Purpose: Contrast-enhanced MR images are widely used to confirm the adequacy of ablation margin after liver ablation for early prediction of local recurrence. However, quantitative assessment of the ablation margin by comparing pre- and post-procedural images remains challenging. We developed and tested a novel method for three-dimensional quantitative assessment of ablation margin based on non-rigid image registration and 3D distance map.
Methods: Our method was tested with pre- and post-procedural MR images acquired in 21 patients who underwent image-guided percutaneous liver ablation. The two images were co-registered using non-rigid intensity-based registration. After the tumor and ablation volumes were segmented, target volume coverage, percent of tumor coverage, and Dice similarity coefficient were calculated as metrics representing overall adequacy of ablation. In addition, 3D distance map around the tumor was computed and superimposed on the ablation volume to identify the area with insufficient margins. For patients with local recurrences, the follow-up images were registered to the post-procedural image. Three-dimensional minimum distance between the recurrence and the areas with insufficient margins was quantified.
Results: The percent tumor coverage for all nonrecurrent cases was 100 %. Five cases had tumor recurrences, and the 3D distance map revealed insufficient tumor coverage or a 0-mm margin. It also showed that two recurrences were remote to the insufficient margin.
Conclusions: Non-rigid registration and 3D distance map allow us to quantitatively evaluate the adequacy of the ablation margin after percutaneous liver ablation. The method may be useful to predict local recurrences immediately following ablation procedure.
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http://dx.doi.org/10.1007/s11548-016-1398-z | DOI Listing |
Comput Struct Biotechnol J
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IBM, New York, NY, USA.
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Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand, 248001, India.
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Hubei Key Laboratory of Vegetable Germplasm Enhancement and Genetic Improvement, Institute of Industrial Crops, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.
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January 2025
School of Information, Yunnan University, Kunming, 650504, China.
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View Article and Find Full Text PDFJ Vis
January 2025
Department of Cognitive Sciences and Neurobiology and Behavior, University of California, Irvine, California, USA.
A salience map is a topographic map that has inputs at each x,y location from many different feature maps and summarizes the combined salience of all those inputs as a real number, salience, which is represented in the map. Of the more than 1 million Google references to salience maps, nearly all use the map for computing the relative priority of visual image components for subsequent processing. We observe that salience processing is an instance of substance-invariant processing, analogous to household measuring cups, weight scales, and measuring tapes, all of which make single-number substance-invariant measurements.
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