Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system

MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delinea...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Medical physics (Lancaster) 2023-10, Vol.50 (10), p.6163-6176
Hauptverfasser: Chen, Sihao, Eldeniz, Cihat, Fraum, Tyler J, Ludwig, Daniel R, Gan, Weijie, Liu, Jiaming, Kamilov, Ulugbek S, Yang, Deshan, Gach, H Michael, An, Hongyu
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 6176
container_issue 10
container_start_page 6163
container_title Medical physics (Lancaster)
container_volume 50
creator Chen, Sihao
Eldeniz, Cihat
Fraum, Tyler J
Ludwig, Daniel R
Gan, Weijie
Liu, Jiaming
Kamilov, Ulugbek S
Yang, Deshan
Gach, H Michael
An, Hongyu
description MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delineate lesions. Existing MRI methods require multiple scans with lengthy acquisition times or are limited by low spatial resolution, contrast, and signal-to-noise ratio. We developed a novel method to obtain motion-resolved 4D-MRIs and motion-integrated 3D-MRI reconstruction using a single rapid (35-45 s scan on a 0.35 T MRI-Linac. Golden-angle radial stack-of-stars MRI scans were acquired from a respiratory motion phantom and 12 healthy volunteers (n = 12) on a 0.35 T MRI-Linac. A self-navigated method was employed to detect respiratory motion using 2000 (acquisition time = 5-7 min) and the first 200 spokes (acquisition time = 35-45 s). Multi-coil non-uniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and deep-learning Phase2Phase (P2P) methods were employed to reconstruct motion-resolved 4D-MRI using 2000 spokes (MCNUFFT2000) and 200 spokes (CS200 and P2P200). Deformable motion vector fields (MVFs) were computed from the 4D-MRIs and used to reconstruct motion-corrected 3D-MRIs with the MOtion Transformation Integrated forward-Fourier (MOTIF) method. Image quality was evaluated quantitatively using the structural similarity index measure (SSIM) and the root mean square error (RMSE), and qualitatively in a blinded radiological review. Evaluation using the respiratory motion phantom experiment showed that the proposed method reversed the effects of motion blurring and restored edge sharpness. In the human study, P2P200 had smaller inaccuracy in MVFs estimation than CS200. P2P200 had significantly greater SSIMs (p  5 out of 10), and then motion-uncorrected (scoring 
doi_str_mv 10.1002/mp.16469
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2813889104</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2813889104</sourcerecordid><originalsourceid>FETCH-LOGICAL-c283t-a07444a711540c64fe3e07f06d02be461ad114cf3112d580e32d8641315f74ca3</originalsourceid><addsrcrecordid>eNpFkEtLw0AUhQdRbK2Cv0Bm6Sb13pmbR5dSfBQqQqkLV2E6uSmRTBJnkkX_va1WXR045-MsPiGuEaYIoO5cN8WEktmJGCtKdUQKZqdiDDCjSBHEI3ERwgcAJDqGczHSKWakIR6L9xWHrvKmb_1Ourav2kY605gtO256OYSq2UojD1Gz9KarCvmyWshgTSPL1u83mOpYrg9ttKwaY2XYhZ7dpTgrTR346pgT8fb4sJ4_R8vXp8X8fhlZlek-MpASkUkRYwKbUMmaIS0hKUBtmBI0BSLZUiOqIs6AtSqyhFBjXKZkjZ6I25_fzrefA4c-d1WwXNem4XYIucpQZ9kMgf5R69sQPJd55ytn_C5HyA8ic9fl3yL36M3xddg4Lv7AX3P6C_3eauY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2813889104</pqid></control><display><type>article</type><title>Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system</title><source>Access via Wiley Online Library</source><source>Alma/SFX Local Collection</source><creator>Chen, Sihao ; Eldeniz, Cihat ; Fraum, Tyler J ; Ludwig, Daniel R ; Gan, Weijie ; Liu, Jiaming ; Kamilov, Ulugbek S ; Yang, Deshan ; Gach, H Michael ; An, Hongyu</creator><creatorcontrib>Chen, Sihao ; Eldeniz, Cihat ; Fraum, Tyler J ; Ludwig, Daniel R ; Gan, Weijie ; Liu, Jiaming ; Kamilov, Ulugbek S ; Yang, Deshan ; Gach, H Michael ; An, Hongyu</creatorcontrib><description>MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delineate lesions. Existing MRI methods require multiple scans with lengthy acquisition times or are limited by low spatial resolution, contrast, and signal-to-noise ratio. We developed a novel method to obtain motion-resolved 4D-MRIs and motion-integrated 3D-MRI reconstruction using a single rapid (35-45 s scan on a 0.35 T MRI-Linac. Golden-angle radial stack-of-stars MRI scans were acquired from a respiratory motion phantom and 12 healthy volunteers (n = 12) on a 0.35 T MRI-Linac. A self-navigated method was employed to detect respiratory motion using 2000 (acquisition time = 5-7 min) and the first 200 spokes (acquisition time = 35-45 s). Multi-coil non-uniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and deep-learning Phase2Phase (P2P) methods were employed to reconstruct motion-resolved 4D-MRI using 2000 spokes (MCNUFFT2000) and 200 spokes (CS200 and P2P200). Deformable motion vector fields (MVFs) were computed from the 4D-MRIs and used to reconstruct motion-corrected 3D-MRIs with the MOtion Transformation Integrated forward-Fourier (MOTIF) method. Image quality was evaluated quantitatively using the structural similarity index measure (SSIM) and the root mean square error (RMSE), and qualitatively in a blinded radiological review. Evaluation using the respiratory motion phantom experiment showed that the proposed method reversed the effects of motion blurring and restored edge sharpness. In the human study, P2P200 had smaller inaccuracy in MVFs estimation than CS200. P2P200 had significantly greater SSIMs (p &lt; 0.0001) and smaller RMSEs (p &lt; 0.001) than CS200 in motion-resolved 4D-MRI and motion-corrected 3D-MRI. The radiological review found that MOTIF 3D-MRIs using MCNUFFT2000 exhibited the highest image quality (scoring &gt; 8 out of 10), followed by P2P200 (scoring &gt; 5 out of 10), and then motion-uncorrected (scoring &lt; 3 out of 10) in sharpness, contrast, and artifact-freeness. We have successfully demonstrated a method for respiratory motion management for MRI-guided RT. The method integrated self-navigated respiratory motion detection, deep-learning P2P 4D-MRI reconstruction, and a motion integrated reconstruction (MOTIF) for 3D-MRI using a single rapid MRI scan (35-45 s) on a 0.35 T MRI-Linac system.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1002/mp.16469</identifier><identifier>PMID: 37184305</identifier><language>eng</language><publisher>United States</publisher><ispartof>Medical physics (Lancaster), 2023-10, Vol.50 (10), p.6163-6176</ispartof><rights>2023 American Association of Physicists in Medicine.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c283t-a07444a711540c64fe3e07f06d02be461ad114cf3112d580e32d8641315f74ca3</citedby><cites>FETCH-LOGICAL-c283t-a07444a711540c64fe3e07f06d02be461ad114cf3112d580e32d8641315f74ca3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27929,27930</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37184305$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Sihao</creatorcontrib><creatorcontrib>Eldeniz, Cihat</creatorcontrib><creatorcontrib>Fraum, Tyler J</creatorcontrib><creatorcontrib>Ludwig, Daniel R</creatorcontrib><creatorcontrib>Gan, Weijie</creatorcontrib><creatorcontrib>Liu, Jiaming</creatorcontrib><creatorcontrib>Kamilov, Ulugbek S</creatorcontrib><creatorcontrib>Yang, Deshan</creatorcontrib><creatorcontrib>Gach, H Michael</creatorcontrib><creatorcontrib>An, Hongyu</creatorcontrib><title>Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delineate lesions. Existing MRI methods require multiple scans with lengthy acquisition times or are limited by low spatial resolution, contrast, and signal-to-noise ratio. We developed a novel method to obtain motion-resolved 4D-MRIs and motion-integrated 3D-MRI reconstruction using a single rapid (35-45 s scan on a 0.35 T MRI-Linac. Golden-angle radial stack-of-stars MRI scans were acquired from a respiratory motion phantom and 12 healthy volunteers (n = 12) on a 0.35 T MRI-Linac. A self-navigated method was employed to detect respiratory motion using 2000 (acquisition time = 5-7 min) and the first 200 spokes (acquisition time = 35-45 s). Multi-coil non-uniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and deep-learning Phase2Phase (P2P) methods were employed to reconstruct motion-resolved 4D-MRI using 2000 spokes (MCNUFFT2000) and 200 spokes (CS200 and P2P200). Deformable motion vector fields (MVFs) were computed from the 4D-MRIs and used to reconstruct motion-corrected 3D-MRIs with the MOtion Transformation Integrated forward-Fourier (MOTIF) method. Image quality was evaluated quantitatively using the structural similarity index measure (SSIM) and the root mean square error (RMSE), and qualitatively in a blinded radiological review. Evaluation using the respiratory motion phantom experiment showed that the proposed method reversed the effects of motion blurring and restored edge sharpness. In the human study, P2P200 had smaller inaccuracy in MVFs estimation than CS200. P2P200 had significantly greater SSIMs (p &lt; 0.0001) and smaller RMSEs (p &lt; 0.001) than CS200 in motion-resolved 4D-MRI and motion-corrected 3D-MRI. The radiological review found that MOTIF 3D-MRIs using MCNUFFT2000 exhibited the highest image quality (scoring &gt; 8 out of 10), followed by P2P200 (scoring &gt; 5 out of 10), and then motion-uncorrected (scoring &lt; 3 out of 10) in sharpness, contrast, and artifact-freeness. We have successfully demonstrated a method for respiratory motion management for MRI-guided RT. The method integrated self-navigated respiratory motion detection, deep-learning P2P 4D-MRI reconstruction, and a motion integrated reconstruction (MOTIF) for 3D-MRI using a single rapid MRI scan (35-45 s) on a 0.35 T MRI-Linac system.</description><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpFkEtLw0AUhQdRbK2Cv0Bm6Sb13pmbR5dSfBQqQqkLV2E6uSmRTBJnkkX_va1WXR045-MsPiGuEaYIoO5cN8WEktmJGCtKdUQKZqdiDDCjSBHEI3ERwgcAJDqGczHSKWakIR6L9xWHrvKmb_1Ourav2kY605gtO256OYSq2UojD1Gz9KarCvmyWshgTSPL1u83mOpYrg9ttKwaY2XYhZ7dpTgrTR346pgT8fb4sJ4_R8vXp8X8fhlZlek-MpASkUkRYwKbUMmaIS0hKUBtmBI0BSLZUiOqIs6AtSqyhFBjXKZkjZ6I25_fzrefA4c-d1WwXNem4XYIucpQZ9kMgf5R69sQPJd55ytn_C5HyA8ic9fl3yL36M3xddg4Lv7AX3P6C_3eauY</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Chen, Sihao</creator><creator>Eldeniz, Cihat</creator><creator>Fraum, Tyler J</creator><creator>Ludwig, Daniel R</creator><creator>Gan, Weijie</creator><creator>Liu, Jiaming</creator><creator>Kamilov, Ulugbek S</creator><creator>Yang, Deshan</creator><creator>Gach, H Michael</creator><creator>An, Hongyu</creator><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20231001</creationdate><title>Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system</title><author>Chen, Sihao ; Eldeniz, Cihat ; Fraum, Tyler J ; Ludwig, Daniel R ; Gan, Weijie ; Liu, Jiaming ; Kamilov, Ulugbek S ; Yang, Deshan ; Gach, H Michael ; An, Hongyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c283t-a07444a711540c64fe3e07f06d02be461ad114cf3112d580e32d8641315f74ca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Sihao</creatorcontrib><creatorcontrib>Eldeniz, Cihat</creatorcontrib><creatorcontrib>Fraum, Tyler J</creatorcontrib><creatorcontrib>Ludwig, Daniel R</creatorcontrib><creatorcontrib>Gan, Weijie</creatorcontrib><creatorcontrib>Liu, Jiaming</creatorcontrib><creatorcontrib>Kamilov, Ulugbek S</creatorcontrib><creatorcontrib>Yang, Deshan</creatorcontrib><creatorcontrib>Gach, H Michael</creatorcontrib><creatorcontrib>An, Hongyu</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Sihao</au><au>Eldeniz, Cihat</au><au>Fraum, Tyler J</au><au>Ludwig, Daniel R</au><au>Gan, Weijie</au><au>Liu, Jiaming</au><au>Kamilov, Ulugbek S</au><au>Yang, Deshan</au><au>Gach, H Michael</au><au>An, Hongyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2023-10-01</date><risdate>2023</risdate><volume>50</volume><issue>10</issue><spage>6163</spage><epage>6176</epage><pages>6163-6176</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><abstract>MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delineate lesions. Existing MRI methods require multiple scans with lengthy acquisition times or are limited by low spatial resolution, contrast, and signal-to-noise ratio. We developed a novel method to obtain motion-resolved 4D-MRIs and motion-integrated 3D-MRI reconstruction using a single rapid (35-45 s scan on a 0.35 T MRI-Linac. Golden-angle radial stack-of-stars MRI scans were acquired from a respiratory motion phantom and 12 healthy volunteers (n = 12) on a 0.35 T MRI-Linac. A self-navigated method was employed to detect respiratory motion using 2000 (acquisition time = 5-7 min) and the first 200 spokes (acquisition time = 35-45 s). Multi-coil non-uniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and deep-learning Phase2Phase (P2P) methods were employed to reconstruct motion-resolved 4D-MRI using 2000 spokes (MCNUFFT2000) and 200 spokes (CS200 and P2P200). Deformable motion vector fields (MVFs) were computed from the 4D-MRIs and used to reconstruct motion-corrected 3D-MRIs with the MOtion Transformation Integrated forward-Fourier (MOTIF) method. Image quality was evaluated quantitatively using the structural similarity index measure (SSIM) and the root mean square error (RMSE), and qualitatively in a blinded radiological review. Evaluation using the respiratory motion phantom experiment showed that the proposed method reversed the effects of motion blurring and restored edge sharpness. In the human study, P2P200 had smaller inaccuracy in MVFs estimation than CS200. P2P200 had significantly greater SSIMs (p &lt; 0.0001) and smaller RMSEs (p &lt; 0.001) than CS200 in motion-resolved 4D-MRI and motion-corrected 3D-MRI. The radiological review found that MOTIF 3D-MRIs using MCNUFFT2000 exhibited the highest image quality (scoring &gt; 8 out of 10), followed by P2P200 (scoring &gt; 5 out of 10), and then motion-uncorrected (scoring &lt; 3 out of 10) in sharpness, contrast, and artifact-freeness. We have successfully demonstrated a method for respiratory motion management for MRI-guided RT. The method integrated self-navigated respiratory motion detection, deep-learning P2P 4D-MRI reconstruction, and a motion integrated reconstruction (MOTIF) for 3D-MRI using a single rapid MRI scan (35-45 s) on a 0.35 T MRI-Linac system.</abstract><cop>United States</cop><pmid>37184305</pmid><doi>10.1002/mp.16469</doi><tpages>14</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0094-2405
ispartof Medical physics (Lancaster), 2023-10, Vol.50 (10), p.6163-6176
issn 0094-2405
2473-4209
language eng
recordid cdi_proquest_miscellaneous_2813889104
source Access via Wiley Online Library; Alma/SFX Local Collection
title Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-16T01%3A24%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Respiratory%20motion%20management%20using%20a%20single%20rapid%20MRI%20scan%20for%20a%200.35%20T%20MRI-Linac%20system&rft.jtitle=Medical%20physics%20(Lancaster)&rft.au=Chen,%20Sihao&rft.date=2023-10-01&rft.volume=50&rft.issue=10&rft.spage=6163&rft.epage=6176&rft.pages=6163-6176&rft.issn=0094-2405&rft.eissn=2473-4209&rft_id=info:doi/10.1002/mp.16469&rft_dat=%3Cproquest_cross%3E2813889104%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2813889104&rft_id=info:pmid/37184305&rfr_iscdi=true