Proc. SPIE. 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
KEYWORDS: 3D acquisition, Visual process modeling, Visualization, Tissues, Magnetic resonance imaging, Image segmentation, Heart, 3D modeling, Electrophysiology, Cardiology, 3D visualizations, Electrical breakdown, Lead
Heart failure is a serious disease affecting about 23 million people worldwide. Cardiac resynchronization therapy is used to treat patients suffering from symptomatic heart failure. However, 30% to 50% of patients have limited clinical benefit. One of the main causes is suboptimal placement of the left ventricular lead. Pacing in areas of myocardial scar correlates with poor clinical outcomes. Therefore precise knowledge of the individual patient’s scar characteristics is critical for delivering tailored treatments capable of improving response rates. Current research methods for scar assessment either map information to an alternative non-anatomical coordinate system or they use the image coordinate system but lose critical information about scar extent and scar distribution. This paper proposes two interactive methods for visualizing relevant scar information. A 2-D slice based approach with a scar mask overlaid on a 16 segment heart model and a 3-D layered mesh visualization which allows physicians to scroll through layers of scar from endocardium to epicardium. These complementary methods enable physicians to evaluate scar location and transmurality during planning and guidance. Six physicians evaluated the proposed system by identifying target regions for lead placement. With the proposed method more target regions could be identified.
The paper presents a technique for detecting detecting left atrium as well as the pulmonary veins of the left
atrium by tracing out their centerlines. A vessel detection and traversal process is initiated from the venoatrial
junctions. Pulmonary veins draining into the left atrium via these junctions are thus detected, also enabling
the detection of the ostium. Ostial diameters are measured from the detected centerlines using a best-fitting
ellipse. Quantitative validation of the techniques are reported on nine patient datasets. In only two of the
datasets, mis-detections were identified. The ostial diameter measurements indicated an error of at most 5% in
most of the cases. We envisage that the techniques presented will facilitate in planning the non-pharmacological
treatment of atrial fibrillation using radio-frequency ablation therapy.
Segmentation of the left atrium is vital for pre-operative assessment of its anatomy in radio-frequency
catheter ablation (RFCA) surgery. RFCA is commonly used for treating atrial fibrillation. In this paper we
present an semi-automatic approach for segmenting the left atrium and the pulmonary veins from MR
angiography (MRA) data sets. We also present an automatic approach for further subdividing the
segmented atrium into the atrium body and the pulmonary veins. The segmentation algorithm is based on
the notion that in MRA the atrium becomes connected to surrounding structures via partial volume affected
voxels and narrow vessels, the atrium can be separated if these regions are characterized and identified. The
blood pool, obtained by subtracting the pre- and post-contrast scans, is first segmented using a region-growing
approach. The segmented blood pool is then subdivided into disjoint subdivisions based on its
Euclidean distance transform. These subdivisions are then merged automatically starting from a seed point
and stopping at points where the atrium leaks into a neighbouring structure. The resulting merged
subdivisions produce the segmented atrium. Measuring the size of the pulmonary vein ostium is vital for
selecting the optimal Lasso catheter diameter. We present a second technique for automatically identifying
the atrium body from segmented left atrium images. The separating surface between the atrium body and
the pulmonary veins gives the ostia locations and can play an important role in measuring their diameters.
The technique relies on evolving interfaces modelled using level sets. Results have been presented on 20
patient MRA datasets.