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

Semi-automatic detection and segmentation algorithm of saccular aneurysms in 2D cerebral DSA images

AbstractObjectiveTo detect and segment cerebral saccular aneurysms (CSAs) in 2D Digital Subtraction Angiography (DSA) images.Patients and methodsTen patients underwent Intra-arterial DSA procedures. Patients were injected with Iodine-containing radiopaque material. A scheme for semi-automatic detection and segmentation of intracranial aneurysms is proposed in this study. The algorithm consisted of three major image processing stages: image enhancement, image segmentation and image classification. Applied to the 2D Digital Subtraction Angiography (DSA) images, the algorithm was evaluated in 19 scene files to detect 10 CSAs.ResultsAneurysms were identified by the proposed detection and segmentation algorithm with 89.47% sensitivity and 80.95% positive predictive value (PPV) after executing the algorithm on 19 DSA images of 10 aneurysms. Results have been verified by specialized radiologists. However, 4 false positive aneurysms were detected when aneurysms’ location is at Anterior Communicating Artery (ACA).ConclusionThe suggested algorithm is a promising method for detection and segmentation of saccular aneurysms; it provides a diagnostic tool for CSAs

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems

Fúze obrazu je dnes jednou z nejběžnějších avšak stále velmi diskutovanou oblastí v lékařském zobrazování a hraje důležitou roli ve všech oblastech lékařské péče jako je diagnóza, léčba a chirurgie. V této dizertační práci jsou představeny tři projekty, které jsou velmi úzce spojeny s oblastí fúze medicínských dat. První projekt pojednává o 3D CT subtrakční angiografii dolních končetin. V práci je využito kombinace kontrastních a nekontrastních dat pro získání kompletního cévního stromu. Druhý projekt se zabývá fúzí DTI a T1 váhovaných MRI dat mozku. Cílem tohoto projektu je zkombinovat stukturální a funkční informace, které umožňují zlepšit znalosti konektivity v mozkové tkáni. Třetí projekt se zabývá metastázemi v CT časových datech páteře. Tento projekt je zaměřen na studium vývoje metastáz uvnitř obratlů ve fúzované časové řadě snímků. Tato dizertační práce představuje novou metodologii pro klasifikaci těchto metastáz. Všechny projekty zmíněné v této dizertační práci byly řešeny v rámci pracovní skupiny zabývající se analýzou lékařských dat, kterou vedl pan Prof. Jiří Jan. Tato dizertační práce obsahuje registrační část prvního a klasifikační část třetího projektu. Druhý projekt je představen kompletně. Další část prvního a třetího projektu, obsahující specifické předzpracování dat, jsou obsaženy v disertační práci mého kolegy Ing. Romana Petera.Image fusion is one of today´s most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. Jiří Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.

Peak Trekking of Hierarchy Mountain for the Detection of Cerebral Aneurysm using Modified Hough Circle Transform

The Circle of Willis is in the junction of two carotid arteries and two vertebral arteries that supply the brain with nutrition. Junctions where these arteries come together may develop weak spots that can balloon out and fill with blood, creating aneurysms. These sac-like areas may leak or rupture, spilling blood into surrounding tissues which may cause artery spasm leading to potential stroke or even death. Clipping and coiling are two treatment options preferred by neurosurgeon which require proper detection of aneurysm. Medical practitioners are therefore emphasizing on the prior detection of cerebral aneurysm (CA) before rupture occurs leading to subarachnoid haemorrhage (SAH). This paper presents a novel method by application of Modified Hough Circle Transform & Peak Trekking (MHCT-PT) technique on the image extracted from Digital subtraction angiography (DSA). Experimental results have firmly substantiated that the proposed method is highly efficient in properly detecting the location, size and type of aneurysm

Peak Trekking of Hierarchy Mountain for the Detection of Cerebral Aneurysm using Modified Hough Circle Transform

The Circle of Willis is in the junction of two carotid arteries and two vertebral arteries that supply the brain with nutrition. Junctions where these arteries come together may develop weak spots that can balloon out and fill with blood, creating aneurysms. These sac-like areas may leak or rupture, spilling blood into surrounding tissues which may cause artery spasm leading to potential stroke or even death. Clipping and coiling are two treatment options preferred by neurosurgeon which require proper detection of aneurysm. Medical practitioners are therefore emphasizing on the prior detection of cerebral aneurysm (CA) before rupture occurs leading to subarachnoid haemorrhage (SAH). This paper presents a novel method by application of Modified Hough Circle Transform & Peak Trekking (MHCT-PT) technique on the image extracted from Digital subtraction angiography (DSA). Experimental results have firmly substantiated that the proposed method is highly efficient in properly detecting the location, size and type of aneurysm

The residual STL volume as a metric to evaluate accuracy and reproducibility of anatomic models for 3D printing: application in the validation of 3D-printable models of maxillofacial bone from reduced radiation dose CT images.

BackgroundThe effects of reduced radiation dose CT for the generation of maxillofacial bone STL models for 3D printing is currently unknown. Images of two full-face transplantation patients scanned with non-contrast 320-detector row CT were reconstructed at fractions of the acquisition radiation dose using noise simulation software and both filtered back-projection (FBP) and Adaptive Iterative Dose Reduction 3D (AIDR3D). The maxillofacial bone STL model segmented with thresholding from AIDR3D images at 100 % dose was considered the reference. For all other dose/reconstruction method combinations, a "residual STL volume" was calculated as the topologic subtraction of the STL model derived from that dataset from the reference and correlated to radiation dose.ResultsThe residual volume decreased with increasing radiation dose and was lower for AIDR3D compared to FBP reconstructions at all doses. As a fraction of the reference STL volume, the residual volume decreased from 2.9 % (20 % dose) to 1.4 % (50 % dose) in patient 1, and from 4.1 % to 1.9 %, respectively in patient 2 for AIDR3D reconstructions. For FBP reconstructions it decreased from 3.3 % (20 % dose) to 1.0 % (100 % dose) in patient 1, and from 5.5 % to 1.6 %, respectively in patient 2. Its morphology resembled a thin shell on the osseous surface with average thickness &lt;0.1 mm.ConclusionThe residual volume, a topological difference metric of STL models of tissue depicted in DICOM images supports that reduction of CT dose by up to 80 % of the clinical acquisition in conjunction with iterative reconstruction yields maxillofacial bone models accurate for 3D printing