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

Stem Cell Imaging: Tools to Improve Cell Delivery and Viability.

Stem cell therapy (SCT) has shown very promising preclinical results in a variety of regenerative medicine applications. Nevertheless, the complete utility of this technology remains unrealized. Imaging is a potent tool used in multiple stages of SCT and this review describes the role that imaging plays in cell harvest, cell purification, and cell implantation, as well as a discussion of how imaging can be used to assess outcome in SCT. We close with some perspective on potential growth in the field

The Integration of Positron Emission Tomography With Magnetic Resonance Imaging

A number of laboratories and companies are currently exploring the development of integrated imaging systems for magnetic resonance imaging (MRI) and positron emission tomography (PET). Scanners for both preclinical and human research applications are being pursued. In contrast to the widely distributed and now quite mature PET/computed tomography technology, most PET/MRI designs allow for simultaneous rather than sequential acquisition of PET and MRI data. While this offers the possibility of novel imaging strategies, it also creates considerable challenges for acquiring artifact-free images from both modalities. This paper discusses the motivation for developing combined PET/MRI technology, outlines the obstacles in realizing such an integrated instrument, and presents recent progress in the development of both the instrumentation and of novel imaging agents for combined PET/MRI studies. The performance of the first-generation PET/MRI systems is described. Finally, a range of possible biomedical applications for PET/MRI are outlined

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

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Hybrid Image Visualization Tool for 3D integration of CT coronary anatomy and quantitative myocardial perfusion PET

Purpose: Multimodal cardiac imaging by CTA and quantitative PET enables acquisition of patient-specific coronary anatomy and absolute myocardial perfusion at rest and during stress. In the clinical setting, integration of this information is performed visually or using coronary arteries distribution models. We developed a new tool for CTA and quantitative PET integrated 3D visualization, exploiting XML and DICOM clinical standards. Methods: The Hybrid Image Tool (HIT) developed in the present study included four main modules: (1) volumetric registration for spatial matching of CTA and PET datasets, (2) an interface to PET quantitative analysis software, (3) a derived DICOM generator able to build DICOM dataset from quantitative polar maps, and (4) a 3D visualization tool of integrated anatomical and quantitative flow information. The four modules incorporated in the HIT tool communicate by defined standard XML files: XML-transformation and XML MIST standards. Results: The HIT tool implements a 3D representation of CTA showing real coronary anatomy fused to PET derived quantitative myocardial blood flow distribution. The technique was validated on 16 datasets from EVINCI study population. The validation of the method confirmed the high matching between "original" and derived datasets as well as the accuracy of the registration procedure. Conclusions: Three-dimensional integration of patient-specific coronary artery anatomy provided by CTA and quantitative myocardial blood flow obtained from PET imaging can improve cardiac disease assessment. The HIT tool introduced in this paper may represent a significant advancement in the clinical use of this multimodal approach

An integrated MR/PET system: prospective applications

Radiology is strongly depending on medical imaging technology and consequently directing technological progress. A novel technology can only be established, however, if improved diagnostic accuracy influence on therapeutic management and/or overall reduced cost can be evidenced. It has been demonstrated recently that Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) can technologically be integrated into one single hybrid system. Some scientific arguments on the benefits are obvious, e.g., that simultaneous imaging of morphological and functional information will improve tissue characterization. However, crossfire of questions still remains: What unmet radiological needs are addressed by the novel system? What level of hardware integration is reasonable, or would software-based image co-registration be sufficient? Will MR/PET achieve higher diagnostic accuracy compared to separate imaging? What is the added value compared to other hybrid imaging modalities like PET/CT? And finally, is the system economically reasonable and has the potential to reduce overall costs for therapy planning and monitoring? This article tries to highlight some perspectives of applying an integrated MR/PET system for simultaneous morphologic and functional imaging