Imaging and Image Processing Techniques for High-Resolution Visualization of Connective Tissue with MRI: Application to Fascia, Aponeurosis, and Tendon
Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generat...
Saved in:
| Published in | Journal of imaging Vol. 11; no. 2; p. 43 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
04.02.2025
MDPI |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2313-433X 2313-433X |
| DOI | 10.3390/jimaging11020043 |
Cover
Loading…
| Abstract | Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3–5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues. |
|---|---|
| AbstractList | Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T
* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3-5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues. Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T[sub.2] * decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3–5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues. Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3-5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues.Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3-5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues. Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3–5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues. Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T 2 * decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3–5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues. |
| Audience | Academic |
| Author | Holdsworth, Samantha J. Perera, Meeghage Randika Bydder, Graeme M. Handsfield, Geoffrey G. |
| AuthorAffiliation | 2 Mātai Medical Research Institute, Tairāwhiti-Gisborne 4010, New Zealand; gbydder@health.ucsd.edu (G.M.B.); s.holdsworth@matai.org.nz (S.J.H.) 7 Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27606, USA 3 Department of Radiology, University of California, San Diego, CA 92093, USA 6 Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA 4 Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland, Auckland 1010, New Zealand 1 Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand; mper162@aucklanduni.ac.nz 5 Department of Orthopaedics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA |
| AuthorAffiliation_xml | – name: 1 Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand; mper162@aucklanduni.ac.nz – name: 3 Department of Radiology, University of California, San Diego, CA 92093, USA – name: 6 Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA – name: 4 Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland, Auckland 1010, New Zealand – name: 5 Department of Orthopaedics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA – name: 2 Mātai Medical Research Institute, Tairāwhiti-Gisborne 4010, New Zealand; gbydder@health.ucsd.edu (G.M.B.); s.holdsworth@matai.org.nz (S.J.H.) – name: 7 Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27606, USA |
| Author_xml | – sequence: 1 givenname: Meeghage Randika surname: Perera fullname: Perera, Meeghage Randika – sequence: 2 givenname: Graeme M. surname: Bydder fullname: Bydder, Graeme M. – sequence: 3 givenname: Samantha J. orcidid: 0000-0002-2929-1169 surname: Holdsworth fullname: Holdsworth, Samantha J. – sequence: 4 givenname: Geoffrey G. orcidid: 0000-0001-5661-0631 surname: Handsfield fullname: Handsfield, Geoffrey G. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39997545$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkk1vEzEQhleoiJbSOydkiQuHpvhrP8wFRRGlkYpAVUDcLK89u3G0sYO9W9T-Ef4u3qRUSYUsy-Px49cz43mZHTnvIMteE3zBmMDvV3atWutaQjDFmLNn2QllhE04Yz-P9uzj7CzGFcaYCJqmeJEdMyFEmfP8JPsz34kg5QwabUDfgtcQ4-hcgF46-2uAiBof0JVtl5MbiL4beusd-mHjoDp7r7Y736CZdw50b28BLWyMA6Dftl-iLzfzD2i62XRW79Deo0sVtVXnyZ2yGoKPNp5vg1iAM969yp43qotw9rCeZt8vPy1mV5Prr5_ns-n1RHNO-kkJghfa0JqDKimpWE2JaohQvCmNKCFnDRY1zkEoJqBWWlBIR4RWuTYAJTvN5jtd49VKbkKqabiTXlm5dfjQShV6qzuQptQ5ScWuc2w4bYpaGA4M0woEMZqKpPVxp7UZ6jUYDa4PqjsQPTxxdilbfysJqfKiEGM07x4Ugh-r3su1jRq6TjnwQ5SMlFiwivMioW-foCs_BJdqNVIEF0WO96hWpQysa3x6WI-iclpRUZZc0JG6-A-VhoG11el_Gpv8Bxfe7Gf6mOK_vkoA3gE6_WwM0DwiBMuxe-XT7mV_AXC25CE |
| Cites_doi | 10.1016/j.jbiomech.2015.01.012 10.1007/s11999-008-0594-8 10.1152/japplphysiol.00976.2016 10.1002/jmri.22596 10.1007/s00330-010-2040-z 10.1002/jmri.23608 10.1002/nbm.3709 10.1371/journal.pone.0205944 10.1111/sms.13407 10.1016/j.jbiomech.2016.11.062 10.1002/mrm.21620 10.21037/qims-23-687 10.1109/ICITIIT49094.2020.9071556 10.1002/nbm.1463 10.1016/j.neuroimage.2011.09.015 10.3109/03008207809152283 10.1049/iet-ipr.2013.0693 10.1002/jmri.22371 10.1046/j.1469-7580.1999.19410079.x 10.1002/acr.21868 10.1016/j.neuroimage.2006.01.015 10.1016/j.mri.2012.02.003 10.1016/S0006-3495(01)76262-5 10.1016/j.media.2004.06.002 10.1016/S0006-3495(01)76081-X 10.1016/j.crad.2003.11.021 10.1002/mrm.22715 10.18240/ijo.2023.08.22 10.2214/AJR.13.11081 10.1016/S0730-725X(02)00515-5 10.1002/mrm.21819 10.1002/jmri.20593 10.1002/jor.25654 10.1186/s13244-019-0735-5 10.1088/1361-6560/acf10c 10.1016/j.pnmrs.2019.07.001 10.1007/s00330-010-2044-8 10.1002/jmri.22873 10.1007/s00330-007-0713-z 10.2217/iim.11.60 10.1371/journal.pone.0080713 10.1007/s00421-004-1186-2 10.1186/1471-2474-10-151 10.3390/diagnostics11020177 10.1111/j.1469-7580.2012.01538.x 10.3389/fspor.2020.00070 10.1063/5.0086459 10.1002/jmri.23884 10.1007/s00276-011-0876-9 10.1186/s12891-024-08254-8 10.1097/SCS.0000000000006135 10.1002/mrm.10198 10.3390/bioengineering11050441 10.2463/mrms.9.111 10.1016/j.compmedimag.2017.02.001 10.1097/RCT.0b013e3182ab60ea 10.1002/jcu.10168 10.1111/joa.12902 10.1016/j.neuroimage.2019.116137 10.2214/AJR.09.3368 10.1111/joa.12370 10.3390/s22218202 10.1002/acr.20628 10.1016/j.neuroimage.2008.10.055 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2025 MDPI AG 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 by the authors. 2025 |
| Copyright_xml | – notice: COPYRIGHT 2025 MDPI AG – notice: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2025 by the authors. 2025 |
| DBID | AAYXX CITATION NPM 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/jimaging11020043 |
| DatabaseName | CrossRef PubMed ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Korea SciTech Premium Collection ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2313-433X |
| ExternalDocumentID | oai_doaj_org_article_d7c51110b50d42f6b9d4e3028e91dc29 PMC11856697 A829774926 39997545 10_3390_jimaging11020043 |
| Genre | Journal Article |
| GeographicLocations | New Zealand |
| GeographicLocations_xml | – name: New Zealand |
| GrantInformation_xml | – fundername: RSNZ Marsden Fast-Start Grant. grantid: (Marsden Grant No. 3716083) – fundername: RSNZ Marsden Fast-Start grantid: 3716083 – fundername: Wu Tsai Human Performance Alliance, Mātai Blue Sky Grant and Kānoa RDU New Zealand |
| GroupedDBID | 5VS 8FE 8FG AADQD AAFWJ AAYXX ADBBV ADMLS AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS ARCSS BCNDV BENPR BGLVJ CCPQU CITATION GROUPED_DOAJ HCIFZ IAO IHR ITC KQ8 MODMG M~E OK1 P62 PGMZT PHGZM PHGZT PIMPY PROAC RPM NPM PQGLB PMFND ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS 7X8 5PM PUEGO |
| ID | FETCH-LOGICAL-c441t-7e946cd2b4ea72183b21af19a4f7d97e53f09b05e9a39ebac92e4f71285cdee73 |
| IEDL.DBID | BENPR |
| ISSN | 2313-433X |
| IngestDate | Wed Aug 27 01:27:21 EDT 2025 Thu Aug 21 18:27:40 EDT 2025 Fri Jul 11 01:52:43 EDT 2025 Fri Jul 25 21:57:46 EDT 2025 Tue Jun 17 21:59:27 EDT 2025 Tue Jun 10 20:57:34 EDT 2025 Mon Jul 21 06:01:44 EDT 2025 Tue Jul 01 04:20:05 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | fascia ultra-short-T2 MRI medical image processing |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c441t-7e946cd2b4ea72183b21af19a4f7d97e53f09b05e9a39ebac92e4f71285cdee73 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-2929-1169 0000-0001-5661-0631 |
| OpenAccessLink | https://www.proquest.com/docview/3171066506?pq-origsite=%requestingapplication% |
| PMID | 39997545 |
| PQID | 3171066506 |
| PQPubID | 2059558 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d7c51110b50d42f6b9d4e3028e91dc29 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11856697 proquest_miscellaneous_3170938446 proquest_journals_3171066506 gale_infotracmisc_A829774926 gale_infotracacademiconefile_A829774926 pubmed_primary_39997545 crossref_primary_10_3390_jimaging11020043 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20250204 |
| PublicationDateYYYYMMDD | 2025-02-04 |
| PublicationDate_xml | – month: 2 year: 2025 text: 20250204 day: 4 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Journal of imaging |
| PublicationTitleAlternate | J Imaging |
| PublicationYear | 2025 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Goh (ref_24) 2012; 35 Kermarrec (ref_7) 2010; 195 Damon (ref_9) 2012; 30 Liu (ref_64) 2011; 33 Damon (ref_12) 2002; 48 Miao (ref_2) 2020; 31 Valentinitsch (ref_38) 2013; 37 Cerda (ref_53) 2015; 227 ref_54 Ward (ref_57) 2009; 467 Jenkinson (ref_47) 2012; 62 Chen (ref_23) 2023; 16 Yushkevich (ref_44) 2006; 31 Bolsterlee (ref_56) 2015; 48 ref_17 Elzibak (ref_22) 2014; 38 Damon (ref_19) 2011; 3 Beattie (ref_43) 2012; 64 Ponrartana (ref_16) 2014; 202 Kastelic (ref_3) 1978; 6 Sinha (ref_11) 2006; 24 Sinha (ref_28) 2011; 34 Froeling (ref_18) 2012; 36 Fu (ref_41) 2004; 8 Heemskerk (ref_48) 2009; 61 Zijta (ref_25) 2011; 21 Bird (ref_61) 2019; 29 Handsfield (ref_4) 2017; 51 Boussouar (ref_46) 2017; 56 ref_62 Bolsterlee (ref_65) 2017; 122 Okamoto (ref_27) 2010; 9 ref_29 Budzik (ref_15) 2007; 17 Hodgson (ref_58) 2011; 65 Rousset (ref_26) 2012; 221 ref_36 ref_34 ref_33 Stecco (ref_52) 2011; 33 Bonny (ref_14) 2002; 20 Wilke (ref_51) 2019; 234 Kretzers (ref_10) 1999; 194 Tournier (ref_31) 2019; 202 Kirchgesner (ref_63) 2019; 10 Napadow (ref_20) 2001; 80 ref_39 Maderwald (ref_13) 2004; 93 Woolrich (ref_32) 2009; 45 Cone (ref_60) 2023; 41 Weiger (ref_1) 2019; 114–115 Heemskerk (ref_8) 2010; 23 Maly (ref_42) 2013; 65 ref_45 Goh (ref_55) 2003; 31 Hodgson (ref_50) 2011; 21 ref_40 Wedeen (ref_21) 2001; 80 Hwang (ref_30) 2023; 13 Yoo (ref_37) 2014; 8 ref_49 Robson (ref_59) 2004; 59 Qian (ref_35) 2008; 60 ref_5 Ma (ref_6) 2022; 9 |
| References_xml | – volume: 48 start-page: 1133 year: 2015 ident: ref_56 article-title: Comparison of measurements of medial gastrocnemius architectural parameters from ultrasound and diffusion tensor images publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2015.01.012 – volume: 467 start-page: 1074 year: 2009 ident: ref_57 article-title: Are current measurements of lower extremity muscle architecture accurate? publication-title: Clin. Orthop. Relat. Res. doi: 10.1007/s11999-008-0594-8 – volume: 122 start-page: 727 year: 2017 ident: ref_65 article-title: How does passive lengthening change the architecture of the human medial gastrocnemius muscle? publication-title: J. Appl. Physiol. doi: 10.1152/japplphysiol.00976.2016 – volume: 34 start-page: 107 year: 2011 ident: ref_28 article-title: Reproducibility analysis of diffusion tensor indices and fiber architecture of human calf muscles in vivo at 1.5 Tesla in neutral and plantarflexed ankle positions at rest publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.22596 – volume: 21 start-page: 1144 year: 2011 ident: ref_50 article-title: Imaging of the Achilles tendon in spondyloarthritis: A comparison of ultrasound and conventional, short and ultrashort echo time MRI with and without intravenous contrast publication-title: Eur. Radiol. doi: 10.1007/s00330-010-2040-z – volume: 36 start-page: 237 year: 2012 ident: ref_18 article-title: Diffusion-tensor MRI reveals the complex muscle architecture of the human forearm publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.23608 – ident: ref_34 doi: 10.1002/nbm.3709 – ident: ref_39 – ident: ref_29 doi: 10.1371/journal.pone.0205944 – volume: 29 start-page: 808 year: 2019 ident: ref_61 article-title: Validity and reliability of measurements of aponeurosis dimensions from magnetic resonance images publication-title: Scand. J. Med. Sci. Sports doi: 10.1111/sms.13407 – volume: 51 start-page: 17 year: 2017 ident: ref_4 article-title: A 3D model of the Achilles tendon to determine the mechanisms underlying nonuniform tendon displacements publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2016.11.062 – volume: 60 start-page: 135 year: 2008 ident: ref_35 article-title: Acquisition-weighted stack of spirals for fast high-resolution three-dimensional ultra-short echo time MR imaging publication-title: Magn. Reson. Med. doi: 10.1002/mrm.21620 – volume: 13 start-page: 8447 year: 2023 ident: ref_30 article-title: Ultrashort echo time pulse sequences for visualization of deep peripheral fasciae and epimysium in porcine models with histologic correlations publication-title: Quant. Imaging Med. Surg. doi: 10.21037/qims-23-687 – ident: ref_36 doi: 10.1109/ICITIIT49094.2020.9071556 – volume: 23 start-page: 294 year: 2010 ident: ref_8 article-title: Repeatability of DTI-based skeletal muscle fiber tracking publication-title: NMR Biomed. doi: 10.1002/nbm.1463 – volume: 62 start-page: 782 year: 2012 ident: ref_47 article-title: Bayesian analysis of neuromaging data in FSL publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.09.015 – volume: 6 start-page: 11 year: 1978 ident: ref_3 article-title: The multicomposite structure of tendon publication-title: Connect. Tissue Res. doi: 10.3109/03008207809152283 – volume: 8 start-page: 815 year: 2014 ident: ref_37 article-title: Image restoration by blind-Wiener filter publication-title: IET Image Process. doi: 10.1049/iet-ipr.2013.0693 – volume: 33 start-page: 173 year: 2011 ident: ref_64 article-title: Diagnostic value of delineating deep fascia in distinguishing between benign and malignant soft-tissue tumors in lower limbs using 3.0 T magnetic resonance imaging publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.22371 – volume: 194 start-page: 79 year: 1999 ident: ref_10 article-title: Diffusion tensor imaging in biomechanical studies of skeletal muscle function publication-title: J. Anat. doi: 10.1046/j.1469-7580.1999.19410079.x – volume: 65 start-page: 44 year: 2013 ident: ref_42 article-title: Relationship of intermuscular fat volume in the thigh with knee extensor strength and physical performance in women at risk of or with knee osteoarthritis publication-title: Arthritis Care Res. doi: 10.1002/acr.21868 – volume: 31 start-page: 1116 year: 2006 ident: ref_44 article-title: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability publication-title: Neuroimage doi: 10.1016/j.neuroimage.2006.01.015 – volume: 30 start-page: 589 year: 2012 ident: ref_9 article-title: Polynomial fitting of DT-MRI fiber tracts allows accurate estimation of muscle architectural parameters publication-title: Magn. Reson. Imaging doi: 10.1016/j.mri.2012.02.003 – volume: 80 start-page: 2968 year: 2001 ident: ref_20 article-title: Quantitative analysis of three-dimensional-resolved fiber architecture in heterogeneous skeletal muscle tissue using NMR and optical imaging methods publication-title: Biophys. J. doi: 10.1016/S0006-3495(01)76262-5 – volume: 8 start-page: 177 year: 2004 ident: ref_41 article-title: Integrating watersheds and critical point analysis for object detection in discrete 2D images publication-title: Med. Image Anal. doi: 10.1016/j.media.2004.06.002 – volume: 80 start-page: 1024 year: 2001 ident: ref_21 article-title: Demonstration of primary and secondary muscle fiber architecture of the bovine tongue by diffusion tensor magnetic resonance imaging publication-title: Biophys. J. doi: 10.1016/S0006-3495(01)76081-X – volume: 59 start-page: 727 year: 2004 ident: ref_59 article-title: Magnetic resonance imaging of the Achilles tendon using ultrashort TE (UTE) pulse sequences publication-title: Clin. Radiol. doi: 10.1016/j.crad.2003.11.021 – volume: 65 start-page: 1372 year: 2011 ident: ref_58 article-title: Quantitative magnetization transfer ultrashort echo time imaging of the Achilles tendon publication-title: Magn. Reson. Med. doi: 10.1002/mrm.22715 – volume: 16 start-page: 1350 year: 2023 ident: ref_23 article-title: Diffusion tensor imaging of horizontal extraocular muscles in patients with concomitant and paralytic esotropia publication-title: Int. J. Ophthalmol. doi: 10.18240/ijo.2023.08.22 – volume: 202 start-page: 567 year: 2014 ident: ref_16 article-title: Repeatability of chemical-shift-encoded water-fat MRI and diffusion-tensor imaging in lower extremity muscles in children publication-title: Am. J. Roentgenol. doi: 10.2214/AJR.13.11081 – volume: 20 start-page: 395 year: 2002 ident: ref_14 article-title: Water diffusion features as indicators of muscle structure ex vivo publication-title: Magn. Reson. Imaging doi: 10.1016/S0730-725X(02)00515-5 – volume: 61 start-page: 467 year: 2009 ident: ref_48 article-title: Quantitative assessment of DTI-based muscle fiber tracking and optimal tracking parameters publication-title: Magn. Reson. Med. doi: 10.1002/mrm.21819 – volume: 24 start-page: 182 year: 2006 ident: ref_11 article-title: In vivo diffusion tensor imaging of the human calf muscle publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.20593 – volume: 41 start-page: 2315 year: 2023 ident: ref_60 article-title: 3D characterization of the triple-bundle Achilles tendon from in vivo high-field MRI publication-title: J. Orthop. Res. doi: 10.1002/jor.25654 – volume: 10 start-page: 47 year: 2019 ident: ref_63 article-title: Fasciae of the musculoskeletal system: MRI findings in trauma, infection and neoplastic diseases publication-title: Insights Imaging doi: 10.1186/s13244-019-0735-5 – ident: ref_17 doi: 10.1088/1361-6560/acf10c – volume: 114–115 start-page: 237 year: 2019 ident: ref_1 article-title: Short-T2 MRI: Principles and recent advances publication-title: Prog. Nuclear Magn. Reson. Spectrosc. doi: 10.1016/j.pnmrs.2019.07.001 – volume: 21 start-page: 1243 year: 2011 ident: ref_25 article-title: Feasibility of diffusion tensor imaging (DTI) with fibre tractography of the normal female pelvic floor publication-title: Eur. Radiol. doi: 10.1007/s00330-010-2044-8 – volume: 35 start-page: 820 year: 2012 ident: ref_24 article-title: Diffusion tensor imaging of the anal canal at 3 tesla: Feasibility and reproducibility of anisotropy measures publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.22873 – volume: 17 start-page: 3079 year: 2007 ident: ref_15 article-title: In vivo MR tractography of thigh muscles using diffusion imaging: Initial results publication-title: Eur. Radiol. doi: 10.1007/s00330-007-0713-z – volume: 3 start-page: 675 year: 2011 ident: ref_19 article-title: Diffusion-tensor MRI-based skeletal muscle fiber tracking publication-title: Imaging Med. doi: 10.2217/iim.11.60 – ident: ref_33 doi: 10.1371/journal.pone.0080713 – volume: 93 start-page: 253 year: 2004 ident: ref_13 article-title: Diffusive sensitivity to muscle architecture: A magnetic resonance diffusion tensor imaging study of the human calf publication-title: Eur. J. Appl. Physiol. doi: 10.1007/s00421-004-1186-2 – ident: ref_49 doi: 10.1186/1471-2474-10-151 – ident: ref_54 doi: 10.3390/diagnostics11020177 – volume: 221 start-page: 221 year: 2012 ident: ref_26 article-title: In vivo visualization of the levator ani muscle subdivisions using MR fiber tractography with diffusion tensor imaging publication-title: J. Anat. doi: 10.1111/j.1469-7580.2012.01538.x – ident: ref_5 doi: 10.3389/fspor.2020.00070 – volume: 9 start-page: 041303 year: 2022 ident: ref_6 article-title: Making the invisible visible—Ultrashort echo time magnetic resonance imaging: Technical developments and applications publication-title: Appl. Phys. Rev. doi: 10.1063/5.0086459 – volume: 37 start-page: 917 year: 2013 ident: ref_38 article-title: Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.23884 – volume: 33 start-page: 891 year: 2011 ident: ref_52 article-title: Hyaluronan within fascia in the etiology of myofascial pain publication-title: Surg. Radiol. Anat. doi: 10.1007/s00276-011-0876-9 – ident: ref_45 doi: 10.1186/s12891-024-08254-8 – volume: 31 start-page: 182 year: 2020 ident: ref_2 article-title: The Type and Content of Collagen Fibers of the Levator Aponeurosis in Patients with Simple Congenital Blepharoptosis publication-title: J. Craniofac. Surg. doi: 10.1097/SCS.0000000000006135 – volume: 48 start-page: 97 year: 2002 ident: ref_12 article-title: Validation of diffusion tensor MRI-based muscle fiber tracking publication-title: Magn. Reson. Med. doi: 10.1002/mrm.10198 – ident: ref_62 doi: 10.3390/bioengineering11050441 – volume: 9 start-page: 111 year: 2010 ident: ref_27 article-title: Gender differences in MR muscle tractography publication-title: Magn. Reson. Med. Sci. doi: 10.2463/mrms.9.111 – volume: 56 start-page: 60 year: 2017 ident: ref_46 article-title: Plantar fascia segmentationand thickness estimation in ultrasound images publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2017.02.001 – volume: 38 start-page: 329 year: 2014 ident: ref_22 article-title: Diffusion tensor imaging of the normal foot at 3 T publication-title: J. Comput. Assist. Tomogr. doi: 10.1097/RCT.0b013e3182ab60ea – volume: 31 start-page: 239 year: 2003 ident: ref_55 article-title: Iliotibial band thickness: Sonographic measurements in asymptomatic volunteers publication-title: J. Clin. Ultrasound doi: 10.1002/jcu.10168 – volume: 234 start-page: 43 year: 2019 ident: ref_51 article-title: Fascia thickness, aging and flexibility: Is there an association? publication-title: J. Anat. doi: 10.1111/joa.12902 – volume: 202 start-page: 116137 year: 2019 ident: ref_31 article-title: MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.116137 – volume: 195 start-page: 352 year: 2010 ident: ref_7 article-title: In vivo diffusion tensor imaging and tractography of human thigh muscles in healthy subjects publication-title: Am. J. Roentgenol. doi: 10.2214/AJR.09.3368 – volume: 227 start-page: 665 year: 2015 ident: ref_53 article-title: In vivo relationship between pelvis motion and deep fascia displacement of the medial gastrocnemius: Anatomical and functional implications publication-title: J. Anat. doi: 10.1111/joa.12370 – ident: ref_40 doi: 10.3390/s22218202 – volume: 64 start-page: 22 year: 2012 ident: ref_43 article-title: Longitudinal Changes in Intermuscular Fat Volume and Quadriceps Muscle Volume in the Thighs of Female Osteoarthritis Initiative Participants publication-title: Arthritis Care Res. doi: 10.1002/acr.20628 – volume: 45 start-page: S173 year: 2009 ident: ref_32 article-title: Bayesian analysis of neuroimaging data in FSL publication-title: Neuroimage doi: 10.1016/j.neuroimage.2008.10.055 |
| SSID | ssj0001920199 |
| Score | 2.2819355 |
| Snippet | Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging,... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | 43 |
| SubjectTerms | Cluster analysis Clustering Connective tissue Connective tissues Edge detection Evaluation fascia Fiber orientation High resolution Image acquisition Image processing Image resolution Magnetic resonance imaging medical image processing Medical imaging Methods Modelling MRI Muscles Musculoskeletal system Physiological aspects Signal to noise ratio Statistical analysis Tendons Tensors ultra-short-T2 Vector quantization Visualization |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwEB6hPcEBsTwDCzISEkLaaJPYiWtuBVHtIi0H1EV7s_wUXWlTRLu_Zf8uM3ZaEjhw4VCp8riNk_ns-SaeGQO8EREXxihj2XlpSmHRZzWtrUtvLFqr6GrXUO7w-Zfu9EJ8vmwvR0d9UUxYLg-cH9yJlw45QV3ZtvKiiZ1VXgSOVjGo2rsmpe6hzRs5U1eZt-BH5X1Jjn79ydXqOh37g39FwOATO5TK9f-9KI-s0jRicmSCFg_g_sAd2TyP-RDuhP4h3BtVFHwEt2f5-sz0ntH3wIZUAGpc7gq2bhhyVUYxHiW9v8_oY99WG0qxzImZbB1ZioJJCyJbJgUxem3Lzr-evWfz3zvfbLtmC4O21Bxj8zqXyFxtjtMgloHOC3kMF4tPy4-n5XD2QumQIG1LGZTonG-sCEYSjbJNbWKtjIjSKxlaHitlqzYow1WwxqkmoAitXet8CJI_gYMeL_gMWOWQGpsOWfyMizY443AVcNy2FqHAbVfAu50m9I9cYkOja0Ja039qrYAPpKp9PyqOnRoQMnqAjP4XZAp4S4rWNIVRmzignImAw6ViWHpO6caSKikWcDTpiVPPTcU7qOhh6m80Qrym_awKxa_3YvolhbP1YX2T-lSKz9AVL-BpRtb-lpAxKom8toDZBHOTe55K-tX3VBgcnUVk50o-_x9P6QXcbeisY4pQF0dwsP15E14iAdvaV2mu_QIvITGc priority: 102 providerName: Directory of Open Access Journals |
| Title | Imaging and Image Processing Techniques for High-Resolution Visualization of Connective Tissue with MRI: Application to Fascia, Aponeurosis, and Tendon |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39997545 https://www.proquest.com/docview/3171066506 https://www.proquest.com/docview/3170938446 https://pubmed.ncbi.nlm.nih.gov/PMC11856697 https://doaj.org/article/d7c51110b50d42f6b9d4e3028e91dc29 |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1ba9VAEB5s-6IP4t1oPawgiNDQJLvJZn2RU-mxFVqknErfwt6iRzCpzelv8e86s8m5RMG3sLshG2Z25pvZuQC8ETUKxlrWceGkjoVBm1XnJo2dNqitapvajHKHz86Lk0vx-Sq_Ghxu3RBWuZKJQVC71pKP_BDfTOmaICk-XP-KqWsU3a4OLTR2YA9FcInG197R8fmXi42XRaGCU6q_n-Ro3x_-WPwM7X9Q7RGD8JE-CmX7_xXOW9ppHDm5pYpmD-D-gCHZtCf6Q7jjm0dwb6uy4GP4fdp_n-nGMXr2bEgJoMH5qnBrxxCzMor1iMmP33Mh-7roKNWyT9Bkbc1CNEwQjGweCMXIfcvOLk7fs-nmBpwtWzbTqFP1AQ63fanMRXcQNjH31DfkCVzOjucfT-KhB0NsESgtY-mVKKzLjPBaEpwyWarrVGlRS6ekz3mdKJPkXmmuvNFWZR6nkBi5dd5L_hR2G_zgc2CJRYisC0TzJRe5t9qiNLDc5AZZgpsigncrSlTXfamNCk0Uolr1N9UiOCJSrddRkeww0N58q4YzVzlpEU6mickTJ7K6MMoJzxFQeZU6m6kI3hKhKzrKSE3cUJ-RgNuloljVlNKOJVVUjGB_tBKPoB1Pr1ilGkRAV20YNoLX62l6k8LaGt_ehjWJ4iWa5BE86zlr_UuIHJVEfBtBOeK50T-PZ5rF91AgHI1GROlKvvj_vl7C3Yy6GVMMutiH3eXNrX-FEGtpJrBTzj5NhtM0CY6KSfCE_QGCoC24 |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR3JbtNA9KmUA3BA7BgKDBIIIdWq7Rl7MkgIhSUkdDmgtOrNzGYIEnFpUiG-hL_gG3lvHCcxSNx6i-aN47HePm8DeCIqFIyVrOLCSR0Lgz6rzk0aO21QW1U2tRnVDu8fFMND8eE4P96A320tDKVVtjIxCGpXW7oj38EnUwoTJMWrk-8xTY2i6Go7QqMhi13_8we6bLOXo7eI36dZNng3fjOMF1MFYouqfx5Lr0RhXWaE15IMBJOlukqVFpV0SvqcV4kySe6V5sobbVXmEYRyPLfOe8nxfy_ARcG5Io7qDd6v7nQUqlOlmmgowpOdr5NvYdgQKlkiR97RfmFIwL-qYE0XdvM01xTf4BpcXVisrN-Q2HXY8NMbcGWtj-FN-DVq3s_01DH67dmiAIEWx22b2BlDC5lRZklMUYOG5tnRZEaFnU05KKsrFnJvghhm40AWjC6L2f7H0QvWX8Xb2bxmA40aXG_jct005pzMtsMhxp6mlNyCw3PBzW3YnOIL7wJLLBrkukDfocdF7q22KHssN7lBAuSmiOB5i4nypGnsUaJDRFgr_8ZaBK8JVct91JI7LNSnn8sFh5dOWjRe08TkiRNZVRjlhOdovnmVOpupCJ4RoksSHIhNPFBT_4DHpRZcZZ-KnCX1b4xgq7MTGd52wS2plAuBMytX7BHB4yWYnqQkuqmvz8KeRPGeELjnTkNZy09CO1VJtKYj6HVorvPNXch08iW0I0cXFX0CJe_9_1yP4NJwvL9X7o0Odu_D5YzmKFP2u9iCzfnpmX-Axt3cPAwcxeDTebPwH2bbZ1E |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1baxNBFD7UFEQfxLurVUdQROiSvczuZASR1DY01oZSUunbOrfVCGZrN0X8Jf4Xf53n7CXJKvjWtzAzm53l3L4zcy4Az3mOijEXuZ9aoXyu0WdViQ59qzRaq9yEJqLc4cNJun_C358mpxvwu82FobDKVidWitoWhs7I-_hkSNcEQdrPm7CIo93R27PvPnWQopvWtp1GzSIH7ucPdN_KN-NdpPWLKBrtTd_t-02HAd8gDFj4wkmeGhtp7pQgsKCjUOWhVDwXVgqXxHkgdZA4qWLptDIycjiFOj0x1jkR4_9egU2BXlHQg82dvcnR8eqER6JxlbK-G41jGfS_zr5VrYfQ5BJzxh1bWLUM-NcwrFnGbtTmmhkc3YQbDX5lw5rhbsGGm9-G62tVDe_Ar3H9fqbmltFvx5p0BBqctkVjS4Z4mVGciU93CLUEsI-zktI86-RQVuSsisSplDKbVkzC6OiYHR6PX7Ph6vadLQo2UmjP1TYOF3WZzlm5XW1i6qhnyV04uRTq3IPeHF_4AFhgEJ6rFD2JQcwTZ5RBTWRinWhkx1inHrxqKZGd1WU-MnSPiGrZ31TzYIdItVxHBbqrgeL8c9bIe2aFQSgbBjoJLI_yVEvLXYxgzsnQmkh68JIInZEaQWrihupsCNwuFeTKhpTyLKiaowdbnZUo_qY73bJK1qifMlsJiwfPltP0JIXUzV1xUa0JZDzgHNfcrzlr-UmIWqVAbO3BoMNznW_uzsxnX6ri5OiwoocgxcP_7-spXEXxzT6MJweP4FpETZUpFJ5vQW9xfuEeI9Jb6CeNSDH4dNlS_AfUTGzj |
| 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=Imaging+and+Image+Processing+Techniques+for+High-Resolution+Visualization+of+Connective+Tissue+with+MRI%3A+Application+to+Fascia%2C+Aponeurosis%2C+and+Tendon&rft.jtitle=Journal+of+imaging&rft.au=Perera%2C+Meeghage+Randika&rft.au=Bydder%2C+Graeme+M&rft.au=Holdsworth%2C+Samantha+J&rft.au=Handsfield%2C+Geoffrey+G&rft.date=2025-02-04&rft.eissn=2313-433X&rft.volume=11&rft.issue=2&rft_id=info:doi/10.3390%2Fjimaging11020043&rft_id=info%3Apmid%2F39997545&rft.externalDocID=39997545 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2313-433X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2313-433X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2313-433X&client=summon |