Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks

Carotid Vessel Wall Segmentation Through Domain Aligner, Topological Learning, and Segment Anything Model for Sparse Annotation in MR Images

Medical image analysis poses significant challenges due to limited availability of clinical data, which is crucial for training accurate models. This limitation is further compounded by the specialized and labor-intensive nature of the data annotation process. For example, despite the popularity of computed tomography angiography (CTA) in diagnosing atherosclerosis with an abundance of annotated datasets, magnetic resonance (MR) images stand out with better visualization for soft plaque and vessel wall characterization. However, the higher cost and limited accessibility of MR, as well as time-consuming nature of manual labeling, contribute to fewer annotated datasets. To address these issues, we formulate a multi-modal transfer learning network, named MT-Net, designed to learn from unpaired CTA and sparsely-annotated MR data. Additionally, we harness the Segment Anything Model (SAM) to synthesize additional MR annotations, enriching the training process. Specifically, our method first segments vessel lumen regions followed by precise characterization of carotid artery vessel walls, thereby ensuring both segmentation accuracy and clinical relevance. Validation of our method involved rigorous experimentation on publicly available datasets from COSMOS and CARE-II challenge, demonstrating its superior performance compared to existing state-of-the-art techniques.

Self-supervised learning for accurately modelling hierarchical evolutionary patterns of cerebrovasculature

Cerebrovascular abnormalities are critical indicators of stroke and neurodegenerative diseases like Alzheimer's disease (AD). Understanding the normal evolution of brain vessels is essential for detecting early deviations and enabling timely interventions. Here, for the first time, we proposed a pipeline exploring the joint evolution of cortical volumes (CVs) and arterial volumes (AVs) in a large cohort of 2841 individuals. Using advanced deep learning for vessel segmentation, we built normative models of CVs and AVs across spatially hierarchical brain regions. We found that while AVs generally decline with age, distinct trends appear in regions like the circle of Willis. Comparing healthy individuals with those affected by AD or stroke, we identified significant reductions in both CVs and AVs, wherein patients with AD showing the most severe impact. Our findings reveal gender-specific effects and provide critical insights into how these conditions alter brain structure, potentially guiding future clinical assessments and interventions.

Efficient and Accurate 3D Thickness Measurement in Vessel Wall Imaging: Overcoming Limitations of 2D Approaches Using the Laplacian Method

The clinical significance of measuring vessel wall thickness is widely acknowledged. Recent advancements have enabled high-resolution 3D scans of arteries and precise segmentation of their lumens and outer walls; however, most existing methods for assessing vessel wall thickness are 2D. Despite being valuable, reproducibility and accuracy of 2D techniques depend on the extracted 2D slices. Additionally, these methods fail to fully account for variations in wall thickness in all dimensions. Furthermore, most existing approaches are difficult to be extended into 3D and their measurements lack spatial localization and are primarily confined to lumen boundaries. We advocate for a shift in perspective towards recognizing vessel wall thickness measurement as inherently a 3D challenge and propose adapting the Laplacian method as an outstanding alternative. The Laplacian method is implemented using convolutions, ensuring its efficient and rapid execution on deep learning platforms. Experiments using digital phantoms and vessel wall imaging data are conducted to showcase the accuracy, reproducibility, and localization capabilities of the proposed approach. The proposed method produce consistent outcomes that remain independent of centerlines and 2D slices. Notably, this approach is applicable in both 2D and 3D scenarios. It allows for voxel-wise quantification of wall thickness, enabling precise identification of wall volumes exhibiting abnormal wall thickness. Our research highlights the urgency of transitioning to 3D methodologies for vessel wall thickness measurement. Such a transition not only acknowledges the intricate spatial variations of vessel walls, but also opens doors to more accurate, localized, and insightful diagnostic insights.

A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges. Firstly, to streamline the annotation process, we employ a semi-automatic segmentation approach using bounding boxes as masks, which is less time-consuming than pixel-level delineation. Subsequently, recursive self-training is utilized to enhance annotation quality. Finally, a 2.5D segmentation technique is adopted, wherein a slice of a volumetric image is segmented using a pseudo-RGB image. The pipeline was applied to segment the carotid artery tree in T1-weighted brain magnetic resonance images. Utilizing 42 volumetric non-contrast T1-weighted brain scans from four datasets, we delineated bounding boxes around the carotid arteries in the axial slices. Pseudo-RGB images were generated from these slices, and recursive segmentation was conducted using a Res-Unet-based neural network architecture. The model's performance was tested on a separate dataset, with ground truth annotations provided by a radiologist. After recursive training, we achieved an Intersection over Union (IoU) score of (0.68 ± 0.08) on the unseen dataset, demonstrating commendable qualitative results.

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).

All answers are in the images: A review of deep learning for cerebrovascular segmentation

Cerebrovascular imaging is a common examination. Its accurate cerebrovascular segmentation become an important auxiliary method for the diagnosis and treatment of cerebrovascular diseases, which has received extensive attention from researchers. Deep learning is a heuristic method that encourages researchers to derive answers from the images by driving datasets. With the continuous development of datasets and deep learning theory, it has achieved important success for cerebrovascular segmentation. Detailed survey is an important reference for researchers. To comprehensively analyze the newest cerebrovascular segmentation, we have organized and discussed researches centered on deep learning. This survey comprehensively reviews deep learning for cerebrovascular segmentation since 2015, it mainly includes sliding window based models, U-Net based models, other CNNs based models, small-sample based models, semi-supervised or unsupervised models, fusion based models, Transformer based models, and graphics based models. We organize the structures, improvement, and important parameters of these models, as well as analyze development trends and quantitative assessment. Finally, we have discussed the challenges and opportunities of possible research directions, hoping that our survey can provide researchers with convenient reference.

VWI-APP: Vessel wall imaging-dedicated automated processing pipeline for intracranial atherosclerotic plaque quantification

BACKGROUND

Quantitative plaque assessment based on 3D magnetic resonance (MR) vessel wall imaging (VWI) has been shown to provide valuable numerical markers of the burden and risk of intracranial atherosclerotic disease (ICAD). However, plaque quantification is currently time-consuming and observer-dependent due to the demand for heavy manual effort. A VWI-dedicated automated processing pipeline (VWI-APP) is desirable.

PURPOSE

To develop and evaluate a VWI-APP for end-to-end quantitative analysis of intracranial atherosclerotic plaque.

METHODS

We retrospectively enrolled 91 subjects with ICAD (80 for pipeline development, 10 for an end-to-end pipeline evaluation, and 1 for demonstrating longitudinal plaque assessment) who had undergone VWI and MR angiography. In an end-to-end evaluation, diameter stenosis (DS), normalized wall index (NWI), remodeling ratio (RR), plaque wall contrast ratio (CR), and total plaque volume (TPV) were quantified at each culprit lesion using the developed VWI-APP and a computer-aided manual approach by a neuroradiologist, respectively. The time consumed in each quantification approach was recorded. Two-sided paired t-tests and intraclass correlation coefficient (ICC) were used to determine the difference and agreement in each plaque metric between VWI-APP and manual quantification approaches.

RESULTS

There was no significant difference between VWI-APP and manual quantification in each plaque metric. The ICC was 0.890, 0.813, 0.827, 0.891, and 0.991 for DS, NWI, RR, CR, and TPV, respectively, suggesting good to excellent accuracy of the pipeline method in plaque quantification. Quantitative analysis of each culprit lesion on average took 675.7 s using the manual approach but shortened to 238.3 s with the aid of VWI-APP.

CONCLUSIONS

VWI-APP is an accurate and efficient approach to intracranial atherosclerotic plaque quantification. Further clinical assessment of this automated tool is warranted to establish its utility in the risk assessment of ICAD lesions.

Impact of lesion size on reproducibility of quantitative measurement and radiomic features in vessel wall MRI

OBJECTIVES

To investigate reproducibility of quantitative measurement and radiomic features in vessel wall MRI (VW-MRI), evaluate the impact of lesion size, and identify reproducible radiomic features.

METHODS

This retrospective, single-center study included 251 patients (mean age, 53 ± 12 years; 128 women) with atherosclerosis, dissection, aneurysm, moyamoya disease, and vasculitis of the intracranial arteries who underwent three-dimensional turbo spin echo T1-weighted image. Lesion thickness, volume, and signal intensity were measured, and 157 radiomic features were extracted. Intra-observer reproducibility of quantitative measurement and radiomic features was evaluated by calculating the concordance correlation coefficient (CCC) and proportion of radiomic features above the predefined CCC. The reproducibility of quantitative measurement and radiomic features according to lesion size (binary comparison and stratification into 5 and 18 groups) was evaluated.

RESULTS

There was an overall serial increase in CCC for thickness measurement when stratified by lesion thickness and volume. There was an overall serial increase in the median CCC for radiomic features and proportion of radiomic features with CCC > 0.85 when stratified by lesion thickness and volume. Reproducibility of radiomic features was higher in the lesions with thickness ≥ 2.5 mm (median CCC, 0.97 vs. 0.89, p < .001; proportion with CCC > 0.85, 88.5% vs. 59.6%, p < .001) and volume ≥ 50 mm³ (median CCC, 0.97 vs. 0.88, p < .001; proportion with CCC > 0.85, 90.4% vs. 59.0%, p < .001). Intensity-based statistical features remained most reproducible in the thinnest and smallest lesions.

CONCLUSIONS

Intra-observer reproducibility of thickness measurement and radiomic features was affected by lesion size in VW-MRI although intensity-based statistical features remained most reproducible.

KEY POINTS

• There was an overall serial increase in CCC for thickness measurement when stratified by lesion size. • There was an overall serial increase in the median CCC for radiomic features and proportion of radiomic features with CCC > 0.85 when stratified by lesion size. • Intensity-based statistical features remained most reproducible in the thinnest and smallest lesions.

3D carotid artery segmentation using shape-constrained active contours

Reconstruction of the carotid artery is demanded in the detection and characterization of atherosclerosis. This study proposes a shape-constrained active contour model for segmenting the carotid artery from MR images, which embeds the output of the deep learning network into the active contour. First the centerline of the carotid artery is localized and then modified active contour initialized from the centerline is used to extract the vessel lumen, finally the probability atlas generated by the deep learning network in polar representation domain is integrated into the active contour as a prior information to detect the outer wall. The results showed that the proposed active contour model was efficient and comparable to manual segmentation.

Intracranial vessel wall segmentation with deep learning using a novel tiered loss function incorporating class inclusion

PURPOSE

To develop an automated vessel wall segmentation method on T1-weighted intracranial vessel wall magnetic resonance images, with a focus on modeling the inclusion relation between the inner and outer boundaries of the vessel wall.

METHODS

We propose a novel method that estimates the inner and outer vessel wall boundaries simultaneously, using a network with a single output channel resembling the level-set function height. The network is driven by a unique tiered loss that accounts for data fidelity of the lumen and vessel wall classes and a length regularization to encourage boundary smoothness.

RESULTS

Implemented with a 2.5D UNet with a ResNet backbone, the proposed method achieved Dice similarity coefficients (DSC) in 2D of 0.925 ± 0.048, 0.786 ± 0.084, Hausdorff distance (HD) of 0.286 ± 0.436, 0.345 ± 0.419 mm, and mean surface distance (MSD) of 0.083 ± 0.037 and 0.103 ± 0.032 mm for the lumen and vessel wall, respectively, on a test set; compared favorably to a baseline UNet model that achieved DSC 0.924 ± 0.047, 0.794 ± 0.082, HD 0.298 ± 0.477, 0.394 ± 0.431 mm, and MSD 0.087 ± 0.056, 0.119 ± 0.059 mm. Our vessel wall segmentation method achieved substantial improvement in morphological integrity and accuracy compared to benchmark methods.

CONCLUSIONS

The proposed method provides a systematic approach to model the inclusion morphology and incorporate it into an optimization infrastructure. It can be applied to any application where inclusion exists among a (sub)set of classes to be segmented. Improved feasibility in result morphology promises better support for clinical quantification and decision.

Automated morphologic analysis of intracranial and extracranial arteries using convolutional neural networks

OBJECTIVE

To develop an automated method for 3D magnetic resonance (MR) vessel wall image analysis to facilitate morphologic quantification of intra- and extracranial arteries, including vessel centerline tracking, vessel straightening and reformation, vessel wall segmentation based on convoluted neural networks (CNNs), and morphological measurement.

METHODS

MR vessel wall images acquired using DANTE-SPACE sequences and corresponding time-of-flight-MRA images of 67 subjects (including 47 healthy volunteers and 20 patients with atherosclerosis) were included in this study. The centerline of the vessel was firstly extracted from the MRA images and copyed to the 3D MR vessel wall images through the registration relationship between the MRA images and the MR vessel wall images to extract, straighten, and reconstruct interested vessel segments into 2D slices. Then a complete CNN-based Unet-like method was used to automatically segment the vessel wall to obtain quantitative morphological measurements such as maximum wall thicknesses and normalized wall index (NWI).

RESULTS

The proposed automatic segmentation network was trained and validated with 11,735 slices and tested on 2517 slices. The method showed satisfactory agreement with manual segmentation method. The Dice coefficients of intracranial arteries were 0.90 for lumen and 0.78 for vessel wall, while the Dice coefficients of extracranial arteries were 0.90 for lumen and 0.82 for vessel wall. The maximum wall thickness and NWI obtained from the proposed automatic method were slightly larger than those obtained from the manual method for both intra- and extracranial arteries. However, there was no significant difference of the quantitative measurements between the two methods (p > 0.05). In addition, the automatically measured NWI of plaque slice was significantly larger than that of normal slice.

CONCLUSION

We propose an automatic analysis method of MR vessel wall images, which can realize automatic segmentation of intra- and extracranial vessel wall. It is expected to facilitate large-scale arterial vessel wall morphological quantification.

ADVANCES IN KNOWLEDGE

We have proposed an automatic method for analysis of intra- and extracranial MR vessel wall images simultaneously based on CNN, which can facilitate large-scale quantitative analyses of vessel walls.

A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis

To develop a precise semantic segmentation method with an emphasis on the edges for automated segmentation of the arterial vessel wall and plaque based on the convolutional neural network (CNN) in order to facilitate the quantitative assessment of plaque in patients with ischemic stroke. A total of 124 subjects’ MR vessel wall images were used to train, validate, and test the model using deep learning. An end-to-end architecture network that can emphasize the edge information, namely the Edge Vessel Segmentation Network (EVSegNet) for automated segmentation of the arterial vessel wall, is proposed. The EVSegNet network consists of two workflows: one is implemented to achieve finely and multiscale segmentation by combining Dense Upsampling Convolution (DUC) and Hybrid Dilated Convolution (HDC) with different dilation rates modules, and the other utilizes edge information and is fused with another workflow to finally segment the vessel wall. The proposed network demonstrates robust segmentation of the vessel wall and better performance with a Dice (%) of 87.5, compared with the traditional U-net that has a Dice (%) of 81.0 and other U-net-based models on the test dataset. The results suggest that the proposed segmentation method with an emphasis on the edges improves segmentation accuracy effectively and will facilitate the quantitative assessment of atherosclerosis.

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images

Purpose

To develop and evaluate an automatic segmentation method of arterial vessel walls and plaques, which is beneficial for facilitating the arterial morphological quantification in magnetic resonance vessel wall imaging (MRVWI).

Methods

MRVWI images acquired from 124 patients with atherosclerotic plaques were included. A convolutional neural network-based deep learning model, namely VWISegNet, was used to extract the features from MRVWI images and calculate the category of each pixel to facilitate the segmentation of vessel wall. Two-dimensional (2D) cross-sectional slices reconstructed from all plaques and 7 main arterial segments of 115 patients were used to build and optimize the deep learning model. The model performance was evaluated on the remaining nine-patient test set using the Dice similarity coefficient (DSC) and average surface distance (ASD).

Results

The proposed automatic segmentation method demonstrated satisfactory agreement with the manual method, with DSCs of 93.8% for lumen contours and 86.0% for outer wall contours, which were higher than those obtained from the traditional U-Net, Attention U-Net, and Inception U-Net on the same nine-subject test set. And all the ASD values were less than 0.198 mm. The Bland-Altman plots and scatter plots also showed that there was a good agreement between the methods. All intraclass correlation coefficient values between the automatic method and manual method were greater than 0.780, and greater than that between two manual reads.

Conclusion

The proposed deep learning-based automatic segmentation method achieved good consistency with the manual methods in the segmentation of arterial vessel wall and plaque and is even more accurate than manual results, hence improved the convenience of arterial morphological quantification.

MRA-free intracranial vessel localization on MR vessel wall images

Analysis of vessel morphology is important in assessing intracranial atherosclerosis disease (ICAD). Recently, magnetic resonance (MR) vessel wall imaging (VWI) has been introduced to image ICAD and characterize morphology for atherosclerotic lesions. In order to automatically perform quantitative analysis on VWI data, MR angiography (MRA) acquired in the same imaging session is typically used to localize the vessel segments of interest. However, MRA may be unavailable caused by the lack or failure of the sequence in a VWI protocol. This study aims to investigate the feasibility to infer the vessel location directly from VWI. We propose to synergize an atlas-based method to preserve general vessel structure topology with a deep learning network in the motion field domain to correct the residual geometric error. Performance is quantified by examining the agreement between the extracted vessel structures from the pair-acquired and alignment-corrected angiogram, and the estimated output using a cross-validation scheme. Our proposed pipeline yields clinically feasible performance in localizing intracranial vessels, demonstrating the promise of performing vessel morphology analysis using VWI alone.

Disparate trends of atherosclerotic plaque evolution in stroke patients under 18-month follow-up: a 3D whole-brain magnetic resonance vessel wall imaging study

PURPOSE

The trend of atherosclerotic plaque feature evolution is unclear in stroke patients with and without recurrence. We aimed to use three-dimensional whole-brain magnetic resonance vessel wall imaging to quantify the morphological changes of causative lesions during medical therapy in patients with symptomatic intracranial atherosclerotic disease.

METHODS

Patients with acute ischemic stroke attributed to intracranial atherosclerotic disease were retrospectively enrolled if they underwent both baseline and follow-up magnetic resonance vessel wall imaging. The morphological features of the causative plaque, including plaque volume, peak normalized wall index, maximum wall thickness, degree of stenosis, pre-contrast plaque-wall contrast ratio, and post-contrast plaque enhancement ratio, were quantified and compared between the non-recurrent and recurrent groups (defined as the recurrence of a vascular event within 18 months of stroke).

RESULTS

Twenty-nine patients were included in the final analysis. No significant differences were found in plaque features in the baseline scan between the non-recurrent (n = 22) and recurrent groups (n = 7). The changes in maximum wall thickness (-13.32% vs. 8.93%, P = 0.026), plaque-wall contrast ratio (-0.82% vs. 3.42%, P = 0.005) and plaque enhancement ratio (-11.03% vs. 9.75%, P = 0.019) were significantly different between the non-recurrent and recurrent groups. Univariable logistic regression showed that the increase in plaque-wall contrast ratio (odds ratio 3.22, 95% confidence interval 1.55-9.98, P = 0.003) was related to stroke recurrence.

CONCLUSION

Morphological changes of plaque features on magnetic resonance vessel wall imaging demonstrated distinct trends in symptomatic intracranial atherosclerotic disease patients with and without stroke recurrence.

Current Clinical Applications of Intracranial Vessel Wall MR Imaging

Intracranial vessel wall MR imaging (VWI) is increasingly being used as a valuable adjunct to conventional angiographic imaging techniques. This article will provide an updated review on intracranial VWI protocols and image interpretation. We review VWI technical considerations, describe common VWI imaging features of different intracranial vasculopathies and show illustrative cases. We review the role of VWI for differentiating among steno-occlusive vasculopathies, such as intracranial atherosclerotic plaque, dissections and Moyamoya disease. We also highlight how VWI may be used for the diagnostic work-up and surveillance of patients with vasculitis of the central nervous system and cerebral aneurysms.

INTRACRANIAL VESSEL WALL SEGMENTATION FOR ATHEROSCLEROTIC PLAQUE QUANTIFICATION

Intracranial vessel wall segmentation is critical for the quantitative assessment of intracranial atherosclerosis based on magnetic resonance vessel wall imaging. This work further improves on a previous 2D deep learning segmentation network by the utilization of 1) a 2.5D structure to balance network complexity and regularizing geometry continuity; 2) a UNET++ model to achieve structure adaptation; 3) an additional approximated Hausdorff distance (HD) loss into the objective to enhance geometry conformality; and 4) landing in a commonly used morphological measure of plaque burden - the normalized wall index (NWI) - to match the clinical endpoint. The modified network achieved Dice similarity coefficient of 0.9172 ± 0.0598 and 0.7833 ± 0.0867, HD of 0.3252 ± 0.5071 mm and 0.4914 ± 0.5743 mm, mean surface distance of 0.0940 ± 0.0781 mm and 0.1408 ± 0.0917 mm for the lumen and vessel wall, respectively. These results compare favorably to those obtained by the original 2D UNET on all segmentation metrics. Additionally, the proposed segmentation network reduced the mean absolute error in NWI from 0.0732 ± 0.0294 to 0.0725 ± 0.0333.

Intracranial Atherosclerotic Plaque Characteristics and Burden Associated With Recurrent Acute Stroke: A 3D Quantitative Vessel Wall MRI Study

Background: Intracranial atherosclerotic disease (ICAD) tends to affect multiple arterial segments, and previous studies rarely performed a comprehensive plaque analysis of the entire circle of Willis for the evaluation of recurrent stroke risk. We aimed to investigate the features of circle of Willis ICAD on 3D magnetic resonance vessel wall imaging (MR-VWI) and their relationships with recurrent acute stroke. Methods: Patients with either acute ischemic stroke (within 4 weeks after stroke) or chronic ischemic stroke (after 3 months of stroke) due to intracranial atherosclerotic plaque underwent 3D contrast-enhanced MR-VWI covering major cerebral arteries. Participants were divided into three groups: first-time acute stroke, recurrent acute stroke, and chronic stroke. Culprit plaque (defined as the only lesion or the most stenotic lesion when multiple plaques were present within the same vascular territory of the stroke) and non-culprit plaque characteristics, including total plaque number, plaque thickness, plaque area, plaque burden (calculated as plaque area divided by outer wall area), enhancement ratio (ER), eccentricity, and stenosis, were measured and compared across the three groups. Associations between plaque characteristics and recurrent acute stroke were investigated by multivariate analysis. Results: A total of 176 participants (aged 61 ± 10 years, 109 men) with 702 intracranial plaques were included in this study. There were 80 patients with first-time acute stroke, 42 patients with recurrent acute stroke, and 54 patients with chronic stroke. More intracranial plaques were found per patient in the recurrent acute stroke group than in the first-time acute stroke or chronic stroke group (5.19 ± 1.90 vs. 3.71 ± 1.96 and 3.46 ± 1.33, p < 0.001). Patients in the recurrent acute stroke group had greater culprit plaque burden (p < 0.001) and higher culprit ER (p < 0.001) than the other two groups. After adjustment of clinical demographic factors, in multivariate analysis, coronary artery disease (CAD) (odds ratio, OR = 4.61; p = 0.035), total plaque number (OR = 1.54; p = 0.003), culprit plaque ER (OR = 2.50; p = 0.036), and culprit plaque burden (OR per 10% increment = 2.44; p = 0.010) were all independently associated with recurrent acute stroke compared to the first-time acute stroke. Conclusion: Increased intracranial atherosclerotic plaque number, higher culprit plaque ER, greater culprit plaque burden, and CAD are independently associated with recurrent acute stroke.

Domain adaptive and fully automated carotid artery atherosclerotic lesion detection using an artificial intelligence approach (LATTE) on 3D MRI

PURPOSE

To develop and evaluate a domain adaptive and fully automated review workflow (lesion assessment through tracklet evaluation, LATTE) for assessment of atherosclerotic disease in 3D carotid MR vessel wall imaging (MR VWI).

METHODS

VWI of 279 subjects with carotid atherosclerosis were used to develop LATTE, mainly convolutional neural network (CNN)-based domain adaptive lesion classification after image quality assessment and artery of interest localization. Heterogeneity in test sets from various sites usually causes inferior CNN performance. With our novel unsupervised domain adaptation (DA), LATTE was designed to accurately classify arteries into normal arteries and early and advanced lesions without additional annotations on new datasets. VWI of 271 subjects from four datasets (eight sites) with slightly different imaging parameters/signal patterns were collected to assess the effectiveness of DA of LATTE using the area under the receiver operating characteristic curve (AUC) on all lesions and advanced lesions before and after DA.

RESULTS

LATTE had good performance with advanced/all lesion classification, with the AUC of >0.88/0.83, significant improvements from >0.82/0.80 if without DA.

CONCLUSIONS

LATTE can locate target arteries and distinguish carotid atherosclerotic lesions with consistently improved performance with DA on new datasets. It may be useful for carotid atherosclerosis detection and assessment on various clinical sites.

Neural network enhanced 3D turbo spin echo for MR intracranial vessel wall imaging

PURPOSE

To improve the signal-to-noise ratio (SNR) and image sharpness for whole brain isotropic 0.5 mm three-dimensional (3D) T₁ weighted (T₁w) turbo spin echo (TSE) intracranial vessel wall imaging (IVWI) at 3 T.

METHODS

The variable flip angle (VFA) method enables useful optimization across scan efficiency, SNR and relaxation induced point spread function (PSF) for TSE imaging. A convolutional neural network (CNN) was developed to retrospectively enhance the acquired TSE image with PSF blurring. The previously developed VFA method to increase SNR at the expense of blur can be combined with the presented PSF correction to yield long echo train length (ETL) scan while the acquired image remains high SNR and sharp. The overall approach can enable an optimized solution for accelerated whole brain high-resolution 3D T₁w TSE IVWI. Its performance was evaluated on healthy volunteers and patients.

RESULTS

The PSF blurred image acquired by a long ETL scan can be enhanced by CNN to restore similar sharpness as a short ETL scan, which outperforms the traditional linear PSF enhancement approach. For accelerated whole brain IVWI on volunteers, the optimized isotropic 0.5 mm 3D T₁w TSE sequence with CNN based PSF enhancement provides sufficient flow suppression and improved image quality. Preliminary results on patients further demonstrated its improved delineation for intracranial vessel wall and plaque morphology.

CONCLUSION

The CNN enhanced VFA TSE imaging enables an overall image quality improvement for high-resolution 3D T₁w IVWI, and may provide a better tradeoff across scan efficiency, SNR and PSF for 3D TSE acquisitions.

Imaging endpoints of intracranial atherosclerosis using vessel wall MR imaging: a systematic review

PURPOSE

The vessel wall MR imaging (VWI) literature was systematically reviewed to assess the criteria and measurement methods of VWI-related imaging endpoints for symptomatic intracranial plaque in patients with ischemic events.

METHODS

PubMed, Scopus, Web of Science, EMBASE, and Cochrane databases were searched up to October 2019. Two independent reviewers extracted data from 47 studies. A modified Guideline for Reporting Reliability and Agreement Studies was used to assess completeness of reporting.

RESULTS

The specific VWI-pulse sequence used to identify plaque was reported in 51% of studies. A VWI-based criterion to define plaque was reported in 38% of studies. A definition for culprit plaque was reported in 40% of studies. Frequently scored qualitative imaging endpoints were plaque quadrant (21%) and enhancement (21%). Frequently measured quantitative imaging endpoints were stenosis (19%), lumen area (15%), and remodeling index (14%). Reproducibility for all endpoints ranged from good to excellent (range: ICC_{T1 hyperintensity} = 0.451 to ICC_stenosis = 0.983). However, rater specialty and years of experience varied among studies.

CONCLUSIONS

Investigators are using different criteria to identify and measure VWI-imaging endpoints for culprit intracranial plaque. Early awareness of these differences to address methods of acquisition and measurement will help focus research resources and efforts in technique optimization and measurement reproducibility. Consensual definitions to detect plaque will be important to develop automatic lesion detection tools particularly in the era of radiomics.

Contemporary and emerging magnetic resonance imaging methods for evaluation of moyamoya disease

Numerous recent technological advances offer the potential to substantially enhance the MRI evaluation of moyamoya disease (MMD). These include high-resolution volumetric imaging, high-resolution vessel wall characterization, improved cerebral angiographic and perfusion techniques, high-field imaging, fast scanning methods, and artificial intelligence. This review discusses the current state-of-the-art MRI applications in these realms, emphasizing key imaging findings, clinical utility, and areas that will benefit from further investigation. Although these techniques may apply to imaging of a wide array of neurovascular or other neurological conditions, consideration of their application to MMD is useful given the comprehensive multidimensional MRI assessment used to evaluate MMD. These MRI techniques span from basic cross-sectional to advanced functional sequences, both qualitative and quantitative.The aim of this review was to provide a comprehensive summary and analysis of current key relevant literature of advanced MRI techniques for the evaluation of MMD with image-rich case examples. These imaging methods can aid clinical characterization, help direct treatment, assist in the evaluation of treatment response, and potentially improve the understanding of the pathophysiology of MMD.

Subvoxel vessel wall thickness measurements of the intracranial arteries using a convolutional neural network

Vessel wall thickening of the intracranial arteries has been associated with cerebrovascular disease and atherosclerotic plaque development. Visualization of the vessel wall has been enabled by recent advancements in vessel wall MRI. However, quantifying early wall thickening from these MR images is difficult and prone to severe overestimation, because the voxel size of clinically used acquisitions exceeds the wall thickness of the intracranial arteries. In this study, we aimed for accurate and precise subvoxel vessel wall thickness measurements. A convolutional neural network was trained on MR images of 34 ex vivo circle of Willis specimens, acquired with a clinically used protocol (isotropic acquired voxel size: 0.8 mm). Ground truth measurements were performed on images acquired with an ultra-high-resolution protocol (isotropic acquired voxel size: 0.11 mm) and were used for evaluation. Additionally, we determined the robustness of our method by applying Monte Carlo dropout and test time augmentation. Lastly, we applied our method on in vivo images of three intracranial aneurysms to measure their wall thickness. Our method shows resolvability of different vessel wall thicknesses, well below the acquired voxel size. The method described may facilitate quantitative measurements on MRI data for a wider range of clinical applications.

Vessel wall MR imaging of intracranial atherosclerosis

Intracranial atherosclerotic disease (ICAD) is one of the most common causes of ischemic stroke worldwide. Along with high recurrent stroke risk from ICAD, its association with cognitive decline and dementia leads to a substantial decrease in quality of life and a high economic burden. Atherosclerotic lesions can range from slight wall thickening with plaques that are angiographically occult to severely stenotic lesions. Recent advances in intracranial high resolution vessel wall MR (VW-MR) imaging have enabled imaging beyond the lumen to characterize the vessel wall and its pathology. This technique has opened new avenues of research for identifying vulnerable plaque in the setting of acute ischemic stroke as well as assessing ICAD burden and its associations with its sequela, such as dementia. We now understand more about the intracranial arterial wall, its ability to remodel with disease and how we can use VW-MR to identify angiographically occult lesions and assess medical treatment responses, for example, to statin therapy. Our growing understanding of ICAD with intracranial VW-MR imaging can profoundly impact diagnosis, therapy, and prognosis for ischemic stroke with the possibility of lesion-based risk models to tailor and personalize treatment. In this review, we discuss the advantages of intracranial VW-MR imaging for ICAD, the potential of bioimaging markers to identify vulnerable intracranial plaque, and future directions of artificial intelligence and its utility for lesion scoring and assessment.

Moving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping

BACKGROUND AND OBJECTIVES

Surgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Tracking of the instruments (graspers, needle drivers) and field of view from the stereoscopic camera during surgery could further help the surgeons to remain focussed and reduce the probability of committing any mistakes. Tracking is usually preferred in computerized video surveillance, traffic monitoring, military surveillance system, and vehicle navigation. Despite the numerous efforts over the last few years, object tracking still remains an open research problem, mainly due to motion blur, image noise, lack of image texture, and occlusion. Most of the existing object tracking methods are time-consuming and less accurate when the input video contains high volume of information and more number of instruments.

METHODS

This paper presents a variational framework to track the motion of moving objects in surgery videos. The key contributions are as follows: (1) A denoising method using stochastic resonance in maximal overlap discrete wavelet transform is proposed and (2) a robust energy functional based on Bhattacharyya coefficient to match the target region in the first frame of the input sequence with the subsequent frames using a similarity metric is developed. A modified affine transformation-based registration is used to estimate the motion of the features following an active contour-based segmentation method to converge the contour resulted from the registration process.

RESULTS AND CONCLUSION

The proposed method has been implemented on publicly available databases; the results are found satisfactory. Overlap index (OI) is used to evaluate the tracking performance, and the maximum OI is found to be 76% and 88% on private data and public data sequences.