technology in biosensors, robotics, medical imaging, biomechanics, telemedicine, and artificial intelligence. We investigated the reliabilities and effectiveness of current spasticity measures employed in clinical environments and the newly developed approaches, published from 2016 to date, which have the potential to be used in clinical environments. The new spasticity scales, taking advantage of quantified information such as torque, or echo intensity, the velocity-dependent feature and patients' self-reported information, grade spasticity semi-quantitatively, have competitive or better reliability than previous spasticity scales. Medical imaging technologies, including near-infrared spectroscopy, magnetic resonance imaging, ultrasound and thermography, can measure muscle hemodynamics and metabolism, muscle tissue properties, or temperature of tissue. Medical imaging-based methods are feasible to provide quantitative information in assessing and monitoring muscle spasticity. Portable devices, robotic based equipment or myotonometry, using information from angular, inertial, torque or surface EMG sensors, can quantify spasticity with the help of machine learning algorithms. However, spasticity measures using those devices are normally not physiological sound. Repetitive peripheral magnetic stimulation can assess patients with severe spasticity, which lost voluntary contractions. Neuromusculoskeletal modeling evaluates the neural and non-neural properties and may gain insights into the underlying pathology of spasticity muscles. Telemedicine technology enables outpatient spasticity assessment. The newly developed spasticity methods aim to standardize experimental protocols and outcome measures and enable quantified, accurate, and intelligent assessment. However, more work is needed to investigate and improve the effectiveness and accuracy of spasticity assessment.

Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons.

Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to quantify longitudinal representativeness of the demographic characteristics of the primary MIDRC dataset compared to the United States general population (US Census) and COVID-19 positive case counts from the Centers for Disease Control and Prevention (CDC). Approach: The Jensen-Shannon distance (JSD), a measure of similarity of two distributions, was used to longitudinally measure the representativeness of the distribution of (1) all unique patients in the MIDRC data to the 2020 US Census and (2) all unique COVID-19 positive patients in the MIDRC data to the case counts reported by the CDC. The distributions were evaluated in the demographic categories of age at index, sex, race, ethnicity, and the combination of race and ethnicity. Results: Representativeness of the MIDRC data by ethnicity and the combination of race and ethnicity was impacted by the percentage of CDC case counts for which this was not reported. The distributions by sex and race have retained their level of representativeness over time. Conclusion: The representativeness of the open medical imaging datasets in the curated public data commons at MIDRC has evolved over time as the number of contributing institutions and overall number of subjects have grown. The use of metrics, such as the JSD support measurement of representativeness, is one step needed for fair and generalizable AI algorithm development.

Assessment of image rejection in digital radiography.

X-ray imaging uses ionizing radiation to generate diagnostic images. However, unnecessary radiation exposure can pose potential risks, including an increased risk of malignancy. One factor contributing to unnecessary radiation exposure is the rejection and retaking of X-ray images, which can lead to higher patient and occupational radiation doses. This study aimed to assess digital radiography rejection rates, causes of recurrence, and the most commonly repeated types of examinations. A cross-sectional online-based survey was conducted in 2022, involving 62 randomly selected radiographers in the UAE. The survey was distributed to radiographers through the head of radiology departments in various hospitals. Hospitals agreed to participate in the survey without disclosing their name. The data collected was analyzed using Excel. The study showed that 71% of radiographers working in the UAE hold a bachelor's degree. The examinations most frequently repeated were related to anatomical areas, with the spine accounting for 37.7% and facial bone for 19.7% of cases. The factors influencing repetition were primarily related to positioning (48.4%) and artifacts (21%), with the motion being the main cause of artifacts, including voluntary and involuntary movements. This study concluded that the most prevalent cause of repeating and retaking images is positioning, followed by artifacts. Furthermore, night shifts and workload impact radiographer performance, increasing the likelihood of picture retakes. The average number of rejects and repeated images has been reduced as new generations and modern equipment have been introduced, which also helped decrease the numbers.

Assessment of coronary microcirculation alterations in a porcine model of no-reflow using ultrasound localization microscopy: a proof of concept study.

BACKGROUND: Coronary microvascular obstruction also known as no-reflow phenomenon is a major issue during myocardial infarction that bears important prognostic implications. Alterations of the microvascular network remains however challenging to assess as there is no imaging modality in the clinics that can image directly the coronary microvascular vessels. Ultrasound Localization Microscopy (ULM) imaging was recently introduced to map microvascular flows at high spatial resolution (∼10 µm). In this study, we developed an approach to image alterations of the microvascular coronary flow in ex vivo perfused swine hearts. METHODS: A porcine model of myocardial ischemia-reperfusion was used to obtain microvascular coronary alterations and no-reflow. Four female hearts with myocardial infarction in addition to 6 controls were explanted and placed immediately in a dedicated preservation and perfusion box manufactured for ultrasound imaging. Microbubbles (MB) were injected into the vasculature to perform Ultrasound Localization Microscopy (ULM) imaging and a linear ultrasound probe mounted on a motorized device was used to scan the heart on multiple slices. The coronary microvascular anatomy and flow velocity was reconstructed using dedicated ULM algorithms and analyzed quantitatively. FINDINGS: We were able to image the coronary microcirculation of ex vivo swine hearts at a resolution of tens of microns and measure flow velocities ranging from 10 mm/s in arterioles up to more than 200 mm/s in epicardial arteries. Under different aortic perfusion pressures, we measured in large arteries of a subset of control hearts an increase of flow velocity from 31 ± 11 mm/s at 87 mmHg to 47 ± 17 mm/s at 132 mmHg (N = 3 hearts, P < 0.05). This increase was compared with a control measurement with a flowmeter in the aorta. We also compared 6 control hearts to 4 hearts in which no-reflow was induced by the occlusion and reperfusion of a coronary artery. Using average MB velocity and average density of MB per unit of surface as two ULM quantitative markers of perfusion, we were able to detect areas of coronary no-reflow in good agreement with a control anatomical pathology analysis of the cardiac tissue. In the no-reflow zone, we measured an average perfusion of 204 ± 305 MB/mm2 compared to 3182 ± 1302 MB/mm2 in the surrounding re-perfused area. INTERPRETATION: We demonstrated this approach can directly image and quantify coronary microvascular obstruction and no-reflow on large mammal perfused hearts. This is a first step for noninvasive, quantitative and affordable assessment of the coronary microcirculation function and particularly coronary microvascular anatomy in the infarcted heart. This approach has the potential to be extended to other clinical situations characterized by microvascular dysfunction. FUNDING: This study was supported by the French National Research Agency (ANR) under ANR-21-CE19-0002 grant agreement.

Automated patellar height assessment on high-resolution radiographs with a novel deep learning-based approach.

BACKGROUND: Artificial intelligence and deep learning have shown promising results in medical imaging and interpreting radiographs. Moreover, medical community shows a gaining interest in automating routine diagnostics issues and orthopedic measurements. AIM: To verify the accuracy of automated patellar height assessment using deep learning-based bone segmentation and detection approach on high resolution radiographs. METHODS: 218 Lateral knee radiographs were included in the analysis. 82 radiographs were utilized for training and 10 other radiographs for validation of a U-Net neural network to achieve required Dice score. 92 other radiographs were used for automatic (U-Net) and manual measurements of the patellar height, quantified by Caton-Deschamps (CD) and Blackburne-Peel (BP) indexes. The detection of required bones regions on high-resolution images was done using a You Only Look Once (YOLO) neural network. The agreement between manual and automatic measurements was calculated using the interclass correlation coefficient (ICC) and the standard error for single measurement (SEM). To check U-Net's generalization the segmentation accuracy on the test set was also calculated. RESULTS: Proximal tibia and patella was segmented with accuracy 95.9% (Dice score) by U-Net neural network on lateral knee subimages automatically detected by the YOLO network (mean Average Precision mAP greater than 0.96). The mean values of CD and BP indexes calculated by orthopedic surgeons (R#1 and R#2) was 0.93 (± 0.19) and 0.89 (± 0.19) for CD and 0.80 (± 0.17) and 0.78 (± 0.17) for BP. Automatic measurements performed by our algorithm for CD and BP indexes were 0.92 (± 0.21) and 0.75 (± 0.19), respectively. Excellent agreement between the orthopedic surgeons' measurements and results of the algorithm has been achieved (ICC > 0.75, SEM < 0.014). CONCLUSION: Automatic patellar height assessment can be achieved on high-resolution radiographs with the required accuracy. Determining patellar end-points and the joint line-fitting to the proximal tibia joint surface allows for accurate CD and BP index calculations. The obtained results indicate that this approach can be valuable tool in a medical practice.

IONPs-Based Medical Imaging in Cancer Care: Moving Beyond Traditional Diagnosis and Therapeutic Assessment.

Cancer-related burden of morbidity and mortality is rapidly rising worldwide. Medical imaging plays an important role in every phase of cancer management, including diagnosis, staging, treatment planning and evaluation. Iron oxide nanoparticles (IONPs) could serve as contrast agents or labeling agents to enhance the identification and visualization of pathological tissues as well as target cells. Multimodal or multifunctional imaging can be easily acquired by modifying IONPs with other imaging agents or functional groups, allowing the accessibility of combined imaging techniques and providing more comprehensive information for cancer care. To date, IONPs-enhanced medical imaging has gained intensive application in early diagnosis, monitoring treatment as well as guiding radio-frequency ablation, sentinel lymph node dissection, radiotherapy and hyperthermia therapy. Besides, IONPs mediated imaging is also capable of promoting the development of anti-cancer nanomedicines through identifying patients potentially sensitive to nanotherapeutics. Based on versatile imaging modes and application fields, this review highlights and summarizes recent research advances of IONPs-based medical imaging in cancer management. Besides, currently existing challenges are also discussed to provide perspectives and advices for the future development of IONPs-based imaging in cancer management.

TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems.

Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems.Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain).Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron-electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement.Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.