Volumetric analysis of arteriovenous malformation using computed tomographic angiography

Thesis (M.A.)--Boston UniversityAn arteriovenous malformation (AVM) is an abnormal collection of blood vessels in which arterial blood flows directly into the draining vein without the normal interposed capillaries. It is an important and growing public healthcare problem affecting millions of Americans and many more people internationally. There are several potential treatment options for the AVM, and the best treatment depends on the maximum length of nidus based on the Spetzler- Martin grading system. However, this grading system is insensitive to volume, because it was designed on the basis of two dimensional digital subtraction angiography images. Here, we report a method using computed tomographic angiography to measure the volume of AVM nidus, as a means for noninvasively assessment. The initial results show statistically significant differences between healthy and AVM subject groups in the direct comparisons of the volume (cm3) through the method we suggested (2.456 ± 1.482, 12.478 ± 5.743 and 53.963 ± 9.338 (mean ± stdev.); Normal (No AVM), Small (< 3cm), Medium (3 ~ 6 cm) respectively; P < 0.005 for all), and they also show the exponential correlation between the AVM volume and the maximum length of a nidus (trend-line: y = 4.4183e0.536x with R2 = 0.945). These results provide more accurate volumetric information. Therefore, this noninvasive imaging-based method is a promising means to measure the volume of AVM using clinically available imaging tools

Optical Coherence Tomography Angiography Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.

PurposeThe purpose of this study was to compare retinal nerve fiber layer (RNFL) thickness and optical coherence tomography angiography (OCT-A) retinal vasculature measurements in healthy, glaucoma suspect, and glaucoma patients.MethodsTwo hundred sixty-one eyes of 164 healthy, glaucoma suspect, and open-angle glaucoma (OAG) participants from the Diagnostic Innovations in Glaucoma Study with good quality OCT-A images were included. Retinal vasculature information was summarized as a vessel density map and as vessel density (%), which is the proportion of flowing vessel area over the total area evaluated. Two vessel density measurements extracted from the RNFL were analyzed: (1) circumpapillary vessel density (cpVD) measured in a 750-μm-wide elliptical annulus around the disc and (2) whole image vessel density (wiVD) measured over the entire image. Areas under the receiver operating characteristic curves (AUROC) were used to evaluate diagnostic accuracy.ResultsAge-adjusted mean vessel density was significantly lower in OAG eyes compared with glaucoma suspects and healthy eyes. (cpVD: 55.1 ± 7%, 60.3 ± 5%, and 64.2 ± 3%, respectively; P &lt; 0.001; and wiVD: 46.2 ± 6%, 51.3 ± 5%, and 56.6 ± 3%, respectively; P &lt; 0.001). For differentiating between glaucoma and healthy eyes, the age-adjusted AUROC was highest for wiVD (0.94), followed by RNFL thickness (0.92) and cpVD (0.83). The AUROCs for differentiating between healthy and glaucoma suspect eyes were highest for wiVD (0.70), followed by cpVD (0.65) and RNFL thickness (0.65).ConclusionsOptical coherence tomography angiography vessel density had similar diagnostic accuracy to RNFL thickness measurements for differentiating between healthy and glaucoma eyes. These results suggest that OCT-A measurements reflect damage to tissues relevant to the pathophysiology of OAG

Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Intraoperative detection of blood vessels with an imaging needle during neurosurgery in humans

Intracranial hemorrhage can be a devastating complication associated with needle biopsies of the brain. Hemorrhage can occur to vessels located adjacent to the biopsy needle as tissue is aspirated into the needle and removed. No intraoperative technology exists to reliably identify blood vessels that are at risk of damage. To address this problem, we developed an “imaging needle” that can visualize nearby blood vessels in real time. The imaging needle contains a miniaturized optical coherence tomography probe that allows differentiation of blood flow and tissue. In 11 patients, we were able to intraoperatively detect blood vessels (diameter, \u3e500 μm) with a sensitivity of 91.2% and a specificity of 97.7%. This is the first reported use of an optical coherence tomography needle probe in human brain in vivo. These results suggest that imaging needles may serve as a valuable tool in a range of neurosurgical needle interventions

3D printed ventricular septal defect patch: a primer for the 2015 Radiological Society of North America (RSNA) hands-on course in 3D printing.

Hand-held three dimensional models of the human anatomy and pathology, tailored-made protheses, and custom-designed implants can be derived from imaging modalities, most commonly Computed Tomography (CT). However, standard DICOM format images cannot be 3D printed; instead, additional image post-processing is required to transform the anatomy of interest into Standard Tessellation Language (STL) format is needed. This conversion, and the subsequent 3D printing of the STL file, requires a series of steps. Initial post-processing involves the segmentation-demarcation of the desired for 3D printing parts and creating of an initial STL file. Then, Computer Aided Design (CAD) software is used, particularly for wrapping, smoothing and trimming. Devices and implants that can also be 3D printed, can be designed using this software environment. The purpose of this article is to provide a tutorial on 3D Printing with the test case of complex congenital heart disease (CHD). While the infant was born with double outlet right ventricle (DORV), this hands-on guide to be featured at the 2015 annual meeting of the Radiological Society of North America Hands-on Course in 3D Printing focused on the additional finding of a ventricular septal defect (VSD). The process of segmenting the heart chambers and the great vessels will be followed by optimization of the model using CAD software. A virtual patch that accurately matches the patient's VSD will be designed and both models will be prepared for 3D printing