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Background: The extended Thrombolysis in Cerebral Infarction (eTICI) score is used in digital subtraction angiography (DSA) to quantify reperfusion grade in patients with an ischemic stroke who undergo endovascular thrombectomy (EVT). A previously developed automatic TICI score (autoTICI), which quantifies the ratio of reperfused pixels after EVT, demonstrates good correlation with eTICI., Objective: To evaluate the autoTICI model in a large multicenter registry of patients with an ischemic stroke, investigate the association with visual eTICI, and compare prediction of functional outcome between autoTICI and eTICI., Methods: Patients in the MR CLEAN Registry with an internal carotid artery, M1, and M2 occlusion were selected if both anteroposterior and lateral views were present in pre- and post-EVT DSA scans. The autoTICI score was compared with eTICI in predicting favorable functional outcome (modified Rankin Scale score 0-2), using area under the receiver operating characteristics curve (AUC) with a multivariable logistic regression model including known prognostic characteristics., Results: In total 421 of 3637 patients were included. AutoTICI was significantly associated with eTICI non-linearly (below 70% cOR=2.3 (95% CI 2.1 to 2.5), above 70% cOR=1.6 (95% CI 1.6 to 1.7) per 10% increment). The AUC of the model predicting favorable functional outcome was similar for autoTICI and eTICI (0.86, 95% CI 0.82 to 0.92 vs 0.86, 95% CI 0.83 to 0.90, P=0.73) and was higher than for a model with prognostic patient characteristics alone (0.86 vs 0.84, P=0.01)., Conclusion: Automatic quantitative assessment of reperfusion after EVT is associated with eTICI, and prediction of functional outcome is similar to that with visual eTICI. Therefore, autoTICI could be used as an alternative or additional review for visual reperfusion assessment to facilitate reproducible and uniform reporting., Competing Interests: Competing interests: Erasmus MC received funds from Stryker® by DD, AvdL, and Bracco Imaging® by DD. Amsterdam UMC received funds from Stryker® for consultations by CM, YR and OB. MUMC received funds from Stryker® and Codman® for consultations by WZ., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both doctors and patients. Inspired by generative artificial intelligence techniques, this study proposes GenDSA, a large-scale pretrained multi-frame generative model-based real-time and low-dose DSA imaging system., Methods: GenDSA was developed to generate 1-, 2-, and 3-frame sequences following each real frame. A large-scale dataset comprising ∼3 million DSA images from 27,117 patients across 10 hospitals was constructed to pretrain, fine-tune, and validate GenDSA. Two other datasets from 25 hospitals were used for evaluation. Objective evaluations included SSIM and PSNR. Five interventional radiologists independently assessed the quality of the generated frames using the Likert scale and visual Turing test. Scoring consistency among the radiologists was measured using the Kendall coefficient of concordance (W). The Fleiss' kappa values were used for inter-rater agreement analysis for visual Turing tests., Findings: Using only one-third of the clinical radiation dose, videos generated by GenDSA were perfectly consistent with real videos. Objective evaluations demonstrated that GenDSA's performance (PSNR = 36.83, SSIM = 0.911, generation time = 0.07 s/frame) surpassed state-of-the-art algorithms. Subjective ratings and statistical results from five doctors indicated no significant difference between real and generated videos. Furthermore, the generated videos were comparable to real videos in overall quality (4.905 vs. 4.935) and lesion assessment (4.825 vs. 4.860)., Conclusions: With clear clinical and translational values, the developed GenDSA can significantly reduce radiation damage to both doctors and patients during DSA-guided procedures., Funding: This study was supported by the National Key R&D Program and the National Natural Science Foundation of China., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: (1) a model that generates synthetic structural errors, and (2) a label appearance simulation network that produces segmentations with synthetic errors that are similar in appearance to the real initial segmentations. Using these segmentations with synthetic errors and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net, four previous label refinement methods, and a U-Net trained with a loss tailored for tubular structures. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shuai Chen reports financial support was provided by Chinese Scholarship Council. Shuai Chen reports a relationship with Erasmus Medical Center that includes: employment. Antonio Garcia-Uceda reports a relationship with Erasmus Medical Center that includes: employment. Jiahang Su reports a relationship with Erasmus Medical Center that includes: employment. Gijs van Tulder reports a relationship with Radboud University that includes: employment. Lennard Wolf reports a relationship with Erasmus Medical Center that includes: employment. Theo van Walsum reports a relationship with Erasmus Medical Center that includes: employment. Marleen de Bruijne reports a relationship with Erasmus Medical Center that includes: employment., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

In 3D-analysis of the calcaneus, a consistent coordinate system aligned with the original anatomical directions is crucial for pre- and postoperative analysis. This importance stems from the calcaneus's key role in weight-bearing and biomechanical alignment. However, defining a reliable coordinate system based solely on fractured or surgically reconstructed calcanei presents significant challenges. Given its anatomical prominence and consistent orientation, the talus offers a potential solution to this challenge. Our work explores the feasibility of talus-derived coordinate systems for 3D-modeling of the calcaneus across its various conditions. Four methods were tested on nonfractured, fractured and surgically reconstructed calcanei, utilizing Principal Component Analysis, anatomical landmarks, bounding box, and an atlas-based approach. The methods were compared with a self-defined calcaneus reference coordinate system. Additionally, the impact of deviation of the coordinate system on morphological measurements was investigated. Among methods for constructing nonfractured calcanei coordinate systems, the atlas-based method displayed the lowest Root Mean Square value in comparison with the reference coordinate system. For morphological measures like Böhler's Angle and the Critical angle of Gissane, the atlas talus-based system closely aligned with ground truth, yielding differences of 0.6° and 1.2°, respectively, compared to larger deviations seen in other talus-based coordinate systems. In conclusion, all tested methods were feasible for creating a talus derived coordinate system. A talus derived coordinate system showed potential, offering benefits for morphological measurements and clinical scenarios involving fractured and surgically reconstructed calcanei. Further research is recommended to assess the impact of these coordinate systems on surgical planning and outcomes., (© 2024 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society.)

Deformable image registration is an essential component of medical image analysis and plays an irreplaceable role in clinical practice. In recent years, deep learning-based registration methods have demonstrated significant improvements in convenience, robustness and execution time compared to traditional algorithms. However, registering images with large displacements, such as those of the liver organ, remains underexplored and challenging. In this study, we present a novel convolutional neural network (CNN)-based unsupervised learning registration method, Cascaded Multi-scale Spatial-Channel Attention-guided Network (CMAN), which addresses the challenge of large deformation fields using a double coarse-to-fine registration approach. The main contributions of CMAN include: (i) local coarse-to-fine registration in the base network, which generates the displacement field for each resolution and progressively propagates these local deformations as auxiliary information for the final deformation field; (ii) global coarse-to-fine registration, which stacks multiple base networks for sequential warping, thereby incorporating richer multi-layer contextual details into the final deformation field; (iii) integration of the spatial-channel attention module in the decoder stage, which better highlights important features and improves the quality of feature maps. The proposed network was trained using two public datasets and evaluated on another public dataset as well as a private dataset across several experimental scenarios. We compared CMAN with four state-of-the-art CNN-based registration methods and two well-known traditional algorithms. The results show that the proposed double coarse-to-fine registration strategy outperforms other methods in most registration evaluation metrics. In conclusion, CMAN can effectively handle the large-deformation registration problem and show potential for application in clinical practice. The source code is made publicly available at https://github.com/LocPham263/CMAN.git., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery-vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts. Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA. To address these limitations, we propose to simultaneously leverage spatial vasculature and temporal cerebral flow characteristics to segment arteries and veins in DSA. The proposed network, coined CAVE, encodes a 2D+time DSA series using spatial modules, aggregates all the features using temporal modules, and decodes it into 2D segmentation maps. On a large multi-center clinical dataset, CAVE achieves a vessel segmentation Dice of 0.84 (±0.04) and an artery-vein segmentation Dice of 0.79 (±0.06). CAVE surpasses traditional Frangi-based k-means clustering (P < 0.001) and U-Net (P < 0.001) by a significant margin, demonstrating the advantages of harvesting spatio-temporal features. This study represents the first investigation into automatic artery-vein segmentation in DSA using deep learning. The code is publicly available at https://github.com/RuishengSu/CAVE&#95;DSA., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: - Danny Ruijters is an employee of Philips Healthcare. - Wiro J. Niessen is founder, scientific lead, and shareholder of Quantb B. - Aad van der Lugt has received research support from GE HealthCare, Siemens Healthineers, Philips Healthcare, Stryker, Penumbra, Cerenovus, Medtronic, and Thrombolytic Sciences inc (all paid to institution)., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: While current preoperative and postoperative assessment of the fractured and surgically reconstructed calcaneus relies on computed tomography (CT)-imaging, there are no established methods to quantify calcaneus morphology on CT-images. This study aims to develop a semi-automated method for morphological measurements of the calcaneus on three-dimensional (3D) models derived from CT-imaging., Methods: Using CT data, 3D models were created from healthy, fractured, and surgically reconstructed calcanei. Böhler's angle (BA) and Critical angle of Gissane (CAG) were measured on conventional lateral radiographs and corresponding 3D CT reconstructions using a novel point-based method with semi-automatic landmark placement by three observers. Intraobserver and interobserver reliability scores were calculated using intra-class correlation coefficient (ICC). In addition, consensus among observers was calculated for a maximal allowable discrepancy of 5 and 10 degrees for both methods., Results: Imaging data from 119 feet were obtained (40 healthy, 39 fractured, 40 reconstructed). Semi-automated measurements on 3D models of BA and CAG showed excellent reliability (ICC: 0.87-1.00). The manual measurements on conventional radiographs had a poor-to-excellent reliability (ICC: 0.22-0.96). In addition, the percentage of consensus among observers was much higher for the 3D method when compared to conventional two-dimensional (2D) measurements., Conclusions: The proposed method enables reliable and reproducible quantification of calcaneus morphology in 3D models of healthy, fractured and reconstructed calcanei., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-142/coif). A.M.W. reports that he received funding from Ostheosynthesis & Trauma Care Foundation for research about the calcaneus. The other authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Purpose: For tumor resection, surgeons need to localize the tumor. For this purpose, a magnetic seed can be inserted into the tumor by a radiologist and, during surgery, a magnetic detection probe informs the distance to the seed for localization. In this case, the surgeon still needs to mentally reconstruct the position of the tumor from the probe's information. The purpose of this study is to develop and assess a method for 3D localization and visualization of the seed, facilitating the localization of the tumor., Methods: We propose a method for 3D localization of the magnetic seed by extending the magnetic detection probe with a tracking-based localization. We attach a position sensor (QR-code or optical marker) to the probe in order to track its 3D pose (respectively, using a head-mounted display with a camera or optical tracker). Following an acquisition protocol, the 3D probe tip and seed position are subsequently obtained by solving a system of equations based on the distances and the 3D probe poses., Results: The method was evaluated with an optical tracking system. An experimental setup using QR-code tracking (resp. using an optical marker) achieves an average of 1.6 mm (resp. 0.8 mm) 3D distance between the localized seed and the ground truth. Using a breast phantom setup, the average 3D distance is 4.7 mm with a QR-code and 2.1 mm with an optical marker., Conclusion: Tracking the magnetic detection probe allows 3D localization of a magnetic seed, which opens doors for augmented reality target visualization during surgery. Such an approach should enhance the perception of the localized region of interest during the intervention, especially for breast tumor resection where magnetic seeds can already be used in the protocol., (© 2024. The Author(s).)

Purpose: Endovascular treatment (EVT) of acute ischemic stroke can be complicated by vessel perforation. We studied the incidence and determinants of vessel perforations. In addition, we studied the association of vessel perforations with functional outcome, and the association between location of perforation on digital subtraction angiography (DSA) and functional outcome, using a large EVT registry., Methods: We included all patients in the MR CLEAN Registry who underwent EVT. We used DSA to determine whether EVT was complicated by a vessel perforation. We analyzed the association with baseline clinical and interventional parameters using logistic regression models. Functional outcome was measured using the modified Rankin Scale at 90 days. The association between vessel perforation and angiographic imaging features and functional outcome was studied using ordinal logistic regression models adjusted for prognostic parameters. These associations were expressed as adjusted common odds ratios (acOR)., Results: Vessel perforation occurred in 74 (2.6%) of 2794 patients who underwent EVT. Female sex (aOR 2.0 (95% CI 1.2-3.2)) and distal occlusion locations (aOR 2.2  (95% CI 1.3-3.5)) were associated with increased risk of vessel perforation. Functional outcome was worse in patients with vessel perforation (acOR 0.38 (95% CI 0.23-0.63)) compared to patients without a vessel perforation. No significant association was found between location of perforation and functional outcome., Conclusion: The incidence of vessel perforation during EVT in this cohort was low, but has severe clinical consequences. Female patients and patients treated at distal occlusion locations are at higher risk., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: Imaging biomarkers, such as the collateral score as determined from Computed Tomography Angiography (CTA) images, play a role in treatment decision making for acute stroke patients. In this manuscript, we present an end-to-end learning approach for automatic determination of a collateral score from a CTA image. Our aim was to investigate whether such end-to-end learning approaches can be used for this classification task, and whether the resulting classification can be used in existing outcome prediction models., Methods: The method consists of a preprocessing step, where the CTA image is aligned to an atlas and divided in the two hemispheres: the affected side and the healthy side. Subsequently, a VoxResNet based convolutional neural network is used to extract features at various resolutions from the input images. This is done by using a Siamese model, such that the classification is driven by the comparison between the affected and healthy using a unique set of features for both hemispheres. After masking the resulting features for both sides with the vascular region and global average pooling (per hemisphere) and concatenation of the resulting features, a fully connected layer is used to determine the categorized collateral score., Experiments: Several experiments have been performed to optimize the model hyperparameters and training procedure, and to validate the final model performance. The hyperparameter optimization and subsequent model training was done using CTA images from the MR CLEAN Registry, a Dutch multi-center multi-vendor registry of acute stroke patients that underwent endovascular treatment. A separate set of images, from the MR CLEAN Trial, served as an external validation set, where collateral scoring was assessed and compared with both human observers and a recent more traditional model. In addition, the automated collateral scores have been used in an existing functional outcome prediction model that uses both imaging and non-imaging clinical parameters., Conclusion: The results show that end-to-end learning of collateral scoring in CTA images is feasible, and does perform similar to more traditional methods, and the performance also is within the inter-observer variation. Furthermore, the results demonstrate that the end-to-end classification results also can be used in an existing functional outcome prediction model., Competing Interests: VF was employed by Quantib BV. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) WZ and RB declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Fortunati, Su, Wolff, van Doormaal, Hofmeijer, Martens, Bokkers, van Zwam, van der Lugt and van Walsum.)

Purpose: Our aim is to automatically align digital subtraction angiography (DSA) series, recorded before and after endovascular thrombectomy. Such alignment may enable quantification of procedural success., Methods: Firstly, we examine the inherent limitations for image registration, caused by the projective characteristics of DSA imaging, in a representative set of image pairs from thrombectomy procedures. Secondly, we develop and assess various image registration methods (SIFT, ORB). We assess these methods using manually annotated point correspondences for thrombectomy image pairs., Results: Linear transformations that account for scale differences are effective in aligning DSA sequences. Two anatomical landmarks can be reliably identified for registration using a U-net. Point-based registration using SIFT and ORB proves to be most effective for DSA registration and are applicable to recordings for all patient sub-types. Image-based techniques are less effective and did not refine the results of the best point-based registration method., Conclusion: We developed and assessed an automated image registration approach for cerebral DSA sequences, recorded before and after endovascular thrombectomy. Accurate results were obtained for approximately 85% of our image pairs., (© 2023. The Author(s).)

This paper presents a novel image analysis strategy that increases the potential of macular Optical Coherence Tomography (OCT) by using speckle features as biomarkers in different stages of glaucoma. A large pool of features (480) were computed for a subset of macular OCT volumes of the Leuven eye study cohort. The dataset contained 258 subjects that were divided into four groups based on their glaucoma severity: Healthy (56), Mild (94), Moderate (48), and Severe (60). The OCT speckle features were categorized as statistical properties, statistical distributions, contrast, spatial gray-level dependence matrices, and frequency domain features. The averaged thicknesses of ten retinal layers were also collected. Kruskal-Wallis H test and multivariable regression models were used to infer the most significant features related to glaucoma severity classification and to the correlation with visual field mean deviation. Four features were selected as being the most relevant: the ganglion cell layer (GCL) and the inner plexiform layer (IPL) thicknesses, and two OCT speckle features, the data skewness computed on the retinal nerve fiber layer (RNFL) and the scale parameter (a) of the generalized gamma distribution fitted to the GCL data. Based on a significance level of 0.05, the regression models revealed that RNFL skewness exhibited the highest significance among the features considered for glaucoma severity staging (p-values of 8.6×10 -6 for the logistic model and 2.8×10 -7 for the linear model). Furthermore, it demonstrated a strong negative correlation with the visual field mean deviation (ρ=-0.64). The post hoc analysis revealed that, when distinguishing healthy controls from glaucoma subjects, GCL thickness is the most relevant feature (p-value of 8.7×10 -5 ). Conversely, when comparing the Mild versus Moderate stages of glaucoma, RNFL skewness emerged as the only feature exhibiting statistical significance (p-value = 0.001). This work shows that macular OCT speckle contains information that is currently not used in clinical practice, and not only complements structural measurements (thickness) but also has a potential for glaucoma staging., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: In complex double outlet right ventricle (DORV) patients, the optimal surgical approach may be difficult to assess based on conventional 2-dimensional (2D) ultrasound (US) and computed tomography (CT) imaging. The aim of this study is to assess the added value of 3-dimensional (3D) printed and 3D virtual reality (3D-VR) models of the heart used for surgical planning in DORV patients, supplementary to the gold standard 2D imaging modalities., Methods: Five patients with different DORV subtypes and high-quality CT scans were selected retrospectively. 3D prints and 3D-VR models were created. Twelve congenital cardiac surgeons and paediatric cardiologists, from 3 different hospitals, were shown 2D-CT first, after which they assessed the 3D print and 3D-VR models in random order. After each imaging method, a questionnaire was filled in on the visibility of essential structures and the surgical plan., Results: Spatial relationships were generally better visualized using 3D methods (3D printing/3D-VR) than in 2D. The feasibility of ventricular septum defect patch closure could be determined best using 3D-VR reconstructions (3D-VR 92%, 3D print 66% and US/CT 46%, P < 0.01). The percentage of proposed surgical plans corresponding to the performed surgical approach was 66% for plans based on US/CT, 78% for plans based on 3D printing and 80% for plans based on 3D-VR visualization., Conclusions: This study shows that both 3D printing and 3D-VR have additional value for cardiac surgeons and cardiologists over 2D imaging, because of better visualization of spatial relationships. As a result, the proposed surgical plans based on the 3D visualizations matched the actual performed surgery to a greater extent., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery.)

Purpose: The assessment of collateral status may depend on the timing of image acquisition. The purpose of this study is to investigate whether there are optimal time points in CT Perfusion (CTP) for collateral status assessment, and compare collaterals scores at these time points with collateral scores from multiphase CT angiography (mCTA)., Methods: Patients with an acute intracranial occlusion who underwent baseline non-contrast CT, mCTA and CT perfusion were selected. Collateral status was assessed using an automatically computed Collateral Ratio (CR) score in mCTA, and predefined time points in CTP acquisition. CRs extracted from CTP were correlated with CRs from mCTA. In addition, all CRs were related to baseline National Institutes of Health Stroke Scale (NIHSS) and Alberta Stoke Program Early CT Score (ASPECTS) with linear regression analysis to find the optimal CR., Results: In total 58 subjects (median age 74 years; interquartile range 61-83 years; 33 male) were included. When comparing the CRs from the CTP vs. mCTA acquisition, the strongest correlations were found between CR from baseline mCTA and the CR at the maximal intensity projection of time-resolved CTP (r = 0.81) and the CR at the peak of arterial enhancement point (r = 0.78). Baseline mCTA-derived CR had the highest correlation with ASPECTS (β = 0.36 (95%CI 0.11, 0.61)) and NIHSS (β = - 0.48 (95%CI - 0.72, - 0.16))., Conclusion: Collateral status assessment strongly depends on the timing of acquisition. Collateral scores obtained from mCTA imaging is close to the optimal collateral score obtained from CTP imaging., (© 2022. The Author(s).)

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and computed tomography perfusion (CTP) images. To this end, we regard the anterior vessel tree as a set of bifurcations and connected centerlines. The method consists of a proximal policy optimization (PPO) based deep reinforcement learning (DRL) approach for tracking centerlines, a convolutional neural network based bifurcation detector, and a breadth-first vessel tree construction approach taking the tracking and bifurcation detection results as input. We experimentally determine the added values of various components of the tracker. Both DRL vessel tracking and CNN bifurcation detection were assessed in a cross validation experiment using 115 subjects. The anterior vessel tree formation was evaluated on an independent test set of 25 subjects, and compared to interobserver variation on a small subset of images. The DRL tracking result achieves a median overlapping rate until the first error (1.8 mm off the reference standard) of 100, [46, 100] % on 8032 vessels over 115 subjects. The bifurcation detector reaches an average recall and precision of 76% and 87% respectively during the vessel tree formation process. The final vessel tree formation achieves a median recall of 68% and precision of 70%, which is in line with the interobserver agreement., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Wim van Zwam reports financial support was provided by Cerenovus. Wim van Zwam reports financial support was provided by Stryker Neurovascular. Aad van der Lugt reports financial support was provided by Siemens Healthineers. Aad van der Lugt reports financial support was provided by GE Healthcare. Aad van der Lugt reports financial support was provided by Philips Healthcare., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objectives: Increasing evidence suggests a role for epicardial fat in the development of coronary artery disease in the general population. Heart transplantation patients are at increased risk of developing a specific form of coronary artery disease, cardiac allograft vasculopathy (CAV), which has far-reaching consequences in terms of morbidity and mortality. Until now, the role of epicardial fat volume (EFV) in the development of CAV remains unknown. Hence, we investigated the relationship between EFV and CAV as well as the influence of donor/recipient sex on EFV., Methods: Adult heart transplant patients who underwent coronary computed tomography angiography (CCTA) for CAV screening who were four or more years post-HT were included. Using the CT examinations, we quantified the EFV and the degree of CAV. Ordinal and linear regression models were used to assess the association of EFV with CAV., Results: In total, 149 (median age 44.5 years, 36% women) patients were included. The median time between HT and the CT scan was 11.0 (7.3-16.1) years. CAV grade 0, 1, 2 and 3 were seen in 85 (57%), 32 (22%), 14 (9%), and 18 (12%) patients, respectively. The median EFV was 208.4 (128.9-276.0) mL. Larger EFV were related to higher degrees of CAV (median of 164.7 to 290.6 mL for CAV grade 0 and 3, respectively, OR 5.23 (2.47-11.06), p < 0.001). Male recipients had significantly more EFV than female recipients irrespective of the donor sex (232.7 mL vs. 147.2 mL respectively, p < 0.001). Determinants for EFV were recipient sex, number of rejections, donor age, time between HT and CT scan, recipient BMI, and diabetes mellitus., Conclusions: EFV was associated with higher degrees of CAV. The recipient sex influenced the EFV more than the donor sex., Key Points: • Patients after heart transplantation have a high amount of epicardial fat while larger amounts of epicardial fat are related to higher grades of cardiac allograft vasculopathy. • Determinants of higher epicardial fat volume included recipient sex, number of rejections, donor age, time between HT and CT scan, recipient BMI, and diabetes mellitus. • Longitudinal studies are needed to further disentangle the role of epicardial fat in the development and progression of cardiac allograft vasculopathy. Demonstration of four patients (from CAV grade 0 to CAV grade 3) in whom epicardial fat volume was determined. In red, the voxels identified as epicardial fat., (© 2022. The Author(s).)

Purpose: Outcome of endovascular treatment in acute ischemic stroke patients is depending on the collateral circulation maintaining blood flow to the ischemic territory. We evaluated the inter-rater reliability and accuracy of raters and an automated algorithm for assessing the collateral score (CS, range: 0-3) in acute ischemic stroke patients., Methods: Baseline CTA scans with an intracranial anterior occlusion from the MR CLEAN study (n=500) were used. For each core lab CS, ten CTA scans with sufficient quality were randomly selected. After a training session in collateral scoring, all selected CTA scans were individually evaluated for a visual CS by three groups: 7 radiologists, 13 junior and 9 senior radiology residents. Two additional radiologists scored CS to be used as reference, with a third providing a CS to produce a 2 out of 3 consensus CS in case of disagreement. An automated algorithm was also used to compute CS. Inter-rater agreement was reported with intraclass correlation coefficient (ICC). Accuracy of visual and automated CS were calculated., Results: 39 CTA scans were assessed (1 corrupt CTA-scan excluded). All groups showed a moderate ICC (0.689-0.780) in comparison to the reference standard. Overall human accuracy was 65± 7% and increased to 88± 5% for dichotomized CS (0-1, 2-3). Automated CS accuracy was 62%, and 90% for dichotomized CS. No significant difference in accuracy was found between groups with different levels of expertise., Conclusion: After training, inter-rater reliability in collateral scoring was not influenced by experience. Automated CS performs similar to residents and radiologists in determining a collateral score., (© 2022. The Author(s).)

Purpose: In minimally invasive spring-assisted craniectomy, surgeons plan the surgery by manually locating the cranial sutures. However, this approach is prone to error. Augmented reality (AR) could be used to visualize the cranial sutures and assist in the surgery planning. The purpose of our work is to develop an AR-based system to visualize cranial sutures, and to assess the accuracy and usability of using AR-based navigation for surgical guidance in minimally invasive spring-assisted craniectomy., Methods: An AR system was developed that consists of an electromagnetic tracking system linked with a Microsoft HoloLens. The system was used to conduct a study with two skull phantoms. For each phantom, five sutures were annotated and visualized on the skull surface. Twelve participants assessed the system. For each participant, model alignment using six anatomical landmarks was performed, followed by the participant delineation of the visualized sutures. At the end, the participants filled a system usability scale (SUS) questionnaire. For evaluation, an independent optical tracking system was used and the delineated sutures were digitized and compared to the CT-annotated sutures., Results: For a total of 120 delineated sutures, the distance of the annotated sutures to the planning reference was [Formula: see text] mm. The average delineation time per suture was [Formula: see text] s. For the system usability questionnaire, an average SUS score of 73 was obtained., Conclusion: The developed AR-system has good accuracy (average 2.4 mm distance) and could be used in the OR. The system can assist in the pre-planning of minimally invasive craniosynostosis surgeries to locate cranial sutures accurately instead of the traditional approach of manual palpation. Although the conducted phantom study was designed to closely reflect the clinical setup in the OR, further clinical validation of the developed system is needed and will be addressed in a future work., (© 2022. The Author(s).)

Objectives: Outcome of endovascular treatment in acute ischemic stroke patients depends on collateral circulation to provide blood supply to the ischemic territory. We evaluated the performance of a commercially available algorithm for assessing the collateral score (CS) in acute ischemic stroke patients., Methods: Retrospectively, baseline CTA scans (≤ 3-mm slice thickness) with an intracranial carotid artery (ICA), middle cerebral artery segment M1 or M2 occlusion, from the MR CLEAN Registry (n = 1627) were evaluated. All CTA scans were evaluated for visual CS (0-3) by eight expert radiologists (reference standard). A Web-based AI algorithm quantified the collateral circulation (0-100%) for correctly detected occlusion sides. Agreement between visual CS and categorized automated CS (0: 0%, 1: > 0- ≤ 50%, 2: > 50- < 100%, 3: 100%) was assessed. Area under the curve (AUC) values for classifying patients in having good (CS: 2-3) versus poor (CS: 0-1) collaterals and for predicting functional independence (90-day modified Rankin Scale 0-2) were computed. Influence of CTA acquisition timing after contrast material administration was reported., Results: In the analyzed scans (n = 1024), 59% agreement was found between visual CS and automated CS. An AUC of 0.87 (95% CI: 0.85-0.90) was found for discriminating good versus poor CS. Timing of CTA acquisition did not influence discriminatory performance. AUC for predicting functional independence was 0.66 (95% CI 0.62-0.69) for automated CS, similar to visual CS 0.64 (95% CI 0.61-0.68)., Conclusions: The automated CS performs similar to radiologists in determining a good versus poor collateral score and predicting functional independence in acute ischemic stroke patients with a large vessel occlusion., Key Points: • Software for automated quantification of intracerebral collateral circulation on computed tomography angiography performs similar to expert radiologists in determining a good versus poor collateral score. • Software for automated quantification of intracerebral collateral circulation on computed tomography angiography performs similar to expert radiologists in predicting functional independence in acute ischemic stroke patients with a large vessel occlusion. • The timing of computed tomography angiography acquisition after contrast material administration did not influence the performance of automated quantification of the collateral status., (© 2022. The Author(s).)

We present a compact multi-modal and multi-scale retinal imaging instrument with an angiographic functional extension for clinical use. The system integrates scanning laser ophthalmoscopy (SLO), optical coherence tomography (OCT) and OCT angiography (OCTA) imaging modalities and provides multi-scale fields of view. For high resolution, and high lateral resolution in particular, cellular imaging correction of aberrations by adaptive optics (AO) is employed. The entire instrument has a compact design and the scanning head is mounted on motorized translation stages that enable 3D self-alignment with respect to the subject's eye by tracking the pupil position. Retinal tracking, based on the information provided by SLO, is incorporated in the instrument to compensate for retinal motion during OCT imaging. The imaging capabilities of the multi-modal and multi-scale instrument were tested by imaging healthy volunteers and patients., (© 2022. The Author(s).)

The optic nerve head (ONH) represents the intraocular section of the optic nerve, which is prone to damage by intraocular pressure (IOP). The advent of optical coherence tomography (OCT) has enabled the evaluation of novel ONH parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane minimum-rim-width (BMO-MRW), these seem to be promising ONH parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these OCT derived biomarkers are mostly extracted through manual segmentation, which is time-consuming and prone to bias, thus limiting their usability in clinical practice. The automatic segmentation of ONH in OCT scans could further improve the current clinical management of glaucoma and other diseases. This review summarizes the current state-of-the-art in automatic segmentation of the ONH in OCT. PubMed and Scopus were used to perform a systematic review. Additional works from other databases (IEEE, Google Scholar and ARVO IOVS) were also included, resulting in a total of 29 reviewed studies. For each algorithm, the methods, the size and type of dataset used for validation, and the respective results were carefully analysed. The results show a lack of consensus regarding the definition of segmented regions, extracted parameters and validation approaches, highlighting the importance and need of standardized methodologies for ONH segmentation. Only with a concrete set of guidelines, these automatic segmentation algorithms will build trust in data-driven segmentation models and be able to enter clinical practice., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Intracranial vessel perforation is a peri-procedural complication during endovascular therapy (EVT). Prompt recognition is important as its occurrence is strongly associated with unfavorable treatment outcomes. However, perforations can be hard to detect because they are rare, can be subtle, and the interventionalist is working under time pressure and focused on treatment of vessel occlusions. Automatic detection holds potential to improve rapid identification of intracranial vessel perforation. In this work, we present the first study on automated perforation detection and localization on X-ray digital subtraction angiography (DSA) image series. We adapt several state-of-the-art single-frame detectors and further propose temporal modules to learn the progressive dynamics of contrast extravasation. Application-tailored loss function and post-processing techniques are designed. We train and validate various automated methods using two national multi-center datasets (i.e., MR CLEAN Registry and MR CLEAN-NoIV Trial), and one international multi-trial dataset (i.e., the HERMES collaboration). With ten-fold cross-validation, the proposed methods achieve an area under the curve (AUC) of the receiver operating characteristic of 0.93 in terms of series level perforation classification. Perforation localization precision and recall reach 0.83 and 0.70 respectively. Furthermore, we demonstrate that the proposed automatic solutions perform at similar level as an expert radiologist., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Charles B.L.M. Majoie received funds from TWIN Foundation (related to this project, paid to institution); and from CVON/Dutch Heart Foundation, Stryker, European Commission, Health Evaluation Netherlands (unrelated; all paid to institution). Charles Majoie is shareholder of Nico.lab, a company that focuses on the use of artificial intelligence for medical imaging analysis. Danny Ruijters is an employee of Philips Healthcare. Wiro J. Niessen is founder, scientific lead, and shareholder of Quantib BV., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases., (Copyright © 2021. Published by Elsevier Inc.)

Significance: Speckle has historically been considered a source of noise in coherent light imaging. However, a number of works in optical coherence tomography (OCT) imaging have shown that speckle patterns may contain relevant information regarding subresolution and structural properties of the tissues from which it is originated., Aim: The objective of this work is to provide a comprehensive overview of the methods developed for retrieving speckle information in biomedical OCT applications., Approach: PubMed and Scopus databases were used to perform a systematic review on studies published until December 9, 2021. From 146 screened studies, 40 were eligible for this review., Results: The studies were clustered according to the nature of their analysis, namely static or dynamic, and all features were described and analyzed. The results show that features retrieved from speckle can be used successfully in different applications, such as classification and segmentation. However, the results also show that speckle analysis is highly application-dependant, and the best approach varies between applications., Conclusions: Several of the reviewed analyses were only performed in a theoretical context or using phantoms, showing that signal-carrying speckle analysis in OCT imaging is still in its early stage, and further work is needed to validate its applicability and reproducibility in a clinical context.

Purpose: To present a novel methodical approach to compare visibility of percutaneous needles in ultrasound images., Methods: A motor-driven rotation platform was used to gradually change the needle angle while capturing image data. Data analysis was automated using block-matching-based registration, with a tracking and refinement step. Every 25 frames, a Hough transform was used to improve needle alignments after large rotations. The method was demonstrated by comparing three commercial needles (14G radiofrequency ablation, RFA; 18G Trocar; 22G Chiba) and six prototype needles with different sizes, materials, and surface conditions (polished, sand-blasted, and kerfed), within polyvinyl alcohol phantom tissue and ex vivo bovine liver models. For each needle and angle, a contrast-to-noise ratio (CNR) was determined to quantify visibility. CNR values are presented as a function of needle type and insertion angle. In addition, the normalized area under the (CNR-angle) curve was used as a summary metric to compare needles., Results: In phantom tissue, the first kerfed needle design had the largest normalized area of visibility and the polished 1 mm diameter stainless steel needle the smallest (0.704 ± 0.199 vs. 0.154 ± 0.027, p < 0.01). In the ex vivo model, the second kerfed needle design had the largest normalized area of visibility, and the sand-blasted stainless steel needle the smallest (0.470 ± 0.190 vs. 0.127 ± 0.047, p < 0.001). As expected, the analysis showed needle visibility peaks at orthogonal insertion angles. For acute or obtuse angles, needle visibility was similar or reduced. Overall, the variability in needle visibility was considerably higher in livers., Conclusion: The best overall visibility was found with kerfed needles and the commercial RFA needle. The presented methodical approach to quantify ultrasound visibility allows comparisons of (echogenic) needles, as well as other technological innovations aiming to improve ultrasound visibility of percutaneous needles, such as coatings, material treatments, and beam steering approaches., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Instrument alignment is a common task in various surgical interventions using navigation. The goal of the task is to position and orient an instrument as it has been planned preoperatively. To this end, surgeons rely on patient-specific data visualized on screens alongside preplanned trajectories. The purpose of this manuscript is to investigate the effect of instrument visualization/non visualization on alignment tasks, and to compare it with virtual extensions approach which augments the realistic representation of the instrument with simple 3D objects. 18 volunteers performed six alignment tasks under each of the following conditions: no visualization on the instrument; realistic visualization of the instrument; realistic visualization extended with virtual elements (Virtual extensions). The first condition represents an egocentric-based alignment while the two other conditions additionally make use of exocentric depth estimation to perform the alignment. The device used was a see-through device (Microsoft HoloLens 2). The positions of the head and the instrument were acquired during the experiment. Additionally, the users were asked to fill NASA-TLX and SUS forms for each condition. The results show that instrument visualization is essential for a good alignment using see-through devices. Moreover, virtual extensions helped achieve the best performance compared to the other conditions with medians of 2 mm and 2° positional and angular error respectively. Furthermore, the virtual extensions decreased the average head velocity while similarly reducing the frustration levels. Therefore, making use of virtual extensions could facilitate alignment tasks in augmented and virtual reality (AR/VR) environments, specifically in AR navigated surgical procedures when using optical see-through devices.

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.

This systematic review provides an overview of augmented reality (AR) and its benefits in craniomaxillofacial surgery in an attempt to answer the question: Is AR beneficial for craniomaxillofacial surgery? This review includes a description of the studies conducted, the systems used and their technical characteristics. The search was performed in four databases: PubMed, Cochrane Library, Embase, and Web of Science. All journal articles published during the past 11 years related to AR, mixed reality, craniomaxillofacial, and surgery were considered in this study. From a total of 7067 articles identified using AR- and surgery-related keywords, 39 articles were finally selected. Based on these articles, a classification of study types, surgery types, devices used, metrics reported, and benefits were collected. The findings of this review indicate that AR could provide various benefits, addressing the challenges of conventional navigation systems, such as hand-eye coordination and depth perception. However, three main concerns were raised while performing this study: (1) it is complicated to aggregate the metrics reported in the articles, (2) it is difficult to obtain statistical value from the current studies, and (3) user evaluation studies are lacking. This article concludes with recommendations for future studies by addressing the latter points., (Copyright © 2020 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.)

Objective: A practical screening tool for chronic mesenteric ischemia (CMI) could facilitate early recognition and reduce undertreatment and diagnostic delay. This study explored the ability to discriminate CMI from non-CMI patients with a mesenteric artery calcium score (MACS)., Methods: This retrospective study included CTAs of consecutive patients with suspected CMI in a tertiary referral center between April 2016 and October 2019. A custom-built software module, using the Agatston definition, was developed and used to calculate the MACS for the celiac artery (CA), superior mesenteric artery (SMA), and inferior mesenteric artery. Scoring was performed by two blinded observers. Interobserver agreement was determined using 39 CTAs scored independently by both observers. CMI was defined as sustained symptom improvement after treatment. Non-CMI patients were patients not diagnosed with CMI after a diagnostic workup and patients not responding to treatment., Results: The MACS was obtained in 184 patients, 49 CMI and 135 non-CMI. Interobserver agreement was excellent (intraclass correlation coefficient 0.910). The MACS of all mesenteric arteries was significantly higher in CMI patients than in non-CMI patients. ROC analysis of the combined MACS of CA + SMA showed an acceptable AUC (0.767), high sensitivity (87.8%), and high NPV (92.1%), when using a ≥ 29.7 CA + SMA MACS cutoff. Comparison of two CTAs, obtained in the same patient at different points in time with different scan and reconstruction parameters, was performed in 29 patients and revealed significant differences in MACSs., Conclusion: MACS seems a promising screening method for CMI, but correction for scan and reconstruction parameters is warranted., Key Points: • A mesenteric artery calcium score obtained in celiac artery and superior mesenteric artery has a high negative predictive value for chronic mesenteric ischemia and could serve as a screening tool. • Interobserver agreement of the mesenteric artery calcium score is excellent. • Scan and reconstruction parameters influence the mesenteric artery calcium score and warrant the development of a method to correct for these parameters.

Background: The mesenteric artery calcium score (MACS) identifies patients with possible chronic mesenteric ischaemia (CMI) using standard computed tomography (CT) imaging. The MACS does not necessitate a dedicated computed tomography angiography (CTA) which is required for evaluation of mesenteric artery patency. This study aimed to test the use of a symptom and MACS based score chart to facilitate the selection of patients with a low probability of CMI, in whom further diagnostic workup can be omitted, and to validate the CTA-based score chart proposed by van Dijk et al. which guides treatment decisions in patients with suspected CMI., Methods: This retrospective study included consecutive patients with suspected CMI. The Agatston definition was used to calculate the MACS. Multivariable logistic regression analysis was used to create a MACS score chart, which was applied in all patients to determine its discriminative ability. The score chart by van Dijk et al. was validated in this independent external patient series., Results: Hundred-ninety-two patients were included, of whom 49 had CMI. The MACS score chart composed of the variables weight loss, postprandial abdominal pain, history of cardiovascular disease, and MACS, showed an excellent discriminative ability (area under the curve [AUC] 0.87). CMI risks were 2.1% in the low-risk group (0-4 points) and 39.1% in the increased risk group (5-10 points); sensitivity (97.8%) and negative predictive value (NPV; 97.9%) were high. The CTA-based score chart by van Dijk et al. showed an excellent discriminative ability (AUC 0.89)., Conclusion: The MACS score chart shows promise for early risk stratification of patients with suspected CMI based on a near-perfect NPV. It is complementary to the CTA-based score chart by van Dijk et al., which showed excellent external validity and is well suited to guide subsequent (invasive) treatment decisions in patients with suspected CMI., (© 2021 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.)

Purpose: Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter., Methods: The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on three-dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio ( PSNR ), structural similarity ( SSIM ), and compression ratio ( CR ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images., Results: The results show that the method can significantly improve CR of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average CR of 6.45, which is 36% higher than that of the original HEVC and outperforms other state-of-the-art lossless medical image compression methods. The means of PSNR and SSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation., Conclusions: We thus conclude that the method has a high potential to be applied in teleintervention applications., (© 2021 American Association of Physicists in Medicine.)

Purpose: In ASPECTS, 10 brain regions are scored visually for presence of acute ischemic stroke damage. We evaluated automated ASPECTS in comparison to expert readers., Methods: Consecutive, baseline non-contrast CT-scans (5-mm slice thickness) from the prospective MR CLEAN trial (n = 459, MR CLEAN Netherlands Trial Registry number: NTR1804) were evaluated. A two-observer consensus for ASPECTS regions (normal/abnormal) was used as reference standard for training and testing (0.2/0.8 division). Two other observers provided individual ASPECTS-region scores. The Automated ASPECTS software was applied. A region score specificity of ≥ 90% was used to determine the software threshold for detection of an affected region based on relative density difference between affected and contralateral region. Sensitivity, specificity, and receiver-operating characteristic curves were calculated. Additionally, we assessed intraclass correlation coefficients (ICCs) for automated ASPECTS and observers in comparison to the reference standard in the test set., Results: In the training set (n = 104), with software thresholds for a specificity of ≥ 90%, we found a sensitivity of 33-49% and an area under the curve (AUC) of 0.741-0.785 for detection of an affected ASPECTS region. In the test set (n = 355), the results for the found software thresholds were 89-89% (specificity), 41-57% (sensitivity), and 0.750-0.795 (AUC). Comparison of automated ASPECTS with the reference standard resulted in an ICC of 0.526. Comparison of observers with the reference standard resulted in an ICC of 0.383-0.464., Conclusion: The performance of automated ASPECTS is comparable to expert readers and could support readers in the detection of early ischemic changes.

The lamina cribrosa (LC) is an active structure that responds to the strain by changing its morphology. Abnormal changes in LC morphology are usually associated with, and indicative of, certain pathologies such as glaucoma, intraocular hypertension, and myopia. Recent developments in optical coherence tomography (OCT) have enabled detailed in vivo studies about the architectural characteristics of the LC. Structural characteristics of the LC have been widely explored in glaucoma management. However, information about which LC biomarkers could be useful for the diagnosis, and follow-up, of other diseases besides glaucoma is scarce. Hence, this literature review aims to summarize the role of the LC in nonophthalmic and ophthalmic diseases other than glaucoma. PubMed was used to perform a systematic review on the LC features that can be extracted from OCT images. All imaging features are presented and discussed in terms of their importance and applicability in clinical practice. A total of 56 studies were included in this review. Overall, LC depth (LCD) and thickness (LCT) have been the most studied features, appearing in 75% and 45% of the included studies, respectively. These biomarkers were followed by the prelaminar tissue thickness (21%), LC curvature index (5.4%), LC global shape index (3.6%), LC defects (3.6%), and LC strains/deformations (1.8%). Overall, the disease groups showed a thinner LC (smaller LCT) and a deeper ONH cup (larger LCD), with some exceptions. A large variability between approaches used to compute LC biomarkers has been observed, highlighting the importance of having automated and standardized methodologies in LC analysis. Moreover, further studies are needed to identify the pathologies where LC features have a diagnostic and/or prognostic value., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Alice Paulo et al.)

Purpose: To develop and assess an automatic procedure for classifying and staging glaucomatous vascular damage based on optical coherence tomography angiography (OCTA) imaging., Methods: OCTA scans (Zeiss Cirrus 5000 HD-OCT) from a random eye of 39 healthy subjects and 82 glaucoma patients were used to develop a new classification algorithm based on multilayer and multisector information. The averaged circumpapillary retinal nerve fiber layer (RNFL) thickness was also collected. Three models, support vector machine (SVM), random forest (RF), and gradient boosting (xGB), were developed and optimized for classifying between healthy and glaucoma patients, primary open-angle glaucoma (POAG) and normal-tension glaucoma (NTG), and glaucoma severity groups., Results: All the models, the SVM (area under the receiver operating characteristic [AUROC] 0.89 ± 0.06), the RF (AUROC 0.86 ± 0.06), and the xGB (AUROC 0.85 ± 0.07), with 26, 22, and 29 vascular features obtained after feature selection, respectively, presented a similar performance to the RNFL thickness (AUROC 0.85 ± 0.06) in classifying healthy and glaucoma patients. The superficial vascular plexus was the most informative layer with the infero temporal sector as the most discriminative region of interest. No significant differentiation was obtained in discriminating the POAG from the NTG group. The xGB model, after feature selection, presented the best performance in classifying the severity groups (AUROC 0.76 ± 0.06), outperforming the RNFL (AUROC 0.67 ± 0.06)., Conclusions: OCTA multilayer and multisector information has similar performance to RNFL for glaucoma diagnosis, but it has an added value for glaucoma severity classification, showing promising results for staging glaucoma progression., Translational Relevance: OCTA, in its current stage, has the potential to be used in clinical practice as a complementary imaging technique in glaucoma management., Competing Interests: Disclosure: D. Andrade De Jesus, None; L.S. Brea, None; J.B. Breda, None; E. Fokkinga, None; V. Ederveen, None; N. Borren, None; A. Bekkers, None; M. Pircher, None; I. Stalmans, None; S. Klein, None; T. van Walsum, None, (Copyright 2020 The Authors.)

A growing number of studies have reported a link between vascular damage and glaucoma based on optical coherence tomography angiography (OCTA) imaging. This multitude of studies focused on different regions of interest (ROIs) which offers the possibility to draw conclusions on the most discriminative locations to diagnose glaucoma. The objective of this work was to review and analyse the discriminative capacity of vascular density, retrieved from different ROIs, on differentiating healthy subjects from glaucoma patients. PubMed was used to perform a systematic review on the analysis of glaucomatous vascular damage using OCTA. All studies up to 21 April 2019 were considered. The ROIs were analysed by region (macula, optic disc and peripapillary region), layer (superficial and deep capillary plexus, avascular, whole retina, choriocapillaris and choroid) and sector (according to the Garway-Heath map). The area under receiver operator characteristic curve (AUROC) and the statistical difference (p-value) were used to report the importance of each ROI for diagnosing glaucoma. From 96 screened studies, 43 were eligible for this review. Overall, the peripapillary region showed to be the most discriminative region with the highest mean AUROC (0.80 ± 0.09). An improvement of the AUROC from this region is observed when a sectorial analysis is performed, with the highest AUROCs obtained at the inferior and superior sectors of the superficial capillary plexus in the peripapillary region (0.86 ± 0.03 and 0.87 ± 0.10, respectively). The presented work shows that glaucomatous vascular damage can be assessed using OCTA, and its added value as a complementary feature for glaucoma diagnosis depends on the region of interest. A sectorial analysis of the superficial layer at the peripapillary region is preferable for assessing glaucomatous vascular damage., (© 2020 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation.)

The collateral score is an important biomarker in decision making for endovascular treatment (EVT) of patients with ischemic stroke. The existing collateral grading systems are based on visual inspection and prone to subjective interpretation and interobserver variation. The purpose of our work is the development of an automatic collateral scoring method. In this work, we present a method that is inspired by human collateral scoring. Firstly, we define an anatomical region by atlas-based registration and extract vessel structures using a deep convolutional neural network. From this, high-level features based on the ratios of vessel length and volume of the occluded and the contralateral side are defined. Multi-class classification models are used to map the feature space to a four-grade collateral score and a quantitative score. The dataset used for training, validation and testing is from a registry of images acquired in clinical routine at multiple medical centers. The model performance is tested on 269 subjects, achieving an accuracy of 0.8. The dichotomized collateral score accuracy is 0.9. The error is comparable to the interobserver variation, the results are comparable to the performance of two radiologists with 10 to 30 years of experience.

In this work we present a fast approach to perform registration of computed tomography to ultrasound volumes for image guided intervention applications. The method is based on a combination of block-matching and outlier rejection. The block-matching uses a correlation based multimodal similarity metric, where the intensity and the gradient of the computed tomography images along with the ultrasound volumes are the input images to find correspondences between blocks in the computed tomography and the ultrasound volumes. A variance and octree based feature point-set selection method is used for selecting distinct and evenly spread point locations for block-matching. Geometric consistency and smoothness criteria are imposed in an outlier rejection step to refine the block-matching results. The block-matching results after outlier rejection are used to determine the affine transformation between the computed tomography and the ultrasound volumes. Various experiments are carried out to assess the optimal performance and the influence of parameters on accuracy and computational time of the registration. A leave-one-patient-out cross-validation registration error of 3.6 mm is achieved over 29 datasets, acquired from 17 patients., (Copyright © 2019 Elsevier B.V. All rights reserved.)

2D ultrasound (US) image guidance is used in minimally invasive procedures in the liver to visualize the target and the needle. Needle insertion using 2D ultrasound keeping the transducer position to view needle and reach target is challenging. Dedicated needle holders attached to the US transducer help to target in plane and at a specific angle. A drawback of this is that, the probe is fixed to the needle and cannot be rotated to assess the position of the needle in a perpendicular plane. In this study, we propose an automatic needle detection and tracking method using 3D US imaging to improve image guidance and visualization of the target in the liver with respect to the needle during these interventional procedures. The method utilizes a convolutional neural network for detection of the needle in 3D US images. In a subsequent step, the output of the convolutional neural network is used to detect needle candidates, which are fed into a final tracking step to determine the real needle position. The needle position is used to present two perpendicular cross-sectional planes of the 3D US image containing the needle in both directions. Performance of the method was evaluated in phantoms and in-vivo data by calculating the needle position distance and needle orientation angle between segmented needles and reference ground truth needles, which were manually annotated by an observer. The method successfully detects the needle position and orientation with mean errors of 1 mm and 2°, respectively. The proposed method yields a robust automatic needle detection and visualization at a frame rate of 3 Hz in 3D ultrasound imaging of the liver., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Cardiac computed tomography (CT) is a valuable tool for functional mechanical heart valve (MHV) assessment. An important aspect of bileaflet MHV assessment is evaluation and measurement of leaflet opening and closing angles. Performed manually, however, it is a laborious and time consuming task. In this paper, we propose an automated approach for bileaflet MHV leaflet angle computation. This method consists of four steps. After a one click selection of the MHV region on an axial image, an automatic MHV extraction using thresholding, and a connected component analysis based on voxel intensities is performed. Then, the MHV component (valve ring and two leaflets) positions are identified using random sample consensus and least square fitting. Finally, the angles are automatically computed based on the orientation of the components in each timeframe. Five multiphase CT scans from patients with a bileaflet MHV containing between 14 and 17 timepoints were used for development and another 15 were used for evaluation. The detected MHV components were scored for their overlap with real components as successful or unsuccessful. For successful results, the angles were compared to those measured by a radiologist. Qualitatively evaluated on a data set of 222 images, a total of 398 out of 444 angle computations (89.6%) were rated as successful. Compared to the angles measured by the radiologist, the successful angles showed a mean difference of 0.54° ± 3.63° from the manual calculations. The method provides a high success rate and an accurate computation of leaflet opening angles compared to manual measurements.

Purpose: To investigate the effect of patient specific vessel cooling on head and neck hyperthermia treatment planning (HTP). Methods and materials: Twelve patients undergoing radiotherapy were scanned using computed tomography (CT), magnetic resonance imaging (MRI) and contrast enhanced MR angiography (CEMRA). 3D patient models were constructed using the CT and MRI data. The arterial vessel tree was constructed from the MRA images using the 'graph-cut' method, combining information from Frangi vesselness filtering and region growing, and the results were validated against manually placed markers in/outside the vessels. Patient specific HTP was performed and the change in thermal distribution prediction caused by arterial cooling was evaluated by adding discrete vasculature (DIVA) modeling to the Pennes bioheat equation (PBHE). Results: Inclusion of arterial cooling showed a relevant impact, i.e., DIVA modeling predicts a decreased treatment quality by on average 0.19 °C ( T 90), 0.32 °C ( T 50) and 0.35 °C ( T 20) that is robust against variations in the inflow blood rate (|Δ T | < 0.01 °C). In three cases, where the major vessels transverse target volume, notable drops (|Δ T | > 0.5 °C) were observed. Conclusion: Addition of patient-specific DIVA into the thermal modeling can significantly change predicted treatment quality. In cases where clinically detectable vessels pass the heated region, we advise to perform DIVA modeling.

Objectives: To compare the accuracy of liver tumour localisation in intraprocedural computed tomography (CT) images of computer-based rigid registration or non-rigid registration versus mental registration performed by interventional radiologists., Methods: Retrospectively (2009-2017), 35 contrast-enhanced CT (CECT) images incorporating 56 tumours, acquired during CT-guided ablation procedures and their corresponding pre-procedural diagnostic CECTs were retrieved from the picture archiving and communication system (PACS). The original intraprocedural CECTs were de-enhanced to create a virtually unenhanced CT image (VUCT). Alignment of diagnostic CECTs to their corresponding intraprocedural VUCTs was performed with non-rigid or rigid registration. Mental registration was performed by four interventional radiologists. The original intraprocedural CECT served as the reference standard. Accuracy of tumour localisation was assessed with the target registration error (TRE). Statistical differences were analysed with the Wilcoxon signed-rank test., Results: Non-rigid registration failed to register two CT datasets, incorporating four tumours. In the remaining 33 datasets, non-rigid, rigid and mental registration showed a median TRE of 3.9 mm, 9.0 mm and 10.9 mm, respectively. Non-rigid registration was significantly more accurate in tumour centre localisation in comparison to rigid (p < 0.001) or mental registration (p < 0.001). Rigid registration was not statistically different from mental registration (p = 0.169). Non-rigid registration was most accurate in localising tumour centres in 42 out of 52 tumours (80.8%), while rigid and mental registration were most accurate in only seven (13.5%) and three (5.8%) tumours, respectively., Conclusions: Computer-based non-rigid registration is statistically significantly more accurate in localising liver tumours in intraprocedural unenhanced CT images in comparison to rigid registration or interventional radiologists' mental mapping abilities., Key Points: • Computer-based non-rigid registration is better (p < 0.001) in localising target tumours prior to ablation in intraprocedural CT images in comparison to rigid registration or interventional radiologists' mental mapping abilities. • Human experts perform sub-optimal localisation of target tumours when relying solely on mental mapping during challenging CT-guided procedures. • This non-rigid registration method shows promising results as a safe alternative to intravenous contrast media in liver tumour localisation prior to ablation during CT-guided procedures.

Purpose: Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle., Methods: We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins., Results: Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%., Conclusions: Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety., (© 2018 American Association of Physicists in Medicine.)

Multimodal image fusion for image guidance in minimally invasive liver interventions generally requires the registration of pre-operatively acquired images with interventional images of the patient. Whereas rigid registration approaches are fast and can be used in an interventional setting, the actual liver deformation may be nonrigid. The purpose of this paper is to assess the magnitude of nonrigid deformation of the liver between pre-operative and interventional CT images in the case of tumor ablations, over the full liver and over parts of the liver that match the volumes typically imaged by a 3D ultrasound transducer. We acquired 3D abdominal CT scans of 38 patients that had undergone the radiofrequency ablation of liver tumors, pre-operative CT images as well as intraoperative CT images. To determine the magnitude of liver deformation due to pose changes and respiration, we nonrigidly registered the pre-operative CT scan with the intraoperative CT scan. By fitting the deformation to a rigid transformation in the region of interest and computing the residual displacements, the nonrigid deformation part can be quantified. We performed quantifications over the complete liver, as well as for two volumes of interest representative of sub-xiphoidal and intercostal 3D ultrasound acquisitions. The results showed that a substantial amount of nonrigid deformation was found, and rotation of the patient's pose and deep inhalation caused significant liver deformation. Hence we concluded that nonrigid motion correction in the interventions should be taken into account.

Blood flow patterns in the human left ventricle (LV) have shown relation to cardiac health. However, most studies in the literature are limited to a few patients and results are hard to generalize. This study aims to provide a new framework to generate more generalized insights into LV blood flow as a function of changes in anatomy and wall motion. In this framework, we studied the four-dimensional blood flow in LV via computational fluid dynamics (CFD) in conjunction with a statistical shape model (SSM), built from segmented LV shapes of 150 subjects. We validated results in an in-vitro dynamic phantom via time-resolved optical particle image velocimetry (PIV) measurements. This combination of CFD and the SSM may be useful for systematically assessing blood flow patterns in the LV as a function of varying anatomy and has the potential to provide valuable data for diagnosis of LV functionality., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Aim: Needle visibility is crucial for effective and safe ultrasound-guided interventional procedures. Several studies have investigated needle visibility in 2D ultrasound imaging, but less information is available for 3D ultrasound imaging, a modality that has great potential for image guidance interventions. We performed a prospective study, to quantitatively compare the echogenicity of various commercially available needles in 3D ultrasound images used in clinical practice under freehand needle introduction., Materials and Methods: A set of seven needles, containing biopsy needles, a TIPS needle, an ablation needle and a puncture needle, were included in the study. A liver-mimicking phantom and cow liver were punctured by each needle. 3D sweeps and real-time 3D data were acquired at three different angles (20°, 55° and 90°). Needle visibility was quantified by calculating contrast-to-noise ratio., Results: In the liver-mimicking phantom, all needles showed better visibility than in the cow liver. At large angles, contrast-to-noise ratio and needle visibility were almost similar in both cases, but at lower angles differences in visibility were observed with different types of needles., Conclusion: The contrast-to-noise ratio increased with the increase in angle of insonation. The difference in visibility of different needles is more pronounced at 20° angle. The echogenic properties of inhomogeneous cow liver tissues make the needles visibility worse as compared to a homogenous phantom. The needle visibility becomes worse in 3D real-time data as compared to 3D ultrasound sweeps.

Purpose: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena., Methods: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic., Results: The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were [Formula: see text] for the intima-media interface, [Formula: see text] for the media-adventitia interface, and [Formula: see text] for the adventitia-periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78-0.98)., Conclusion: The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.

Localization of the correct vertebral level for surgical entry during lumbar hernia surgery is not straightforward. In this paper, we develop and evaluate a solution using free-hand 2-D ultrasound (US) imaging in the operation room (OR). Our system exploits the difference in spinous process shapes of the vertebrae. The spinous processes are pre-operatively outlined and labeled in a lateral lumbar X-ray of the patient. Then, in the OR the spinous processes are imaged with 2-D sagittal US, and are automatically segmented and registered with the X-ray shapes. After a small number of scanned vertebrae, the system robustly matches the shapes, and propagates the X-ray label to the US images. The main contributions of our work are: we propose a deep convolutional neural network-based bone segmentation algorithm from US imaging that outperforms state of the art methods in both performance and speed. We present a matching strategy that determines the levels of the spinal processes being imaged. And lastly, we evaluate the complete procedure on 19 clinical data sets from two hospitals, and two observers. The final labeling was correct in 92% of the cases, demonstrating the feasibility of US-based surgical entry point detection for spinal surgeries.

Background: The purpose of our work is to quantitatively assess clinically relevant geometric properties of proximal middle cerebral arteries (pMCA), to investigate the degree of their lateral symmetry, and to evaluate whether the pMCA can be modeled by using state-of-the-art deformable image registration of the ipsi- and contralateral hemispheres., Methods: Individual pMCA segments were identified, quantified, and statistically evaluated on a set of 55 publicly available magnetic resonance angiography time-of-flight images. Rigid and deformable image registrations were used for geometric alignment of the ipsi- and contralateral hemispheres. Lateral symmetry of relevant geometric properties was evaluated before and after the image registration., Results: No significant lateral differences regarding tortuosity and diameters of contralateral M1 segments of pMCA were identified. Regarding the length of M1 segment, 44% of all subjects could be considered laterally symmetrical. Dominant M2 segment was identified in 30% of men and 9% of women in both brain hemispheres. Deformable image registration performed significantly better (P < .01) than rigid registration with regard to distances between the ipsi- and the contralateral centerlines of M1 segments (1.5 ± 1.1 mm versus 2.8 ± 1.2 mm respectively) and between the M1 and the anterior cerebral artery (ACA) branching points (1.6 ± 1.4 mm after deformable registration)., Conclusions: Although natural lateral variation of the length of M1 may not allow for sufficient modeling of the complete pMCA, deformable image registration of the contralateral brain hemisphere to the ipsilateral hemisphere is feasible for localization of ACA-M1 branching point and for modeling 71 ± 23% of M1 segment., (Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.)