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

Automated myocardial perfusion from coronary X-ray angiography

The purpose of our study is the evaluation of an algorithm to determine the physiological relevance of a coronary\ud lesion as seen in a coronary angiogram. The aim is to extract as much as possible information from a standard\ud coronary angiogram to decide if an abnormality, percentage of stenosis, as seen in the angiogram, results in\ud physiological impairment of the blood supply of the region nourished by the coronary artery. Coronary angiography,\ud still the golden standard, is used to determine the cause of angina pectoris based on the demonstration\ud of an important stenose in a coronary artery. Dimensions of a lesion such as length and percentage of narrowing\ud can at present easily be calculated by using an automatic computer algorithm such as Quantitative Coronary\ud Angiography (QCA) techniques resulting in just anatomical information ignoring the physiological relevance of\ud the lesion. In our study we analyze myocardial perfusion images in standard coronary angiograms in rest and\ud in artificial hyperemic phases, using a drug e.g. papaverine intracoronary. Setting a Region of Interest (ROI) in\ud the angiogram without overlying major vessels makes it possible to calculate contrast differences as a function of\ud time, so called time-density curves, in the basal and hyperemic phases. In minimizing motion artifacts, end diastolic\ud images are selected ECG based in basal and hyperemic phase in an identical ROI in the same angiographic\ud projection. The development of new algorithms for calculating differences in blood supply in the region as set\ud are presented together with the results of a small clinical case study using the standard angiographic procedur

CT angiography, MR angiography and rotational digital subtraction angiography for volumetric assessment of intracranial aneurysms. An experimental study

The purpose of our experimental study was to assess the accuracy and precision of CT angiography (CTA), MR angiography (MRA) and rotational digital subtraction angiography (DSA) for measuring the volume of an in vitro aneurysm model. A rigid model of the anterior cerebral circulation harbouring an anterior communicating aneurysm was connected to a pulsatile circuit. It was studied using unenhanced 3D time-of-flight MRA, contrast-enhanced CTA and rotational DSA angiography. The source images were then postprocessed on dedicated workstations to calculate the volume of the aneurysm. CTA was more accurate than MRA (P=0.0019). Rotational DSA was more accurate than CTA, although the difference did not reach statistical significance (P=0.1605), and significantly more accurate than MRA (P<0.00001). CTA was more precise than MRA (P=0.12), although this did not reach statistical significance. Rotational DSA can be part of the diagnosis, treatment planning and support endovascular treatment of intracranial aneurysms. The emerging endovascular treatment techniques which consist of using liquid polymers as implants to exclude aneurysms from arterial circulation would certainly benefit from this precise measurement of the volume of aneurysm

autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) sequence is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.Comment: 10 pages; submitted to IEEE TM