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Spatiotemporal registration and fusion of transthoracic echocardiography and volumetric coronary artery tree

Ghodsizad, T ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1007/s11548-021-02421-1
  3. Publisher: Springer Science and Business Media Deutschland GmbH , 2021
  4. Abstract:
  5. Purpose: Cardiac multimodal image fusion can offer an image with various types of information in a single image. Many coronary stenosis, which are anatomically clear, are not functionally significant. The treatment of such kind of stenosis can cause irreversible effects on the patient. Thus, choosing the best treatment planning depend on anatomical and functional information is very beneficial. Methods: An algorithm for the fusion of coronary computed tomography angiography (CCTA) as an anatomical and transthoracic echocardiography (TTE) as a functional modality is presented. CCTA and TTE are temporally registered using manifold learning. A pattern search optimization algorithm, using normalized mutual information, is used to find the best match slice to TTE frame from CCTA volume. By employing a free-form deformation, the heart's non-rigid deformations are modeled. The spatiotemporal registered TTE frame is embedded to achieve the fusion result. Results: The accuracy is evaluated on CCTA and TTE data obtained from 10 patients. In temporal registration, mean absolute error of 1.97 ± 1.23 is resulted from comparing the output frame numbers from the algorithm and from manual assignment by an expert. In spatial registration, the accuracy of the similarity between the best match slice from CCTA volume and TTE frame is resulted in 1.82 ± 0.024 mm, 6.74 ± 0.013 mm, and 0.901 ± 0.0548 due to mean absolute distance, Hausdorff distance, and Dice similarity coefficient, respectively. Conclusion: Without the use of ECG and Optical tracking systems, a semiautomatic framework of spatiotemporal registration and fusion of CCTA volume and TTE frame is presented. The experimental results showed the effectiveness of our proposed method to create complementary information from TTE and CCTA, which may help in the early diagnosis and effective treatment of cardiovascular diseases (CVDs). © 2021, CARS
  6. Keywords:
  7. aAdult ; Affine transform ; Clinical article ; Computed tomographic angiography ; Coronary artery ; Coronary artery obstruction ; Dyspnea ; Female ; Heart ; Heart cycle ; Heart left ventricle ; Heart right ventricle ; Human ; Hypertension ; Image processing ; Locally linear embedding ; Male ; Manifold learning ; Middle aged ; Pulmonary artery ; Rib ; RR interval ; Spinal cord ; Sternum ; Thorax pain ; Transthoracic echocardiography ; Treatment planning ; Algorithm ; Coronary angiography ; Coronary blood vessel ; Diagnostic imaging ; Tree ; Algorithms ; Computed Tomography Angiography ; Coronary Angiography ; Coronary Vessels ; Echocardiography ; Humans ; Trees
  8. Source: International Journal of Computer Assisted Radiology and Surgery ; Volume 16, Issue 9 , 2021 , Pages 1493-1505 ; 18616410 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s11548-021-02421-1