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Pohlman RM, Turney MR, Wu PH, Brace CL, Ziemlewicz TJ, Varghese T. Two-dimensional ultrasound-computed tomography image registration for monitoring percutaneous hepatic intervention. Med Phys 2019; 46:2600-2609. [PMID: 31009079 DOI: 10.1002/mp.13554] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 04/14/2019] [Accepted: 04/15/2019] [Indexed: 01/03/2023] Open
Abstract
PURPOSE Deformable registration of ultrasound (US) and contrast enhanced computed tomography (CECT) images are essential for quantitative comparison of ablation boundaries and dimensions determined using these modalities. This comparison is essential as stiffness-based imaging using US has become popular and offers a nonionizing and cost-effective imaging modality for monitoring minimally invasive microwave ablation procedures. A sensible manual registration method is presented that performs the required CT-US image registration. METHODS The two-dimensional (2D) virtual CT image plane that corresponds to the clinical US B-mode was obtained by "virtually slicing" the 3D CT volume along the plane containing non-anatomical landmarks, namely points along the microwave ablation antenna. The initial slice plane was generated using the vector acquired by rotating the normal vector of the transverse (i.e., xz) plane along the angle subtended by the antenna. This plane was then further rotated along the ablation antenna and shifted along with the direction of normal vector to obtain similar anatomical structures, such as the liver surface and vasculature that is visualized on both the CT virtual slice and US B-mode images on 20 patients. Finally, an affine transformation was estimated using anatomic and non-anatomic landmarks to account for distortion between the colocated CT virtual slice and US B-mode image resulting in a final registered CT virtual slice. Registration accuracy was measured by estimating the Euclidean distance between corresponding registered points on CT and US B-mode images. RESULTS Mean and SD of the affine transformed registration error was 1.85 ± 2.14 (mm), computed from 20 coregistered data sets. CONCLUSIONS Our results demonstrate the ability to obtain 2D virtual CT slices that are registered to clinical US B-mode images. The use of both anatomical and non-anatomical landmarks result in accurate registration useful for validating ablative margins and comparison to electrode displacement elastography based images.
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Affiliation(s)
- Robert M Pohlman
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Michael R Turney
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Po-Hung Wu
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Christopher L Brace
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Timothy J Ziemlewicz
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Tomy Varghese
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53706, USA
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Peoples JJ, Bisleri G, Ellis RE. Deformable multimodal registration for navigation in beating-heart cardiac surgery. Int J Comput Assist Radiol Surg 2019; 14:955-966. [PMID: 30888597 DOI: 10.1007/s11548-019-01932-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Accepted: 03/01/2019] [Indexed: 11/29/2022]
Abstract
PURPOSE Minimally invasive beating-heart surgery is currently performed using endoscopes and without navigation. Registration of intraoperative ultrasound to a preoperative cardiac CT scan is a valuable step toward image-guided navigation. METHODS The registration was achieved by first extracting a representative point set from each ultrasound image in the sequence using a deformable registration. A template shape representing the cardiac chambers was deformed through a hierarchy of affine transformations to match each ultrasound image using a generalized expectation maximization algorithm. These extracted point sets were matched to the CT by exhaustively searching over a large number of precomputed slices of 3D geometry. The result is a similarity transformation mapping the intraoperative ultrasound to preoperative CT. RESULTS Complete data sets were acquired for four patients. Transesophageal echocardiography ultrasound sequences were deformably registered to a model of oriented points with a mean error of 2.3 mm. Ultrasound and CT scans were registered to a mean of 3 mm, which is comparable to the error of 2.8 mm expected by merging ultrasound registration with uncertainty of cardiac CT. CONCLUSION The proposed algorithm registered 3D CT with dynamic 2D intraoperative imaging. The algorithm aligned the images in both space and time, needing neither dynamic CT imaging nor intraoperative electrocardiograms. The accuracy was sufficient for navigation in thoracoscopically guided beating-heart surgery.
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Khalil A, Ng SC, Liew YM, Lai KW. An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment. Cardiol Res Pract 2018; 2018:1437125. [PMID: 30159169 PMCID: PMC6109558 DOI: 10.1155/2018/1437125] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/05/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.
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Affiliation(s)
- Azira Khalil
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Faculty of Science and Technology, Islamic Science University of Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia
| | - Siew-Cheok Ng
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Xiao Y, Fortin M, Unsgård G, Rivaz H, Reinertsen I. REtroSpective Evaluation of Cerebral Tumors (RESECT): A clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med Phys 2017; 44:3875-3882. [PMID: 28391601 DOI: 10.1002/mp.12268] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 03/05/2017] [Accepted: 04/05/2017] [Indexed: 11/11/2022] Open
Abstract
PURPOSE The advancement of medical image processing techniques, such as image registration, can effectively help improve the accuracy and efficiency of brain tumor surgeries. However, it is often challenging to validate these techniques with real clinical data due to the rarity of such publicly available repositories. ACQUISITION AND VALIDATION METHODS Pre-operative magnetic resonance images (MRI), and intra-operative ultrasound (US) scans were acquired from 23 patients with low-grade gliomas who underwent surgeries at St. Olavs University Hospital between 2011 and 2016. Each patient was scanned by Gadolinium-enhanced T1w and T2-FLAIR MRI protocols to reveal the anatomy and pathology, and series of B-mode ultrasound images were obtained before, during, and after tumor resection to track the surgical progress and tissue deformation. Retrospectively, corresponding anatomical landmarks were identified across US images of different surgical stages, and between MRI and US, and can be used to validate image registration algorithms. Quality of landmark identification was assessed with intra- and inter-rater variability. DATA FORMAT AND ACCESS In addition to co-registered MRIs, each series of US scans are provided as a reconstructed 3D volume. All images are accessible in MINC2 and NIFTI formats, and the anatomical landmarks were annotated in MNI tag files. Both the imaging data and the corresponding landmarks are available online as the RESECT database at https://archive.norstore.no (search for "RESECT"). POTENTIAL IMPACT The proposed database provides real high-quality multi-modal clinical data to validate and compare image registration algorithms that can potentially benefit the accuracy and efficiency of brain tumor resection. Furthermore, the database can also be used to test other image processing methods and neuro-navigation software platforms.
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Affiliation(s)
- Yiming Xiao
- PERFORM Centre, Concordia University, Montreal, H4B 1R6, Canada.,Department of Electrical and Computer Engineering, Concordia University, Montreal, H3G 1M8, Canada
| | - Maryse Fortin
- PERFORM Centre, Concordia University, Montreal, H4B 1R6, Canada.,Department of Electrical and Computer Engineering, Concordia University, Montreal, H3G 1M8, Canada
| | - Geirmund Unsgård
- Department of Neurosurgery, St. Olavs University Hospital, Trondheim, NO-7006, Norway.,Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, NO-7491, Norway.,Norwegian National Advisory Unit for Ultrasound and Image Guided Therapy, St. Olavs University Hospital, Trondheim, NO-7006, Norway
| | - Hassan Rivaz
- PERFORM Centre, Concordia University, Montreal, H4B 1R6, Canada.,Department of Electrical and Computer Engineering, Concordia University, Montreal, H3G 1M8, Canada
| | - Ingerid Reinertsen
- Department of Medical Technology, SINTEF, Trondheim, NO-7465, Norway.,Norwegian National Advisory Unit for Ultrasound and Image Guided Therapy, St. Olavs University Hospital, Trondheim, NO-7006, Norway
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Hughes C, Voros S, Moreau-Gaudry A. Unintended Consequences of Sensor, Signal, and Imaging Informatics: New Problems and New Solutions. Yearb Med Inform 2016:159-162. [PMID: 27830245 DOI: 10.15265/iy-2016-053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2016 of excellent research in the broad field of Sensor, Signal and Imaging Informatics published in the year 2015, with a focus on Unintended consequences: new problems and new solutions. METHODS We performed a systematic initial selection and a double blind peer review process to find the best papers in this domain published in 2015, from the PubMed and Web of Science databases. The set of MesH keywords used was provided by experts. RESULTS The constant advances in medical technology allow ever more relevant diagnostic and therapeutic approaches to be designed. Nevertheless, there is a need to acquire expert knowledge of these innovations in order to identify precociously new associated problems for which new solutions need to be designed and developed.
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Paragios N, Ferrante E, Glocker B, Komodakis N, Parisot S, Zacharaki EI. (Hyper)-graphical models in biomedical image analysis. Med Image Anal 2016; 33:102-106. [DOI: 10.1016/j.media.2016.06.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Revised: 06/16/2016] [Accepted: 06/16/2016] [Indexed: 11/29/2022]
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Drouin S, Kochanowska A, Kersten-Oertel M, Gerard IJ, Zelmann R, De Nigris D, Bériault S, Arbel T, Sirhan D, Sadikot AF, Hall JA, Sinclair DS, Petrecca K, DelMaestro RF, Collins DL. IBIS: an OR ready open-source platform for image-guided neurosurgery. Int J Comput Assist Radiol Surg 2016; 12:363-378. [DOI: 10.1007/s11548-016-1478-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 08/19/2016] [Indexed: 10/21/2022]
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