Preoperative Systems for Computer Aided Diagnosis based on Image Registration: Applications to Breast Cancer and Atherosclerosis

Computer Aided Diagnosis (CAD) systems assist clinicians including radiologists and cardiologists to detect abnormalities and highlight conspicuous possible disease. Implementing a pre-operative CAD system contains a framework that accepts related technical as well as clinical parameters as input by analyzing the predefined method and demonstrates the prospective output. In this work we developed the Computer Aided Diagnostic System for biomedical imaging analysis of two applications on Breast Cancer and Atherosclerosis. The aim of the first CAD application is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. Base on the fact that automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70±0.03, 0.74±0.03 and 0.81±0.09 (out of 1) for Affine, BSpline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation. The aim of the second implemented CAD application is to assess contribution of calcification in plaque vulnerability and wall rupture and to find its maximum resistance before break in image-based models of carotid artery stenting. The role of calcification inside fibroatheroma during carotid artery stenting operation is controversial in which cardiologists face two major problems during the placement: (i) “plaque protrusion” (i.e. elastic fibrous caps containing early calcifications that penetrate inside the stent); (ii) “plaque vulnerability” (i.e. stiff plaques with advanced calcifications that break the arterial wall or stent). Finite Element Analysis was used to simulate the balloon and stent expansion as a preoperative patient-specific virtual framework. A nonlinear static structural analysis was performed on 20 patients acquired using in vivo MDCT angiography. The Agatston Calcium score was obtained for each patient and subject-specific local Elastic Modulus (EM) was calculated. The in silico results showed that by imposing average ultimate external load of 1.1MPa and 2.3MPa on balloon and stent respectively, average ultimate stress of 55.7±41.2kPa and 171±41.2kPa are obtained on calcifications. The study reveals that a significant positive correlation (R=0.85, p<0.0001) exists on stent expansion between EM of calcification and ultimate stress as well as Plaque Wall Stress (PWS) (R=0.92, p<0.0001), comparing to Ca score that showed insignificant associations with ultimate stress (R=0.44, p=0.057) and PWS (R=0.38, p=0.103), suggesting minor impact of Ca score in plaque rupture. These average data are in good agreement with results obtained by other research groups and we believe this approach enriches the arsenal of tools available for pre-operative prediction of carotid artery stenting procedure in the presence of calcified plaques

Rinnan lämpökuvien aikasarjojen stabilointi

Dynamic infrared imaging (DIRI) is an emerging technology for the early detection of breast cancer. In this method time-series of thermal breast images are obtained. The patient motion in the time-series can distort the DIRI analysis in such a way that the detection of breast cancer becomes impossible. Image registration can be used to eliminate the patient motion from the time-series data. In this thesis, two different registration algorithms were tested: Thirion's demons algorithm and an algorithm based on an affine transformation. Furthermore, a combined method where the affine method is used as a pre-registration step for the demons method was tested. The algorithms were implemented with Matlab and their performance in the task of registering a time-series of thermal breast images was evaluated using four different performance metrics. The registration algorithms were implemented for time-series data of 20 healthy (no malignant lesions) subjects. The demons method outperformed the affine method and is recommended as a suitable tool for time-series registration of thermal breast images. The combined method achieved slightly improved results compared to the demons method but with significantly increased computation time.Dynaaminen lämpökuvantaminen on lupaava menetelmä rintasyövän aikaiseen havaitsemiseen. Menetelmässä rinnoista otetaan lämpökuvien aikasarja. Kuvantamisen aikana tapahtuva potilaan liike voi vaikeuttaa aikasarjan analysointia niin, että rintasyövän tunnistaminen ei ole mahdollista. Liike voidaan poistaa aikasarjasta kuvastabiloinnin avulla. Tässä työssä tutkittiin kahta kuvastabilointiin kehitettyä algoritmia: Thirionin demons-algoritmia ja algoritmia, joka perustuu affiiniin muunnokseen. Lisäksi tutkittiin yhdistettyä menetelmää, jossa affiinia menetelmää käytetään esiaskeleena demons-menetelmälle. Algoritmien laskenta toteutettiin Matlabilla. Algoritmien tuottaman tuloksen laatua arvioitiin neljällä erillisellä laatumittarilla. Testidatana käytettiin aikasarjoja, jotka oli kuvattu 20:stä terveestä (ei pahanlaatuisia kasvaimia) potilaasta. Demons-menetelmä osoittautui affiinia menetelmää paremmaksi. Demons-menetelmää voidaan suositella rintojen lämpökuvien aikasarjojen stabilointiin. Yhdistetty menetelmä tuotti hiukan parempia tuloksia kuin demons-menetelmä, mutta vaati huomattavasti enemmän laskenta-aikaa

Nonrigid Registration of Brain Tumor Resection MR Images Based on Joint Saliency Map and Keypoint Clustering

This paper proposes a novel global-to-local nonrigid brain MR image registration to compensate for the brain shift and the unmatchable outliers caused by the tumor resection. The mutual information between the corresponding salient structures, which are enhanced by the joint saliency map (JSM), is maximized to achieve a global rigid registration of the two images. Being detected and clustered at the paired contiguous matching areas in the globally registered images, the paired pools of DoG keypoints in combination with the JSM provide a useful cluster-to-cluster correspondence to guide the local control-point correspondence detection and the outlier keypoint rejection. Lastly, a quasi-inverse consistent deformation is smoothly approximated to locally register brain images through the mapping the clustered control points by compact support radial basis functions. The 2D implementation of the method can model the brain shift in brain tumor resection MR images, though the theory holds for the 3D case