Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy
Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully recon...
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| Veröffentlicht in: | IEEE transactions on medical imaging 2022-09, Vol.41 (9), p.2385-2398 |
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| creator | You, Qi Trzasko, Joshua D. Lowerison, Matthew R. Chen, Xi Dong, Zhijie ChandraSekaran, Nathiya Vaithiyalingam Llano, Daniel A. Chen, Shigao Song, Pengfei |
| description | Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1-2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations. |
| doi_str_mv | 10.1109/TMI.2022.3162839 |
| format | Article |
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However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1-2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations.</description><identifier>ISSN: 0278-0062</identifier><identifier>ISSN: 1558-254X</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2022.3162839</identifier><identifier>PMID: 35344488</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Animals ; Blood vessels ; Brain ; Chick Embryo ; Chickens ; Chorioallantoic membrane ; compressive sampling ; curvelet transform ; Data acquisition ; Embryos ; Flow mapping ; Flow velocity ; Image acquisition ; Image reconstruction ; Imaging ; Localization ; Location awareness ; Mice ; Microbubbles ; Microscopy ; Microscopy - methods ; Microvessels - diagnostic imaging ; Neuroimaging ; Sparsity ; sparsity promoting ; super-resolution imaging ; Transformations (mathematics) ; Transforms ; Ultrasonic imaging ; Ultrasonography - methods ; Ultrasound ; Ultrasound localization microscopy</subject><ispartof>IEEE transactions on medical imaging, 2022-09, Vol.41 (9), p.2385-2398</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-1abe296ee2849daf0fb62a52eaf770203edf02b6eb3ab96c8a685373ee88f18b3</citedby><cites>FETCH-LOGICAL-c444t-1abe296ee2849daf0fb62a52eaf770203edf02b6eb3ab96c8a685373ee88f18b3</cites><orcidid>0000-0002-1125-4554 ; 0000-0003-3460-127X ; 0000-0002-9103-6345 ; 0000-0002-2706-1394 ; 0000-0001-8098-7789 ; 0000-0002-4151-4820</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9743950$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>230,314,778,782,794,883,27907,27908,54741</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35344488$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>You, Qi</creatorcontrib><creatorcontrib>Trzasko, Joshua D.</creatorcontrib><creatorcontrib>Lowerison, Matthew R.</creatorcontrib><creatorcontrib>Chen, Xi</creatorcontrib><creatorcontrib>Dong, Zhijie</creatorcontrib><creatorcontrib>ChandraSekaran, Nathiya Vaithiyalingam</creatorcontrib><creatorcontrib>Llano, Daniel A.</creatorcontrib><creatorcontrib>Chen, Shigao</creatorcontrib><creatorcontrib>Song, Pengfei</creatorcontrib><title>Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1-2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Blood vessels</subject><subject>Brain</subject><subject>Chick Embryo</subject><subject>Chickens</subject><subject>Chorioallantoic membrane</subject><subject>compressive sampling</subject><subject>curvelet transform</subject><subject>Data acquisition</subject><subject>Embryos</subject><subject>Flow mapping</subject><subject>Flow velocity</subject><subject>Image acquisition</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Localization</subject><subject>Location awareness</subject><subject>Mice</subject><subject>Microbubbles</subject><subject>Microscopy</subject><subject>Microscopy - methods</subject><subject>Microvessels - diagnostic imaging</subject><subject>Neuroimaging</subject><subject>Sparsity</subject><subject>sparsity promoting</subject><subject>super-resolution imaging</subject><subject>Transformations (mathematics)</subject><subject>Transforms</subject><subject>Ultrasonic imaging</subject><subject>Ultrasonography - methods</subject><subject>Ultrasound</subject><subject>Ultrasound localization microscopy</subject><issn>0278-0062</issn><issn>1558-254X</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkc9r2zAYhsVYWdNu98FgCHbpxZl-W7oMutBuhZQNlsJuQrY_pyq2lUp2If3rp5AsdDvp8D3fy_vpQeg9JXNKifm8ur2ZM8LYnFPFNDev0IxKqQsmxe_XaEZYqQtCFDtFZyk9EEKFJOYNOuWSCyG0niG3mOITdDDiVXRDakPsi68uQYN_bVxMftzinzH0YfTDGl926xD9eN_jzOFrl0Z8143RpTANDV6G2nX-2Y0-DPjW1zGkOmy2b9FJ67oE7w7vObq7vlotvhfLH99uFpfLos5VxoK6CphRAEwL07iWtJViTjJwbVkSRjg0LWGVgoq7yqhaO6UlLzmA1i3VFT9HX_a5m6nqoalhyM06u4m-d3Frg_P238ng7-06PFkjjJJG5YCLQ0AMjxOk0fY-1dB1boAwJcuUEEYorUlGP_2HPoQpDvk8y0qilWCSs0yRPbX7ihShPZahxO782ezP7vzZg7-88vHlEceFv8Iy8GEPeAA4jk0puJGE_wE9QqG4</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>You, Qi</creator><creator>Trzasko, Joshua D.</creator><creator>Lowerison, Matthew R.</creator><creator>Chen, Xi</creator><creator>Dong, Zhijie</creator><creator>ChandraSekaran, Nathiya Vaithiyalingam</creator><creator>Llano, Daniel A.</creator><creator>Chen, Shigao</creator><creator>Song, Pengfei</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1-2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>35344488</pmid><doi>10.1109/TMI.2022.3162839</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-1125-4554</orcidid><orcidid>https://orcid.org/0000-0003-3460-127X</orcidid><orcidid>https://orcid.org/0000-0002-9103-6345</orcidid><orcidid>https://orcid.org/0000-0002-2706-1394</orcidid><orcidid>https://orcid.org/0000-0001-8098-7789</orcidid><orcidid>https://orcid.org/0000-0002-4151-4820</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Animals Blood vessels Brain Chick Embryo Chickens Chorioallantoic membrane compressive sampling curvelet transform Data acquisition Embryos Flow mapping Flow velocity Image acquisition Image reconstruction Imaging Localization Location awareness Mice Microbubbles Microscopy Microscopy - methods Microvessels - diagnostic imaging Neuroimaging Sparsity sparsity promoting super-resolution imaging Transformations (mathematics) Transforms Ultrasonic imaging Ultrasonography - methods Ultrasound Ultrasound localization microscopy |
| title | Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy |
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