A task-driven cerebral angiographic imaging based on CT perfusion.

Introduction: Current clinical computed tomography arteriography (cCTA) and clinical computed tomography venography (cCTV) images often display restricted cerebrovascular profiles, incomplete brain tissue segmentation, and incomplete artery-vein segmentation. Especially for vessels associated with diseases, capturing their complete profiles proves challenging. Methods: In this work, we developed a Task-driven Cerebral Angiographic Imaging (TDCAI) technique using computed tomography perfusion (CTP) images of stroke patients. A evaluation on intracranial hemorrhagic stroke (IHS) and acute ischemic stroke (AIS) cases was performed with CT perfusion imaging. The TDCAI technique processed the CTP images, resulting in supplementary diagnostic images, including CTA, CTV, centerline images of the vessels-of-interest [internal carotid artery (ICA) for AIS patients, Labbé vein for IHS patients], and straightened images of the vessels-of-interest. Results: We conducted a comparison between the obtained CTA/CTV images and the cCTA/cCTV images in terms of overall image quality and visibility of the vessels-of-interest. By constructing a virtual vascular phantom, we extracted its centerline and compared it with the actual centerline to calculate maximum and average deviations. This allowed us to evaluate both the accuracy of the centerline extraction algorithm and its capability to resist the influence of side branches. We assessed whether vascular stenosis and dilatation could be expressed in straightened vessel images, conducting statistical analyses to establish the superiority of TDCAI technique. Discussion: This study proposes a TDCAI technique to eliminate bone and soft tissue interference, effectively segregate the comprehensive cerebral venous and arterial systems, and extract centerlines and straighten the vessels-of-interest, which would aid doctors in assessing the outflow profiles of vessels after a stroke and seeking imaging biomarkers correlated with clinical outcomes.

A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data.

PURPOSE: 7T time-of-flight (TOF) MRI provides high resolution for the evaluation of cerebrovascular vessels and pathologies. In combination with 4D flow fields acquired with phase-contrast (PC) MRI, hemodynamic information can be extracted to enhance the analysis by providing direct measurements in the larger arteries or patient-specific boundary conditions. Hence, a registration between both modalities is required. METHODS: To combine TOF and PC-MRI data, we developed a hybrid registration approach. Vessels and their centerlines are segmented from the TOF data. The centerline is fit to the intensity ridges of the lower resolved PC-MRI data, which provides temporal information. We used a metric that utilizes a scaled sum of weighted intensities and gradients on the normal plane. The registration is then guided by decoupled local affine transformations. It is applied hierarchically following the branching order of the vessel tree. RESULTS: A landmark validation over Monte Carlo simulations yielded an average mean squared error of 184.73 mm and an average Hausdorff distance of 15.20 mm. The hierarchical traversal that transforms child vessels with their parents registers even small vessels not detectable in the PC-MRI. CONCLUSION: The presented work combines high-resolution tomographic information from 7T TOF-MRI and measured flow data from 4D 7T PC-MRI scan for the arteries of the brain. This enables usage of patient-specific flow parameters for realistic simulations, thus supporting research in areas such as cerebral small vessel disease. Automatization and free deformations can help address the limiting error measures in the future.

Automated intracranial vessel labeling with learning boosted by vessel connectivity, radii and spatial context.

Cerebrovascular diseases are among the world's top causes of death and their screening and diagnosis rely on angiographic imaging. We focused on automated anatomical labeling of cerebral arteries that enables their cross-sectional quantification and inter-subject comparisons and thereby identification of geometric risk factors correlated to the cerebrovascular diseases. We used 152 cerebral TOF-MRA angiograms from three publicly available datasets and manually created reference labeling using Slicer3D. We extracted centerlines from nnU-net based segmentations using VesselVio and labeled them according to the reference labeling. Vessel centerline coordinates, in combination with additional vessel connectivity, radius and spatial context features were used for training seven distinct PointNet++ models. Model trained solely on the vessel centerline coordinates resulted in ACC of 0.93 and across-labels average TPR was 0.88. Including vessel radius significantly improved ACC to 0.95, and average TPR to 0.91. Finally, focusing spatial context to the Circle of Willis are resulted in best ACC of 0.96 and best average TPR of 0.93. Hence, using vessel radius and spatial context greatly improved vessel labeling, with the attained perfomance opening the avenue for clinical applications of intracranial vessel labeling.

Visible Vessels of Vocal Folds: Can they have a Diagnostic Role?

BACKGROUND: Challenges in visual identification of laryngeal disorders lead researchers to investigate new opportunities to help clinical examination. This paper presents an efficient and simple method which extracts and assesses blood vessels on vocal fold tissue in order to serve medical diagnosis. METHODS: The proposed vessel segmentation approach has been designed in order to overcome difficulties raised by design specifications of videolaryngostroboscopy and anatomic structure of vocal fold vasculature. The limited number of medical studies on vocal fold vasculature point out that the direction of blood vessels and amount of vasculature are discriminative features for vocal fold disorders. Therefore, we extracted the features of vessels on the basis of these studies. We represent vessels as vascular vectors and suggest a vector field based measurement that quantifies the orientation pattern of blood vessels towards vocal fold pathologies. RESULTS: In order to demonstrate the relationship between vessel structure and vocal fold disorders, we performed classification of vocal fold disorders by using only vessel features. A binary tree of Support Vector Machine (SVM) has been exploited for classification. Average recall of proposed vessel extraction method was calculated as 0.82 while healthy, sulcus vocalis, laryngitis classification accuracy of 0.75 was achieved. CONCLUSION: Obtained success rates showed the efficiency of vocal fold vessels in serving as an indicator of laryngeal diseases.

Wide-field absolute transverse blood flow velocity mapping in vessel centerline.

We propose a wide-field absolute transverse blood flow velocity measurement method in vessel centerline based on absorption intensity fluctuation modulation effect. The difference between the light absorption capacities of red blood cells and background tissue under low-coherence illumination is utilized to realize the instantaneous and average wide-field optical angiography images. The absolute fuzzy connection algorithm is used for vessel centerline extraction from the average wide-field optical angiography. The absolute transverse velocity in the vessel centerline is then measured by a cross-correlation analysis according to instantaneous modulation depth signal. The proposed method promises to contribute to the treatment of diseases, such as those related to anemia or thrombosis.

Classification of laryngeal disorders based on shape and vascular defects of vocal folds.

Vocal fold disorders such as laryngitis, vocal nodules, and vocal polyps may cause hoarseness, breathing and swallowing difficulties due to vocal fold malfunction. Despite the fact that state of the art medical imaging techniques help physicians to obtain more detailed information, difficulty in differentiating minor anomalies of vocal folds encourages physicians to research new strategies and technologies to aid the diagnostic process. Recent studies on vocal fold disorders note the potential role of the vascular structure of vocal folds in differential diagnosis of anomalies. However, standards of clinical usage of the blood vessels have not been well established yet due to the lack of objective and comprehensive evaluation of the vascular structure. In this paper, we present a novel approach that categorizes vocal folds into healthy, nodule, polyp, sulcus vocalis, and laryngitis classes exploiting visible blood vessels on the superior surface of vocal folds and shapes of vocal fold edges by using image processing techniques and machine learning methods. We first detected the vocal folds on videolaryngostroboscopy images by using Histogram of Oriented Gradients (HOG) descriptors. Then we examined the shape of vocal fold edges in order to provide features such as size and splay portion of mass lesions. We developed a new vessel centerline extraction procedure that is specialized to the vascular structure of vocal folds. Extracted vessel centerlines were evaluated in order to get vascular features of vocal folds, such as the amount of vessels in the longitudinal and transverse form. During the last step, categorization of vocal folds was performed by a novel binary decision tree architecture, which evaluates features of the vocal fold edge shape and vascular structure. The performance of the proposed system was evaluated by using laryngeal images of 70 patients. Sensitivity of 86%, 94%, 80%, 73%, and 76% were obtained for healthy, polyp, nodule, laryngitis, and sulcus vocalis classes, respectively. These results indicate that visible vessels of vocal folds can act as a prognostic marker for vocal fold pathologies, as well as the vocal fold shape features, and may play a critical role in more effective diagnosis.