Inputs for subject-specific computational fluid dynamics simulation of blood flow in the mouse aorta
Mouse models are an important way for exploring relationships between blood hemodynamics and eventual plaque formation. We have developed a mouse model of aortic regurgitation (AR) that produces large changes in plaque burden with charges in hemodynamics [Zhou et al., 2010, "Aortic Regurgitatio...
Uložené v:
| Vydané v: | Journal of biomechanical engineering Ročník 136; číslo 10; s. 101008 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
01.10.2014
|
| Predmet: | |
| ISSN: | 1528-8951, 1528-8951 |
| On-line prístup: | Zistit podrobnosti o prístupe |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Mouse models are an important way for exploring relationships between blood hemodynamics and eventual plaque formation. We have developed a mouse model of aortic regurgitation (AR) that produces large changes in plaque burden with charges in hemodynamics [Zhou et al., 2010, "Aortic Regurgitation Dramatically Alters the Distribution of Atherosclerotic Lesions and Enhances Atherogenesis in Mice," Arterioscler. Thromb. Vasc. Biol., 30(6), pp. 1181-1188]. In this paper, we explore the amount of detail needed for realistic computational fluid dynamics (CFD) calculations in this experimental model. The CFD calculations use inputs based on experimental measurements from ultrasound (US), micro computed tomography (CT), and both anatomical magnetic resonance imaging (MRI) and phase contrast MRI (PC-MRI). The adequacy of five different levels of model complexity (a) subject-specific CT data from a single mouse; (b) subject-specific CT centerlines with radii from US; (c) same as (b) but with MRI derived centerlines; (d) average CT centerlines and averaged vessel radius and branching vessels; and (e) same as (d) but with averaged MRI centerlines) is evaluated by demonstrating their impact on relative residence time (RRT) outputs. The paper concludes by demonstrating the necessity of subject-specific geometry and recommends for inputs the use of CT or anatomical MRI for establishing the aortic centerlines, M-mode US for scaling the aortic diameters, and a combination of PC-MRI and Doppler US for estimating the spatial and temporal characteristics of the input wave forms. |
|---|---|
| AbstractList | Mouse models are an important way for exploring relationships between blood hemodynamics and eventual plaque formation. We have developed a mouse model of aortic regurgitation (AR) that produces large changes in plaque burden with charges in hemodynamics [Zhou et al., 2010, "Aortic Regurgitation Dramatically Alters the Distribution of Atherosclerotic Lesions and Enhances Atherogenesis in Mice," Arterioscler. Thromb. Vasc. Biol., 30(6), pp. 1181-1188]. In this paper, we explore the amount of detail needed for realistic computational fluid dynamics (CFD) calculations in this experimental model. The CFD calculations use inputs based on experimental measurements from ultrasound (US), micro computed tomography (CT), and both anatomical magnetic resonance imaging (MRI) and phase contrast MRI (PC-MRI). The adequacy of five different levels of model complexity (a) subject-specific CT data from a single mouse; (b) subject-specific CT centerlines with radii from US; (c) same as (b) but with MRI derived centerlines; (d) average CT centerlines and averaged vessel radius and branching vessels; and (e) same as (d) but with averaged MRI centerlines) is evaluated by demonstrating their impact on relative residence time (RRT) outputs. The paper concludes by demonstrating the necessity of subject-specific geometry and recommends for inputs the use of CT or anatomical MRI for establishing the aortic centerlines, M-mode US for scaling the aortic diameters, and a combination of PC-MRI and Doppler US for estimating the spatial and temporal characteristics of the input wave forms. Mouse models are an important way for exploring relationships between blood hemodynamics and eventual plaque formation. We have developed a mouse model of aortic regurgitation (AR) that produces large changes in plaque burden with charges in hemodynamics [Zhou et al., 2010, "Aortic Regurgitation Dramatically Alters the Distribution of Atherosclerotic Lesions and Enhances Atherogenesis in Mice," Arterioscler. Thromb. Vasc. Biol., 30(6), pp. 1181-1188]. In this paper, we explore the amount of detail needed for realistic computational fluid dynamics (CFD) calculations in this experimental model. The CFD calculations use inputs based on experimental measurements from ultrasound (US), micro computed tomography (CT), and both anatomical magnetic resonance imaging (MRI) and phase contrast MRI (PC-MRI). The adequacy of five different levels of model complexity (a) subject-specific CT data from a single mouse; (b) subject-specific CT centerlines with radii from US; (c) same as (b) but with MRI derived centerlines; (d) average CT centerlines and averaged vessel radius and branching vessels; and (e) same as (d) but with averaged MRI centerlines) is evaluated by demonstrating their impact on relative residence time (RRT) outputs. The paper concludes by demonstrating the necessity of subject-specific geometry and recommends for inputs the use of CT or anatomical MRI for establishing the aortic centerlines, M-mode US for scaling the aortic diameters, and a combination of PC-MRI and Doppler US for estimating the spatial and temporal characteristics of the input wave forms.Mouse models are an important way for exploring relationships between blood hemodynamics and eventual plaque formation. We have developed a mouse model of aortic regurgitation (AR) that produces large changes in plaque burden with charges in hemodynamics [Zhou et al., 2010, "Aortic Regurgitation Dramatically Alters the Distribution of Atherosclerotic Lesions and Enhances Atherogenesis in Mice," Arterioscler. Thromb. Vasc. Biol., 30(6), pp. 1181-1188]. In this paper, we explore the amount of detail needed for realistic computational fluid dynamics (CFD) calculations in this experimental model. The CFD calculations use inputs based on experimental measurements from ultrasound (US), micro computed tomography (CT), and both anatomical magnetic resonance imaging (MRI) and phase contrast MRI (PC-MRI). The adequacy of five different levels of model complexity (a) subject-specific CT data from a single mouse; (b) subject-specific CT centerlines with radii from US; (c) same as (b) but with MRI derived centerlines; (d) average CT centerlines and averaged vessel radius and branching vessels; and (e) same as (d) but with averaged MRI centerlines) is evaluated by demonstrating their impact on relative residence time (RRT) outputs. The paper concludes by demonstrating the necessity of subject-specific geometry and recommends for inputs the use of CT or anatomical MRI for establishing the aortic centerlines, M-mode US for scaling the aortic diameters, and a combination of PC-MRI and Doppler US for estimating the spatial and temporal characteristics of the input wave forms. |
| Author | Steinman, David A Zhang, Xiaoli Zhou, Yu-Qing Henkelman, R Mark Van Doormaal, Mark |
| Author_xml | – sequence: 1 givenname: Mark surname: Van Doormaal fullname: Van Doormaal, Mark – sequence: 2 givenname: Yu-Qing surname: Zhou fullname: Zhou, Yu-Qing – sequence: 3 givenname: Xiaoli surname: Zhang fullname: Zhang, Xiaoli – sequence: 4 givenname: David A surname: Steinman fullname: Steinman, David A – sequence: 5 givenname: R Mark surname: Henkelman fullname: Henkelman, R Mark |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25070260$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkE9LxDAUxIOsuH_04BeQHL10zUuTTT3K4urCghc9l9fkFbM0TW1aZL-9q67gaQbmxzDMnE3a2BJj1yCWAKDvYKmELECoMzYDLYusuNcw-eenbJ7SXgiAQokLNpVaGCFXYsbctu3GIfE69jyN1Z7skKWOrK-95TaGY4iDjy02vG5G77g7tBi8TTz5MDY_GY81r5oY3RGJn9y3fHgnHuKYiGPsB7xk5zU2ia5OumBvm8fX9XO2e3narh92GeYGhqwC6Yx2KEy-KtCCVjlVhUMUtiZSijRUukaSIBUIhJUpkHJnrbOVU6jlgt3-9nZ9_BgpDWXwyVLTYEvHNSVonecSjPlGb07oWAVyZdf7gP2h_HtGfgEm9mfv |
| CitedBy_id | crossref_primary_10_1007_s13239_021_00600_4 crossref_primary_10_1002_cnm_3457 crossref_primary_10_1098_rsos_171447 crossref_primary_10_1016_j_ultrasmedbio_2025_01_012 crossref_primary_10_1007_s10439_015_1310_y crossref_primary_10_1186_s12938_016_0270_2 crossref_primary_10_1016_j_jbiomech_2016_06_010 |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1115/1.4028104 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Medicine Engineering Forestry |
| EISSN | 1528-8951 |
| ExternalDocumentID | 25070260 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Canadian Institutes of Health Research grantid: MOP-10290 |
| GroupedDBID | --- -~X .DC .GJ 29J 4.4 53G 5AI 5GY 6TJ AAYJJ ABJNI ACBEA ACGFO ACGFS ACKMT ACXMS ADPDT AI. ALEEW ALMA_UNASSIGNED_HOLDINGS CGR CS3 CUY CVF EBS ECM EIF EJD F5P H~9 L7B NPM P2P RAI RNS RXW TAE TN5 UKR VH1 WHG ZE2 7X8 AGNGV |
| ID | FETCH-LOGICAL-a371t-b12d75da07368ac1543eb8daa0cfee44e51b5fae212410a1678ae3dccdcbd4a52 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000341298400008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1528-8951 |
| IngestDate | Thu Jul 10 20:46:10 EDT 2025 Thu Apr 03 07:09:06 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a371t-b12d75da07368ac1543eb8daa0cfee44e51b5fae212410a1678ae3dccdcbd4a52 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 25070260 |
| PQID | 1553321775 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_1553321775 pubmed_primary_25070260 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-10-01 |
| PublicationDateYYYYMMDD | 2014-10-01 |
| PublicationDate_xml | – month: 10 year: 2014 text: 2014-10-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Journal of biomechanical engineering |
| PublicationTitleAlternate | J Biomech Eng |
| PublicationYear | 2014 |
| SSID | ssj0011840 |
| Score | 2.14142 |
| Snippet | Mouse models are an important way for exploring relationships between blood hemodynamics and eventual plaque formation. We have developed a mouse model of... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 101008 |
| SubjectTerms | Animals Aorta - physiology Aorta, Thoracic - physiology Hemodynamics Hydrodynamics Image Processing, Computer-Assisted Mice Models, Cardiovascular |
| Title | Inputs for subject-specific computational fluid dynamics simulation of blood flow in the mouse aorta |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/25070260 https://www.proquest.com/docview/1553321775 |
| Volume | 136 |
| WOSCitedRecordID | wos000341298400008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ1LSwMxEMeDWhE9-Kiv-iKC1-hmN9ukJxGxKNjSg0pvZfJYKOhudVv9-k52U-pFELwse9iFkMfkl5nJ_Am5iKLMJLiGmBLcMiFtzDRSBNNSQtxWAFlVvvjlUfb7ajjsDILDrQxplXObWBlqWxjvI7_y-jYJ8rNMryfvzKtG-ehqkNBYJo0EUcandMnhIorgTy9VvdRYMYUoESoLIQRd8Us8OCle6bP9QpbVDtPd-m_btslmYEt6U0-GHbLk8ibZ-FFxsEnWvBSn13fD114Iq-8S-5BPZtOSIsDScqa9a4b5K5g-jYiaSvch-Axp9jobW2prHfuSluO3oP9Fi4xWWfD4SfFFxzlFtKTeseAoeMbfI8_du6fbexbkFxgkkk-Z5rGVqQU0AjhoBlkrcVpZgMhkzgnhUq7TDBxufoJHwHHbA5dYY6zRVkAa75OVvMjdIaExHjNBdNqaQyxcx4DlCcRK8YwrF2nRIufzjh3h9PYxC8gdtnC06NoWOahHZzSp63CMkN6kL4l29Ie_j8k6oo6o0_BOSCPDxe1Oyar5nI7Lj7Nq3uCzP-h9A6GUzj0 |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Inputs+for+subject-specific+computational+fluid+dynamics+simulation+of+blood+flow+in+the+mouse+aorta&rft.jtitle=Journal+of+biomechanical+engineering&rft.au=Van+Doormaal%2C+Mark&rft.au=Zhou%2C+Yu-Qing&rft.au=Zhang%2C+Xiaoli&rft.au=Steinman%2C+David+A&rft.date=2014-10-01&rft.eissn=1528-8951&rft.volume=136&rft.issue=10&rft.spage=101008&rft_id=info:doi/10.1115%2F1.4028104&rft_id=info%3Apmid%2F25070260&rft_id=info%3Apmid%2F25070260&rft.externalDocID=25070260 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1528-8951&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1528-8951&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1528-8951&client=summon |