Cardiac Resynchronization Therapy (CRT) can effectively treat left ventricle (LV) driven Heart Failure (HF). However, 30% of the CRT recipients do not experience symptomatic benefit. Recent studies show that the CRT response rate can reach 95% when the LV pacing lead is placed at an optimal site at a region of maximal LV dyssynchrony and away from myocardial scars. Cardiac Magnetic Resonance (CMR) can identify the optimal site in three dimensions (3D). 3D CMR data can be registered to clinical standard x-ray fluoroscopy to achieve an optimal pacing of the LV. We have developed a 3D CMR to 2D x-ray image registration method for CRT procedures. We have employed the LV pacing lead on x-ray images and coronary sinus on MR data as landmarks. The registration method makes use of a guidewire simulation algorithm, edge based image registration technique and x-ray C-arm tracking to register the coronary sinus and pacing lead landmarks.

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.

Cardiac image registration is drawing attention for a range of merits in integrating and enhancing real-time (RT) images using a priori and complementary images of the myocardium, which might additionally be captured from other modalities. Myocardial stem cell delivery and radio-frequency ablation are some of the cases that could benefit from RT registration of high quality images. Unfortunately, most of these applications are of long duration, and must account in some manner for respiratory motion. Moreover, registration is not so keen as to compensate for these motions. Time series prediction techniques could compensate this shortcoming by proposing future approximate displacements caused by respiratory motion. In this study, we propose a three-stage framework for RT 2D into 3D cardiac image registration during respiration, composed of prior registration to extract the trend of respiratory motion and to calibrate a set of time series predictors for future motion prediction, as well RT registration to update estimated transform parameters. The proposed approach was validated in the course of four simulations and shows acceptable results for clinical circumstances.

Background LV intraventricular dyssynchrony is commonly assessed by systolic dyssynchrony index (SDI), which is defined by the dispersion of time of minimum regional cavity volume. Several studies have reported conflicting results on cardiac resynchronization therapy (CRT) responses based on SDI values. In this study, we aim to characterise SDI distributions in asymptomatic patients and to analyse how particular regions affect the global SDI measurement in different patient groups.

Background Previously a myocardial tissue classification algorithm has been developed to locate and quantify infarct in a given myocardial region-of-interest specified on late gadolinium enhancement (LGE) MR images [1]. To complete the automation requires an endocardial and epicardial contour detection algorithm to replace the current practice of manual contouring that is time-consuming and subject to intraand inter-observer variability. Challenges include: 1) the intensity inhomogeneity of both the healthy and infarct myocardium; 2) the existence of an infarct on a given slice is not known a priori; 3) a subendocardial infarct region’s boundary can be easily mistaken for the endocardial contour due to the proximity and strength of the edge (gradient); and 4) incorporating prior anatomical information (e.g., cine steady-state free precession (SSFP) MRI) while allowing for possible motion between separate studies.

RATIONALE AND OBJECTIVES:A fully automated left ventricle segmentation method for the functional analysis of cine short axis (SAX) magnetic resonance (MR) images was developed, and its performance evaluated with 133 studies of subjects with diverse pathology: ischemic heart failure (n=34), non-ischemic heart failure (n=30), hypertrophy (n=32), and healthy (n=37). MATERIALS AND METHODS:The proposed automatic method locates the left ventricle (LV), then for each image detects the contours of the endocardium, epicardium, papillary muscles and trabeculations. Manually and automatically determined contours and functional parameters were compared quantitatively. RESULTS:There was no significant difference between automatically and manually determined end systolic volume (ESV), end diastolic volume (EDV), ejection fraction (EF) and left ventricular mass (LVM) for each of the four groups (paired sample t-test, α=0.05). The automatically determined functional parameters showed high correlations with those derived from manual contours, and the Bland-Altman analysis biases were small (1.51 mL, 1.69 mL, -0.02%, -0.66 g for ESV, EDV, EF and LVM, respectively). CONCLUSIONS:The proposed technique automatically and rapidly detects endocardial, epicardial, papillary muscles' and trabeculations' contours providing accurate and reproducible quantitative MRI parameters, including LV mass and EF.

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.

Background Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. Methods The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Results Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. Conclusions The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface.

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 patient's 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.

In this work, we propose a semi-automated myocardial infarction quantification method for cardiac contrast delayed enhancement magnetic resonance images (DE-MRI). Advantages of this method include that it reduces manual contouring of the left ventricle, obviates a remote myocardium region, and automatically distinguishes infarct, healthy and heterogeneous ("gray zone") tissue despite variability in intensity and noise across images. Quantitative evaluation results showed that the automatically determined infarct core and gray zone size have high correlation with that derived from the averaged results of the manual full width at half maximum (FWHM) methods (R(2)=0.99 for infarct core and gray zone size). Compared with the manual method, a much better reproducibility was achieved with the proposed algorithm and it shortens the evaluation time to one second per image, compared with 2-5 min per image for the manual method.

Guidance of electrophysiological (EP) procedures by magnetic resonance imaging (MRI) has significant advantages over x-ray fluoroscopy. Display of electroanatomic mapping (EAM) during an intervention fused with a prior MR volume and DE-MRI derived tissue classification should improve the accuracy of cardiac resynchronization therapy (CRT) for ventricular arrhythmias. Improved accuracy in the spatial localization of recorded EP points will produce an EAM to constrain and customize patient-specific cardiac electroanatomic models being developed for understanding the patterns of arrhythmogenic slow conduction zones causing reentry circuits and treatment planning. The Vurtigo software presented here is a four dimensional (3D+time) real-time visualization application for guiding interventions capable of displaying prior volumes, real-time MRI scan planes, EAM (voltage or activation times), segmented models, and tracked catheters. This paper will describe the architecture and features of Vurtigo followed by the application example of guiding percutaneous cardiac electroanatomic mapping in porcine models.

Background Two popular methods for determining the threshold values for the infarct core and gray zone on delayed enhancement MR images (DE-MRI) have been proposed previously: full width and half maximum (FWHM) and standard deviation (SD) methods [1]. Major limitations of these methods are:1) three manually drawn contours are needed for endocardial, epicardial and remote myocardium boundaries, which is time consuming and suffers from inter-observer and intra-observer variability; 2) the difficulty in reproducible manual delineation of remote myocardium, is an important contributor to variability in results; and 3) the dependence on the remote region statistics is problematic due to the low SNR of this region [2]. The purpose of this research was to develop a novel algorithm for segmentation of infarct core and gray zone from conventional IR-GRE shortaxis MR images with highly robust and reproducible results comparable to the FWHM analysis while eliminating the requirement for drawing a remote myocardial region.

To quantitatively analyze global and regional cardiac function from MR, clinical parameters such as ejection fraction (EF) and volumes are required. These depend upon accurate delineation of endo- and epicardial contours of the left ventricle (LV). Previous work [1] has demonstrated the difficulty of accurate LV segmentation, especially in basal slices where the LV outflow tract (LVOT) interrupts continuous myocardial contours. A novel method for the robust, accurate and fully automatic LV segmentation from short axis (SA) cine MR images is presented in this study that applies watershed technique to solve basal slice segmentation.

The presence of ischemia-induced microvascular obstruction (MVO) despite successful coronary revascularization, has been associated with poor functional recovery and adverse left ventricular remodeling after acute myocardial infarction (AMI). Additionally, the extent of the infarct gray zone is a strong independent predictor of post-AMI mortality. However, the evolution of gray zone after AMI has not been investigated and its relationship with MVO in terms of risk stratification is unknown.

In this work, we propose an automated infarct heterogeneity analysis method for cardiac delayed enhancement magnetic resonance images (DE-MRI). Advantages of this method include that it eliminates manual contouring of the left ventricle and automatically distinguishes infarct, "gray zone" (heterogeneous mixture of healthy and infarct tissue), and healthy tissue pixels despite variability in intensity and noise across images. Quantitative evaluation was performed on 12 patients. The automatically determined infarct core size and gray zone size showed high correlation with that derived from manual delineation (R2 = 0.91 for infarct core size and R2 = 0.87 for gray zone size). The automatic method shortens the evaluation to 5.6 ±2.2 s per image, compared with 3 min for the manual method. These results indicate a promising method for automatic analysis of infarct heterogeneity with DE-MRI that should be beneficial for reducing variability in quantitative analysis and improving workflow.

Benjamin P. Grabow, Walter F. Block, Andy L. Alexander, Samuel A. Hurley, Chris D. Ross, Karl A. Sillay, Juan M. Santos, William R. Overall, Perry E. Radau, Graham A. Wright, and Ethan K. Brodsky University of Wisconsin, Madison, WI, United States, Engineering Resources Group, Inc., Pembroke Pines, FL, United States, HeartVista Inc., Palo Alto, CA, United States, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

Introduction To quantitatively analyze global and regional cardiac function from MR, clinical parameters such as ejection fraction (EF) and volumes are required. These depend upon accurate delineation of endoand epicardial contours of the left ventricle (LV). Accurate LV segmentation is acknowledged as a difficult problem because of the overlap between the intensity distributions within the cardiac regions, the lack of edge information, and the intersubject variability. A novel method for the robust, accurate and fully automatic LV segmentation from short axis (SA) cine MR images is presented in this study.