6,568 research outputs found
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
Data science of stroke imaging and enlightenment of the penumbra.
Imaging protocols of acute ischemic stroke continue to hold significant uncertainties regarding patient selection for reperfusion therapy with thrombolysis and mechanical thrombectomy. Given that patient inclusion criteria can easily introduce biases that may be unaccounted for, the reproducibility and reliability of the patient screening method is of utmost importance in clinical trial design. The optimal imaging screening protocol for selection in targeted populations remains uncertain. Acute neuroimaging provides a snapshot in time of the brain parenchyma and vasculature. By identifying the at-risk but still viable penumbral tissue, imaging can help estimate the potential benefit of a reperfusion therapy in these patients. This paper provides a perspective about the assessment of the penumbral tissue in the context of acute stroke and reviews several neuroimaging models that have recently been developed to assess the penumbra in a more reliable fashion. The complexity and variability of imaging features and techniques used in stroke will ultimately require advanced data driven software tools to provide quantitative measures of risk/benefit of recanalization therapy and help aid in making the most favorable clinical decisions
Computational fluid dynamics modeling of symptomatic intracranial atherosclerosis may predict risk of stroke recurrence.
BackgroundPatients with symptomatic intracranial atherosclerosis (ICAS) of ≥ 70% luminal stenosis are at high risk of stroke recurrence. We aimed to evaluate the relationships between hemodynamics of ICAS revealed by computational fluid dynamics (CFD) models and risk of stroke recurrence in this patient subset.MethodsPatients with a symptomatic ICAS lesion of 70-99% luminal stenosis were screened and enrolled in this study. CFD models were reconstructed based on baseline computed tomographic angiography (CTA) source images, to reveal hemodynamics of the qualifying symptomatic ICAS lesions. Change of pressures across a lesion was represented by the ratio of post- and pre-stenotic pressures. Change of shear strain rates (SSR) across a lesion was represented by the ratio of SSRs at the stenotic throat and proximal normal vessel segment, similar for the change of flow velocities. Patients were followed up for 1 year.ResultsOverall, 32 patients (median age 65; 59.4% males) were recruited. The median pressure, SSR and velocity ratios for the ICAS lesions were 0.40 (-2.46-0.79), 4.5 (2.2-20.6), and 7.4 (5.2-12.5), respectively. SSR ratio (hazard ratio [HR] 1.027; 95% confidence interval [CI], 1.004-1.051; P = 0.023) and velocity ratio (HR 1.029; 95% CI, 1.002-1.056; P = 0.035) were significantly related to recurrent territorial ischemic stroke within 1 year by univariate Cox regression, respectively with the c-statistics of 0.776 (95% CI, 0.594-0.903; P = 0.014) and 0.776 (95% CI, 0.594-0.903; P = 0.002) in receiver operating characteristic analysis.ConclusionsHemodynamics of ICAS on CFD models reconstructed from routinely obtained CTA images may predict subsequent stroke recurrence in patients with a symptomatic ICAS lesion of 70-99% luminal stenosis
3D reconstruction of cerebral blood flow and vessel morphology from x-ray rotational angiography
Three-dimensional (3D) information on blood
flow and vessel morphology is important when
assessing cerebrovascular disease and when monitoring interventions. Rotational angiography
is nowadays routinely used to determine the geometry of the cerebral vasculature. To this end,
contrast agent is injected into one of the supplying arteries and the x-ray system rotates around
the head of the patient while it acquires a sequence of x-ray images. Besides information on the
3D geometry, this sequence also contains information on blood flow, as it is possible to observe
how the contrast agent is transported by the blood. The main goal of this thesis is to exploit
this information for the quantitative analysis of blood flow.
I propose a model-based method, called
flow map fitting, which determines the blood flow
waveform and the mean volumetric flow rate in the large cerebral arteries. The method uses a
model of contrast agent transport to determine the
flow parameters from the spatio-temporal
progression of the contrast agent concentration, represented by a flow map. Furthermore, it
overcomes artefacts due to the rotation (overlapping vessels and foreshortened vessels at some
projection angles) of the c-arm using a reliability map.
For the flow quantification, small changes to the clinical protocol of rotational angiography
are desirable. These, however, hamper the standard 3D reconstruction. Therefore, a new method
for the 3D reconstruction of the vessel morphology which is tailored to this application is also presented.
To the best of my knowledge, I have presented the first quantitative results for blood flow
quantification from rotational angiography. Additionally, the model-based approach overcomes
several problems which are known from flow quantification methods for planar angiography.
The method was mainly validated on images from different phantom experiments. In most
cases, the relative error was between 5% and 10% for the volumetric mean flow rate and between
10% and 15% for the blood flow waveform. Additionally, the applicability of the flow model was shown on clinical images from planar angiographic acquisitions. From this, I conclude that the method has the potential to give quantitative estimates of blood flow parameters during
cerebrovascular interventions
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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