Deformable multimodal registration for navigation in beating-heart cardiac surgery

Peoples, Jacob; Bisleri, Gianluigi; Ellis, Randy E.

doi:10.1007/s11548-019-01932-2

Cited by 11 publications

(1 citation statement)

References 39 publications

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“…Tracking Registration [22] Yes No ICP [23] 3D Slicer OTS Surface-matching [24] Learning-based No ICP [25] No Learning-based Learning-based [26] Learning-based No No [27,28] Yes OTS Fiducial markers [29] Threshold/Region growing OTS ICP [30] Region growing Visual-inertial stereo slam ICP [31] Threshold Learning-based Super4PCS [32] [33] Yes OTS ICP [34] 3D Slicer EMT Fiducial markers [35] Mimics 6 OTS Anatomical landmark [36] Manual No ICP [37] No Depth estimation Learning-based [38] [39] EndoSize 6 No Rigid intensity-based [40] Yes EMT ICP [41] Threshold OTS ICP [42] 3D Slicer/Learning-based No ICP and CPD 5 [43] Yes Visual SLAM Visual SLAM 1 EMT: electromagnetic tracker. 2 PDM: Philips Disease Management.…”

Section: Paper Segmentationmentioning

confidence: 99%

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

Lin,

Lei,

Yang

2023

Sensors

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Medical image analysis forms the basis of image-guided surgery (IGS) and many of its fundamental tasks. Driven by the growing number of medical imaging modalities, the research community of medical imaging has developed methods and achieved functionality breakthroughs. However, with the overwhelming pool of information in the literature, it has become increasingly challenging for researchers to extract context-relevant information for specific applications, especially when many widely used methods exist in a variety of versions optimized for their respective application domains. By being further equipped with sophisticated three-dimensional (3D) medical image visualization and digital reality technology, medical experts could enhance their performance capabilities in IGS by multiple folds. The goal of this narrative review is to organize the key components of IGS in the aspects of medical image processing and visualization with a new perspective and insights. The literature search was conducted using mainstream academic search engines with a combination of keywords relevant to the field up until mid-2022. This survey systemically summarizes the basic, mainstream, and state-of-the-art medical image processing methods as well as how visualization technology like augmented/mixed/virtual reality (AR/MR/VR) are enhancing performance in IGS. Further, we hope that this survey will shed some light on the future of IGS in the face of challenges and opportunities for the research directions of medical image processing and visualization.

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Section: Paper Segmentationmentioning

confidence: 99%

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

Lin,

Lei,

Yang

2023

Sensors

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Composition of Transformations in the Registration of Sets of Points or Oriented Points

Peoples

Ellis

2020

Shape in Medical Imaging

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Impact of cardiac and respiratory motion on the 3D accuracy of image-guided interventions on monoplane systems

et al. 2023

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Purpose Image-guided intervention (IGI) systems have the potential to increase the efficiency in interventional cardiology but face limitations from motion. Even though motion compensation approaches have been proposed, the resulting accuracy has rarely been quantified using in vivo data. The purpose of this study is to investigate the potential benefit of motion-compensation in IGS systems. Methods Patients scheduled for left atrial appendage closure (LAAc) underwent pre- and postprocedural non-contrast-enhanced cardiac magnetic resonance imaging (CMR). According to the clinical standard, the final position of the occluder device was routinely documented using x-ray fluoroscopy (XR). The accuracy of the IGI system was assessed retrospectively based on the distance of the 3D device marker location derived from the periprocedural XR data and the respective location as identified in the postprocedural CMR data. Results The assessment of the motion-compensation depending accuracy was possible based on the patient data. With motion synchronization, the measured accuracy of the IGI system resulted similar to the estimated accuracy, with almost negligible distances of the device marker positions identified in CMR and XR. Neglection of the cardiac and/or respiratory phase significantly increased the mean distances, with respiratory motion mainly reducing the accuracy with rather low impact on the precision, whereas cardiac motion decreased the accuracy and the precision of the image guidance. Conclusions In the presented work, the accuracy of the IGI system could be assessed based on in vivo data. Motion consideration clearly showed the potential to increase the accuracy in IGI systems. Where the general decrease in accuracy in non-motion-synchronized data did not come unexpected, a clear difference between cardiac and respiratory motion-induced errors was observed for LAAc data. Since sedation and intervention location close to the large vessels likely impacts the respiratory motion contribution, an intervention-specific accuracy analysis may be useful for other interventions.

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Deformable multimodal registration for navigation in beating-heart cardiac surgery

Cited by 11 publications

References 39 publications

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

Composition of Transformations in the Registration of Sets of Points or Oriented Points

Impact of cardiac and respiratory motion on the 3D accuracy of image-guided interventions on monoplane systems

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