Prashant Athavale

Multiscale registration of real-time and prior MRI data for image-guided cardiac interventions.

Recently, there is a growing interest in using magnetic resonance imaging (MRI) to guide interventional procedures due to its excellent soft tissue contrast and lack of ionizing radiation compared to traditional radiographic guidance. One of these applications is the use of MRI to guide radio frequency ablation of anatomic substrates, within the left ventricle, responsible for ventricular tachycardia. However, different MRI acquisition schemes have significant tradeoffs between image quality and acquisition time. Guidance using high-quality preoperative 3-D MR images is limited in the case of cardiac interventions because the heart moves dynamically during the procedure. On the other hand, 2-D real-time MR images acquired during the intervention sacrifice image quality for shorter image acquisition time, leading to real-time positional updates of cardiac anatomy. Ideally, we wish to combine the advantages of live feedback from real-time images and accurate visualization of anatomical structures from preoperative images. Therefore, to improve the MRI guidance capabilities for cardiac interventions, we describe a novel multiscale rigid registration framework to correct for respiratory motion between the prior and real-time datasets. In the proposed approach, we use a weighted total variation flow algorithm to extract coarse-to-fine features from the input images and subsequently register the corresponding scales in a hierarchical manner. Registration experiments were performed with in vivo human imaging data, and the target registration error achieved was 1.51 mm. Thus, the feasibility of motion correction in an interventional setting has been demonstrated, which may lead to significant improvements in the guidance of cardiac interventions.

Multiscale properties of weighted total variation flow with applications to denoising and registration

Images consist of structures of varying scales: large scale structures such as flat regions, and small scale structures such as noise, textures, and rapidly oscillatory patterns. In the hierarchical (BV, L(2)) image decomposition, Tadmor, et al. (2004) start with extracting coarse scale structures from a given image, and successively extract finer structures from the residuals in each step of the iterative decomposition. We propose to begin instead by extracting the finest structures from the given image and then proceed to extract increasingly coarser structures. In most images, noise could be considered as a fine scale structure. Thus, starting the image decomposition with finer scales, rather than large scales, leads to fast denoising. We note that our approach turns out to be equivalent to the nonstationary regularization in Scherzer and Weickert (2000). The continuous limit of this procedure leads to a time-scaled version of total variation flow. Motivated by specific clinical applications, we introduce an image depending weight in the regularization functional, and study the corresponding weighted TV flow. We show that the edge-preserving property of the multiscale representation of an input image obtained with the weighted TV flow can be enhanced and localized by appropriate choice of the weight. We use this in developing an efficient and edge-preserving denoising algorithm with control on speed and localization properties. We examine analytical properties of the weighted TV flow that give precise information about the denoising speed and the rate of change of energy of the images. An additional contribution of the paper is to use the images obtained at different scales for robust multiscale registration. We show that the inherently multiscale nature of the weighted TV flow improved performance for registration of noisy cardiac MRI images, compared to other methods such as bilateral or Gaussian filtering. A clinical application of the multiscale registration algorithm is also demonstrated for aligning viability assessment magnetic resonance (MR) images from 8 patients with previous myocardial infarctions.

A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes

QR bar codes are prototypical images for which part of the image is a priori known (required patterns). Open source bar code readers, such as ZBar, are readily available. We exploit both these facts to provide and assess purely regularization-based methods for blind deblurring of QR bar codes in the presence of noise.

Myocardial segmentation in late-enhancement MR images via registration and propagation of cine contours

Segmentation of myocardium in Late Gadolinium Enhanced (LGE) MR images is often difficult due to accumulation of contrast agent in the infarct areas, leading to poor delineation from adjacent blood pools. Thus, manual determination of the endo-and epicardial contours is challenging, time consuming, and subject to significant intra-and inter-observer variability. In this paper, we propose to use prior information from cine images of the same patient to achieve accurate segmentation in the corresponding LGE images. The proposed method first delineates the endo-and epicardial borders in the higher quality cine images of the patients heart. Then, a robust multiscale registration framework incorporating multiscale total variation (TV) flow as a preprocessing procedure is used to align the 3D cine and 2D LGE data for the same patient. The contours from the cine images are then propagated to the LGE dataset using the same transformation. Promising results were achieved through experimental validation.

Multiscale TV flow with applications to fast denoising and registration

Medical images consist of image structures of varying scales, with different scales representing different components. For example, in cardiac images, left ventricle, myocardium and blood pool are the large scale structures, whereas infarct and noise are represented by relatively small scale structures. Thus, extracting different scales in an image i.e. multiscale image representation, is a valuable tool in medical image processing. There are various multiscale representation techniques based on different image decomposition algorithms and denoising methods. Gaussian blurring with varying standard deviation can be considered as a multiscale representation, but it diffuses the image isotropically, thereby diffusing main edges. On the other hand, inverse scale representations based on variational formulations preserve edges; but they tend to be time consuming and thus unsuitable for real-time applications. In the present work, we propose a fast multiscale representation technique, motivated by successive decomposition of smooth parts based on total variation (TV ) minimization. Thus, we smooth a given image at an increasing scale, producing a multiscale TV representation. As noise is a small scale component of an image, we can effectively use the proposed method for denoising . We also prove that the denoising speed, up to the time-step, is determined by the user, making the algorithm well-suited for real-time applications. The proposed method inherits edge preserving property from total variation flow. Using this property, we propose a novel multiscale image registration algorithm, where we register corresponding scales in images, thereby registering images efficiently and accurately.

Integro-Differential Equations Based on

A novel approach for multiscale image processing based on integro-differential equations (IDEs) was proposed in [E. Tadmor and P. Athavale, Inverse Probl. Imaging, 3 (2009), pp. 693-710]. These IDEs, which stem naturally from multiscale

(BV, L^1)

(BV,L^2)

Image Decomposition

hierarchical decompositions, yield inverse scale representations of images in the sense that the

Respiratory motion model based correction for improving the targeting accuracy of MRI-guided intracardiac electrophysiology procedures

BV

Feasibility Study of Respiratory Motion Modeling Based Correction for MRI-Guided Intracardiac Interventional Procedures

-dual norms of their residuals are inversely proportional to the scaling parameters. Motivated by the fact that