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

MR/CT image fusion of the spine after spondylodesis: a feasibility study

The objective of this study is to evaluate feasibility, accuracy and time requirements of MR/CT image fusion of the lumbar spine after spondylodesis. Sagittal MR and CT images derived from standard imaging protocols (sagittal T2-weighted MR/sagittal reformatted multi-planar-reformation of the CT) of the lumbar spine with correct (n=5) and incorrect (n=5) implant position were fused by two readers (R1, R2) using OsiriX in two sessions placing one (session 1) or two (session 2) reference point(s) on the dorsal tip(s) of the cranial and caudal endplates from the second lumbar to the first sacral vertebra. R1 was an experienced musculoskeletal radiologist; R2 a spine surgeon, both had received a short training on the software tool. Fusion times and fusion accuracy, defined as the largest deviation between MR and CT in the median sagittal plane on the ventral tip of the cranial end plate of the most cranial vertebra visible on the CT, were measured in both sessions. Correct or incorrect implant position was evaluated upon the fused images for all patients by an experienced senior staff musculoskeletal radiologist. Mean fusion time (session 1/session 2; in seconds) was 100.4/95 (R1) and 104.2/119.8 (R2). Mean fusion deviation (session 1/session 2; in mm) was 1.24/2.20 (R1) and 0.79/1.62 (R2). The correct/incorrect implant position was identified correctly in all cases. In conclusion, MR/CT image fusion of the spine with metallic implants is feasible, fast, accurate and easy to implement in daily routine wor

Data Fusion of Left Ventricle Electro-Anatomic Mapping and Multislice Computerized Tomography for Cardiac Resynchronisation Therapy Optimization

Cardiac Resynchronization Therapy is a treatment for bi-ventricular asynchronism. It can be optimized by the identification of the most effective pacing sites. The aim of this study is to provide a helpful tool to perform this identification by the fusion of electrical and anatomical information resulting from Electro-Anatomic Mapping (EAM) data and Multislice Computerized Tomography (MSCT) imaging. EAM data provide an approximation of the left ventricle (LV) 3D-surface (SEAM). Left cardiac chambers are segmented from MSCT imaging and surfaces are reconstructed (SCT). In order to represent this information in a unified framework, a three steps method is proposed: (1) the LV is separated from the left auricle on SCT providing S ′ CT; (2) a semi-automatic rigid registration method; (3) activation time delays is applied to SEAM and S ′ CT are estimated on S ′ CT from the EAM data. This method results in a graphical interface offering to clinicians means to identify abnormal electrical activity sites

Simultaneous segmentation of the left and right heart ventricles in 3D cine MR images of small animals

New high resolution image techniques allow to capture the anatomy and movement of the heart of small animals. The availability of these in vivo images can be very useful for medical research, however the amount of generated data for large animal studies makes manual analysis a very tedious task. To cope with the problem of automatic analysis of these images, we propose the use of the Deformable Elastic Template method to perform automatic segmentation of the ventricles. To adapt the method to the specificities of high-resolution MRI, several improvements are presented, including an image-context dependent scheme for more robust segmentation. Qualitative results show that our method is able to correctly retrieve the heart’s contours in 3D. 1

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

Analysis of cardiac motion using MRI and nonrigid image registration

Imperial Users onl

Magnetocardiography in unshielded location in coronary artery disease detection using computerized classification of current density vectors maps

The aim of this was to examine the capability of magnetocardiographic mapping at rest using a simple system, installed in unshielded location, to detect myocardial ischemia in a heterogeneous CAD population including subsets of patients with ischemia in any of the main coronary artery branches regions but without prior myocardial infarction and with normal 12-leads ECG at rest. A total of 110 patients with CAD and 98 healthy controls were included in the study. Another 15 volunteers with no history of any cardiovascular disease were used for run-torun and day-to-day reproducibility assessment. MCG recordings were taken with the help of four-channel SQUID-magnetometer system , installed in unshielded setting, at 36 pre-thoracic sites over the pericardial area. For further analysis the reconstruction of CDV maps within the ST-T interval was applied. Each CDV map was classified automatically by means of a classification system with a scale from 0 to 4 with increasing abnormality . The averaged class was calculated for each subject in order to discriminate between groups. The results showed that CDV maps of healthy volunteers have mainly dipolar structure with only one area of larger vectors directed left – downwards. In contrast the CDV maps of patients with CAD demonstrate mainly non-dipolar structure with additional areas of larger current vectors. With the threshold 1,75 of average map class sensitivity 74% , specificity 80%, PPV 81 %, NPV 73,5 % were achieved. The parameters tested had low disagreement between repeated measurements. Relatively simple 4-channals system, installed in unshielded location, was robust in operation, signal quality was good enough for the analysis. Results were discussed

A novel SPECT camera for molecular imaging of the prostate

Citation: Proc. SPIE 8143, Medical Applications of Radiation Detectors, 814305 (September 14, 2011); doi:10.1117/12.896235The objective of this work is to develop an improved SPECT camera for dedicated prostate imaging. Complementing the recent advancements in agents for molecular prostate imaging, this device has the potential to assist in distinguishing benign from aggressive cancers, to improve site-specific localization of cancer, to improve accuracy of needle-guided prostate biopsy of cancer sites, and to aid in focal therapy procedures such as cryotherapy and radiation. Theoretical calculations show that the spatial resolution/detection sensitivity of the proposed SPECT camera can rival or exceed 3D PET and further signal-to-noise advantage is attained with the better energy resolution of the CZT modules. Based on photon transport simulation studies, the system has a reconstructed spatial resolution of 4.8 mm with a sensitivity of 0.0001. Reconstruction of a simulated prostate distribution demonstrates the focal imaging capability of the system

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples despite their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. To help the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis. Specifically, we introduce the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modelling frameworks: diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging.Comment: Second revision: including more papers and further discussion