Validation of an automated method to quantify stress-induced ischemia and infarction in rest-stress myocardial perfusion SPECT.

Myocardial perfusion SPECT (MPS) is one of the frequently used methods for quantification of perfusion defects in patients with known or suspected coronary artery disease. This article describes open access software for automated quantification in MPS of stress-induced ischemia and infarction and provides phantom and in vivo validation

Data registration and fusion for cardiac applications

The registration and fusion of information from multiple cardiac image modalities such as magnetic resonance imaging (MRI), X-ray computed tomography (CT), positron emission tomography (PET) and single photon emission computed tomography (SPECT) has been of increasing interest to the medical community as tools for furthering physiological understanding and for diagnostic of ischemic heart diseases. Ischemic heart diseases and their consequence, myocardial infarct, are the leading cause of mortality in industrial countries. In cardiac image registration and data fusion, the combination of structural information from MR images and functional information from PET and SPECT is of special interest in the estimation of myocardial function and viability. Cardiac image registration is a more complex problem than brain image registration. The non-rigid motion of the heart and the thorax structures introduce additional difficulties in registration. In this thesis the goal was develop methods for cardiac data registration and fusion. A rigid registration method was developed to register cardiac MR and PET images. The method was based on the registration of the segmented thorax structures from MR and PET transmission images. The thorax structures were segmented from images using deformable models. A MR short axis registration with PET emission image was also derived. The rigid registration method was evaluated using simulated images and clinical MR and PET images from ten patients with multivessel coronary artery diseases. Also an elastic registration method was developed to register intra-patient cardiac MR and PET images and inter-patient head MR images. In the elastic registration method, a combination of mutual information, gradient information and smoothness of transformation was used to guide the deformation of one image towards another image. An approach for the creation of 3-D functional maps of the heart was also developed. An individualized anatomical heart model was extracted from the MR images. A rigid registration of anatomical MR images and PET metabolic images was carried out using surface based registration, and the registration of MR images with magnetocardiography (MCG) data using external markers. The method resulted in a 3-D anatomical and functional model of the heart that included structural information from the MRI and functional information from the PET and MCG. Different error sources in the registration method of the MR images and MCG data was also evaluated in this thesis. The results of the rigid MR-PET registration method were also used in the comparison of multimodality MR imaging methods to PET.reviewe

Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms

Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets

Ultrasound and computed tomography cardiac image registration

As the trend of the medical intervention moves towards becoming minimally invasive, the role of medical imaging has grown increasingly important. Medical images acquired from a variety of imaging modalities require image preprocessing, information extraction and data analysis algorithms in order for the potentially useful information to be delivered to clinicians so as to facilitate better diagnosis, treatment planning and surgical intervention. This thesis investigates the employment of an affine registration method to register the pre-operative Computed Tomography (CT) and intra-operative Ultrasound cardiac images. The main benefit of registering Ultrasound and CT cardiac images is to compensate the weaknesses and combine the advantages from both modalities. However, the multimodal registration is a complex and challenging task since there is no specific relationship between the intensity values of the corresponding pixels. Image preprocessing methods such as image denoising, edge detection and contour delineation are implemented to obtain the salient and significant features before the registration process. The features-based Scale Invariant Feature Transform (SIFT) method and homography transformation are then applied to find the transformation that aligns the floating image to the reference image. The registration results of three different patient datasets are assessed by the objective performance measures to ensure that the clinically meaningful result are obtained. Furthermore, the relationship between the preoperative CT image and the transformed intra-operative Ultrasound image are evaluated using joint histogram, MI and NMI. Although the proposed framework falls slightly short of achieving the perfect compensation of cardiac movements and deformation, it can be legitimately implemented as an initialisation step for further studies in dynamic and deformable cardiac registration

Computer image registration techniques applied to nuclear medicine images

Modern medicine has been using imaging as a fundamental tool in a wide range of applications. Consequently, the interest in automated registration of images from either the same or different modalities has increased. In this chapter, computer techniques of image registration are reviewed, and cover both their classification and the main steps involved. Moreover, the more common geometrical transforms, optimization and interpolation algorithms are described and discussed. The clinical applications examined emphases nuclear medicine

Spatio-Temporal Nonrigid Registration for Ultrasound Cardiac Motion Estimation

We propose a new spatio-temporal elastic registration algorithm for motion reconstruction from a series of images. The specific application is to estimate displacement fields from two-dimensional ultrasound sequences of the heart. The basic idea is to find a spatio-temporal deformation field that effectively compensates for the motion by minimizing a difference with respect to a reference frame. The key feature of our method is the use of a semi-local spatio-temporal parametric model for the deformation using splines, and the reformulation of the registration task as a global optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy to obtain a higher speed and robustness. We evaluated the accuracy of our algorithm using a synthetic sequence generated with an ultrasound simulation package, together with a realistic cardiac motion model. We compared our new global multiframe approach with a previous method based on pairwise registration of consecutive frames to demonstrate the benefits of introducing temporal consistency. Finally, we applied the algorithm to the regional analysis of the left ventricle. Displacement and strain parameters were evaluated showing significant differences between the normal and pathological segments, thereby illustrating the clinical applicability of our method

Analysis of cardiac motion using MRI and nonrigid image registration

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