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Shape-based tracking of left ventricular wall motion
An approach for tracking and quantifying the nonrigid, nonuniform motion of the left ventricular (LV) endocardial wall from two-dimensional (2-D) cardiac image sequences, on a point-by-point basis over the entire cardiac cycle, is presented. Given a set of boundaries, motion computation involves fir...
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| Published in: | IEEE transactions on medical imaging 1997-06, Vol.16 (3), p.270-283 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c359t-d04c39b01b65c6e776451b569cef2b1c4facf33e4c72c0ec07cd292739394b53 |
| container_end_page | 283 |
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| container_start_page | 270 |
| container_title | IEEE transactions on medical imaging |
| container_volume | 16 |
| creator | McEachen, J.C. Duncan, J.S. |
| description | An approach for tracking and quantifying the nonrigid, nonuniform motion of the left ventricular (LV) endocardial wall from two-dimensional (2-D) cardiac image sequences, on a point-by-point basis over the entire cardiac cycle, is presented. Given a set of boundaries, motion computation involves first matching local segments on one contour to segments on the next contour in the sequence using a shape-based strategy. Results from the match process are incorporated with a smoothness term into an optimization functional. The global minimum of this functional is found, resulting in a smooth flow field that is consistent with the match data. The computation is performed for all pairs of frames in the temporal sequence and equally sampled points on one contour are tracked throughout the sequence, resulting in a composite flow field over the entire sequence. Two perspectives on characterizing the optimization functional are presented which result in a tradeoff resolved by the confidence in the initial boundary segmentation. Experimental results for contours derived from diagnostic image sequences of three different imaging modalities are presented. A comparison of trajectory estimates with trajectories of gold-standard markers implanted in the LV wall are presented for validation. The results of this comparison confirm that although cardiac motion is a three-dimensional (3-D) problem, two-dimensional (2-D) analysis provides a rich testing ground for algorithm development. |
| doi_str_mv | 10.1109/42.585761 |
| format | article |
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Given a set of boundaries, motion computation involves first matching local segments on one contour to segments on the next contour in the sequence using a shape-based strategy. Results from the match process are incorporated with a smoothness term into an optimization functional. The global minimum of this functional is found, resulting in a smooth flow field that is consistent with the match data. The computation is performed for all pairs of frames in the temporal sequence and equally sampled points on one contour are tracked throughout the sequence, resulting in a composite flow field over the entire sequence. Two perspectives on characterizing the optimization functional are presented which result in a tradeoff resolved by the confidence in the initial boundary segmentation. Experimental results for contours derived from diagnostic image sequences of three different imaging modalities are presented. 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Given a set of boundaries, motion computation involves first matching local segments on one contour to segments on the next contour in the sequence using a shape-based strategy. Results from the match process are incorporated with a smoothness term into an optimization functional. The global minimum of this functional is found, resulting in a smooth flow field that is consistent with the match data. The computation is performed for all pairs of frames in the temporal sequence and equally sampled points on one contour are tracked throughout the sequence, resulting in a composite flow field over the entire sequence. Two perspectives on characterizing the optimization functional are presented which result in a tradeoff resolved by the confidence in the initial boundary segmentation. Experimental results for contours derived from diagnostic image sequences of three different imaging modalities are presented. A comparison of trajectory estimates with trajectories of gold-standard markers implanted in the LV wall are presented for validation. The results of this comparison confirm that although cardiac motion is a three-dimensional (3-D) problem, two-dimensional (2-D) analysis provides a rich testing ground for algorithm development.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Biological and medical sciences</subject><subject>Cardiovascular system</subject><subject>Computed tomography</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Dogs</subject><subject>Echocardiography</subject><subject>General aspects. Methods</subject><subject>Heart</subject><subject>Heart - diagnostic imaging</subject><subject>Humans</subject><subject>Image motion analysis</subject><subject>Image Processing, Computer-Assisted</subject><subject>Image segmentation</subject><subject>Image sequences</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Magnetic Resonance Imaging</subject><subject>Medical sciences</subject><subject>Models, Cardiovascular</subject><subject>Myocardial Contraction - physiology</subject><subject>Optical imaging</subject><subject>Radiodiagnosis. Nmr imagery. Nmr spectrometry</subject><subject>Radiography</subject><subject>Tracking</subject><subject>Two dimensional displays</subject><subject>Ventricular Function, Left - physiology</subject><subject>X-ray imaging</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNqFkD1LxEAQhhdRzvO0sBWEFCJY5NzP7G4ph19wYOEVdmGzmdXoJjl3E8V_byThWqspnod3Zl6ETgleEoL1NadLoYTMyB6aEyFUSgV_2UdzTKVKMc7oITqK8R1jwgXWMzTTRHGl9Bzx5zezhbQwEcqkC8Z-VM1r0rrEg-uSL2i6UNnem5B8G--Tuu2qtjlGB874CCfTXKDN3e1m9ZCun-4fVzfr1DKhu7TE3DJdYFJkwmYgZcYFKUSmLThaEMudsY4x4FZSi8FiaUuqqWSaaV4ItkCXY-w2tJ89xC6vq2jBe9NA28dcasyHN_C_IlUZUYzTQbwaRRvaGAO4fBuq2oSfnOD8r8mc03xscnDPp9C-qKHcmVN1A7-YuInWeBdMY6u402imtWB_t52NWgUAOzrt-AXYqoFx</recordid><startdate>19970601</startdate><enddate>19970601</enddate><creator>McEachen, J.C.</creator><creator>Duncan, J.S.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7U5</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>19970601</creationdate><title>Shape-based tracking of left ventricular wall motion</title><author>McEachen, J.C. ; Duncan, J.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-d04c39b01b65c6e776451b569cef2b1c4facf33e4c72c0ec07cd292739394b53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Biological and medical sciences</topic><topic>Cardiovascular system</topic><topic>Computed tomography</topic><topic>Computerized, statistical medical data processing and models in biomedicine</topic><topic>Dogs</topic><topic>Echocardiography</topic><topic>General aspects. Methods</topic><topic>Heart</topic><topic>Heart - diagnostic imaging</topic><topic>Humans</topic><topic>Image motion analysis</topic><topic>Image Processing, Computer-Assisted</topic><topic>Image segmentation</topic><topic>Image sequences</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Magnetic Resonance Imaging</topic><topic>Medical sciences</topic><topic>Models, Cardiovascular</topic><topic>Myocardial Contraction - physiology</topic><topic>Optical imaging</topic><topic>Radiodiagnosis. Nmr imagery. Nmr spectrometry</topic><topic>Radiography</topic><topic>Tracking</topic><topic>Two dimensional displays</topic><topic>Ventricular Function, Left - physiology</topic><topic>X-ray imaging</topic><toplevel>online_resources</toplevel><creatorcontrib>McEachen, J.C.</creatorcontrib><creatorcontrib>Duncan, J.S.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McEachen, J.C.</au><au>Duncan, J.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shape-based tracking of left ventricular wall motion</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>1997-06-01</date><risdate>1997</risdate><volume>16</volume><issue>3</issue><spage>270</spage><epage>283</epage><pages>270-283</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><notes>ObjectType-Article-2</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-1</notes><notes>content type line 23</notes><notes>ObjectType-Article-1</notes><notes>ObjectType-Feature-2</notes><abstract>An approach for tracking and quantifying the nonrigid, nonuniform motion of the left ventricular (LV) endocardial wall from two-dimensional (2-D) cardiac image sequences, on a point-by-point basis over the entire cardiac cycle, is presented. Given a set of boundaries, motion computation involves first matching local segments on one contour to segments on the next contour in the sequence using a shape-based strategy. Results from the match process are incorporated with a smoothness term into an optimization functional. The global minimum of this functional is found, resulting in a smooth flow field that is consistent with the match data. The computation is performed for all pairs of frames in the temporal sequence and equally sampled points on one contour are tracked throughout the sequence, resulting in a composite flow field over the entire sequence. Two perspectives on characterizing the optimization functional are presented which result in a tradeoff resolved by the confidence in the initial boundary segmentation. Experimental results for contours derived from diagnostic image sequences of three different imaging modalities are presented. A comparison of trajectory estimates with trajectories of gold-standard markers implanted in the LV wall are presented for validation. The results of this comparison confirm that although cardiac motion is a three-dimensional (3-D) problem, two-dimensional (2-D) analysis provides a rich testing ground for algorithm development.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>9184889</pmid><doi>10.1109/42.585761</doi><tpages>14</tpages></addata></record> |
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| identifier | ISSN: 0278-0062 |
| ispartof | IEEE transactions on medical imaging, 1997-06, Vol.16 (3), p.270-283 |
| issn | 0278-0062 1558-254X |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_42_585761 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | Algorithms Animals Biological and medical sciences Cardiovascular system Computed tomography Computerized, statistical medical data processing and models in biomedicine Dogs Echocardiography General aspects. Methods Heart Heart - diagnostic imaging Humans Image motion analysis Image Processing, Computer-Assisted Image segmentation Image sequences Investigative techniques, diagnostic techniques (general aspects) Magnetic Resonance Imaging Medical sciences Models, Cardiovascular Myocardial Contraction - physiology Optical imaging Radiodiagnosis. Nmr imagery. Nmr spectrometry Radiography Tracking Two dimensional displays Ventricular Function, Left - physiology X-ray imaging |
| title | Shape-based tracking of left ventricular wall motion |
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