Deep learning-based reconstruction of ultrasound images from raw channel data
Purpose We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reco...
Gespeichert in:
| Veröffentlicht in: | International journal for computer assisted radiology and surgery Jg. 15; H. 9; S. 1487 - 1490 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer International Publishing
01.09.2020
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 1861-6410, 1861-6429, 1861-6429 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Purpose
We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions.
Methods
We propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data.
Results
The images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is
4.23
±
1.52
for the phantom images and
6.09
±
0.72
for the in vivo data.
Conclusion
The result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest. |
|---|---|
| AbstractList | We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions.PURPOSEWe investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions.We propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data.METHODSWe propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data.The images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is [Formula: see text] for the phantom images and [Formula: see text] for the in vivo data.RESULTSThe images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is [Formula: see text] for the phantom images and [Formula: see text] for the in vivo data.The result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest.CONCLUSIONThe result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest. PurposeWe investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions.MethodsWe propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data.ResultsThe images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is 4.23±1.52 for the phantom images and 6.09±0.72 for the in vivo data.ConclusionThe result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest. We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions. We propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data. The images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is [Formula: see text] for the phantom images and [Formula: see text] for the in vivo data. The result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest. Purpose We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions. Methods We propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data. Results The images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is 4.23 ± 1.52 for the phantom images and 6.09 ± 0.72 for the in vivo data. Conclusion The result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest. |
| Author | Günther, Matthias Rothlübbers, Sven Eickel, Klaus Strohm, Hannah |
| Author_xml | – sequence: 1 givenname: Hannah orcidid: 0000-0001-8355-484X surname: Strohm fullname: Strohm, Hannah email: hannah.strohm@mevis.fraunhofer.de organization: Fraunhofer Institute for Digital Medicine MEVIS – sequence: 2 givenname: Sven surname: Rothlübbers fullname: Rothlübbers, Sven organization: Fraunhofer Institute for Digital Medicine MEVIS – sequence: 3 givenname: Klaus surname: Eickel fullname: Eickel, Klaus organization: University of Bremen – sequence: 4 givenname: Matthias surname: Günther fullname: Günther, Matthias organization: Fraunhofer Institute for Digital Medicine MEVIS, University of Bremen |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32495155$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kU1v3CAQhlGVKl_tH8ghQuqlF7cMHgy-RKrSTylVL80ZsXi8ceSFDdhd9d-X7SabNoccEEg878w7856wgxADMXYG4h0Iod9nAIWmElKUA62uNi_YMZgGqgZle7B_gzhiJznfCoFK1-qQHdUSWwVKHbPvH4nWfCSXwhCW1cJl6ngiH0Oe0uynIQYeez6PU3I5zqHjw8otKfM-xRVPbsP9jQuBRt65yb1iL3s3Znp9f5-y68-ffl5-ra5-fPl2-eGq8qhxqjptTI291qIhBX3XNK1xUqoGSRqoPZLvoFMIiCgbv_AIREY4ckYthHT1KbvY1V3PixV1nkKxN9p1KubSbxvdYP__CcONXcZfViO0aHQp8Pa-QIp3M-XJrobsaRxdoDhnK1G0ZYm12KJvnqC3cU6hjFeoWppWqRYKdf6vo72Vh00XQO4An2LOifo9AsJu47S7OG2J0_6N026KyDwR-WFy21DKVMP4vLTeSXPpE5aUHm0_o_oDYYW1CQ |
| CitedBy_id | crossref_primary_10_1109_TUFFC_2021_3064303 crossref_primary_10_1007_s00371_021_02350_9 crossref_primary_10_1016_j_artmed_2023_102664 crossref_primary_10_1109_TUFFC_2024_3462299 crossref_primary_10_3390_s23218760 crossref_primary_10_1088_1361_6560_ad997f crossref_primary_10_3390_app12083754 crossref_primary_10_1109_TMI_2021_3087450 crossref_primary_10_1016_j_ultras_2023_107096 crossref_primary_10_3389_fphy_2024_1398393 crossref_primary_10_1016_j_compmedimag_2022_102073 |
| Cites_doi | 10.1109/TUFFC.2017.2736890 10.1109/CISS.2019.8692835 10.1109/ICASSP.2018.8461575 10.1109/ULTSYM.2016.7728908 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2020 The Author(s) 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2020 – notice: The Author(s) 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM K9. 7X8 5PM |
| DOI | 10.1007/s11548-020-02197-w |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic ProQuest Health & Medical Complete (Alumni) MEDLINE |
| 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 | fulltext_linktorsrc |
| Discipline | Medicine Computer Science |
| EISSN | 1861-6429 |
| EndPage | 1490 |
| ExternalDocumentID | PMC7419487 32495155 10_1007_s11548_020_02197_w |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Fraunhofer-Gesellschaft grantid: 600 725 funderid: http://dx.doi.org/10.13039/501100003185 – fundername: Fraunhofer-Gesellschaft grantid: 600 725 – fundername: ; grantid: 600 725 |
| GroupedDBID | --- -5E -5G -BR -EM -Y2 -~C .86 .VR 06C 06D 0R~ 0VY 1N0 203 29J 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5GY 5VS 67Z 6NX 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABOCM ABPLI ABQBU ABQSL ABSXP ABTEG ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACDTI ACGFS ACHSB ACHXU ACKNC ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADJJI ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETCA AETLH AEVLU AEXYK AFBBN AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKMHD ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR ARMRJ ASPBG AVWKF AVXWI AXYYD AZFZN B-. BA0 BDATZ BGNMA BSONS C6C CAG COF CS3 CSCUP DNIVK DPUIP EBD EBLON EBS EIOEI EJD EMOBN EN4 ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC G-Y G-Z GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HF~ HG5 HG6 HLICF HMJXF HQYDN HRMNR HVGLF HZ~ IHE IJ- IKXTQ IMOTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KPH LLZTM M4Y MA- N2Q N9A NPVJJ NQJWS NU0 O9- O93 O9I O9J OAM P2P P9S PF0 PT4 QOR QOS R89 R9I RNS ROL RPX RSV S16 S1Z S27 S37 S3B SAP SDH SHX SISQX SJYHP SMD SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZ9 SZN T13 TSG TSK TSV TT1 TUC U2A U9L UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WJK WK8 YLTOR Z45 Z7R Z7V Z7X Z82 Z83 Z87 Z88 ZMTXR ZOVNA ~A9 7X7 88E 8FI 8FJ AAYXX ABBRH ABDBE ABFSG ABRTQ ABUWG ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFKRA AFOHR AGQPQ AHPBZ AHWEU AIXLP ARAPS ATHPR AYFIA BENPR BGLVJ CCPQU CITATION FYUFA HCIFZ HMCUK M1P PHGZM PHGZT PJZUB PPXIY PQGLB PSQYO UKHRP CGR CUY CVF ECM EIF NPM K9. 7X8 5PM |
| ID | FETCH-LOGICAL-c474t-d78834f7706e51fd6698a22564e2813c4ecd1d54144426cbc41ee80aea85b02a3 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000537649700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1861-6410 1861-6429 |
| IngestDate | Thu Aug 21 18:45:25 EDT 2025 Thu Oct 02 09:53:48 EDT 2025 Tue Oct 07 07:16:48 EDT 2025 Thu Apr 03 06:50:19 EDT 2025 Tue Nov 18 22:23:27 EST 2025 Sat Nov 29 01:30:17 EST 2025 Fri Feb 21 02:42:14 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Keywords | Deep learning Plane wave ultrasound imaging |
| Language | English |
| License | Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c474t-d78834f7706e51fd6698a22564e2813c4ecd1d54144426cbc41ee80aea85b02a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-8355-484X |
| OpenAccessLink | https://link.springer.com/10.1007/s11548-020-02197-w |
| PMID | 32495155 |
| PQID | 2432895591 |
| PQPubID | 2043910 |
| PageCount | 4 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7419487 proquest_miscellaneous_2409642307 proquest_journals_2432895591 pubmed_primary_32495155 crossref_primary_10_1007_s11548_020_02197_w crossref_citationtrail_10_1007_s11548_020_02197_w springer_journals_10_1007_s11548_020_02197_w |
| PublicationCentury | 2000 |
| PublicationDate | 2020-09-01 |
| PublicationDateYYYYMMDD | 2020-09-01 |
| PublicationDate_xml | – month: 09 year: 2020 text: 2020-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Cham |
| PublicationPlace_xml | – name: Cham – name: Germany – name: Heidelberg |
| PublicationSubtitle | A journal for interdisciplinary research, development and applications of image guided diagnosis and therapy |
| PublicationTitle | International journal for computer assisted radiology and surgery |
| PublicationTitleAbbrev | Int J CARS |
| PublicationTitleAlternate | Int J Comput Assist Radiol Surg |
| PublicationYear | 2020 |
| Publisher | Springer International Publishing Springer Nature B.V |
| Publisher_xml | – name: Springer International Publishing – name: Springer Nature B.V |
| References | Liebgott H, Rodriguez-Molares A, Cervenansky F, Jensen JA, Bernard O (2016) Plane-wave imaging challenge in medical ultrasound. In: 2016 IEEE International ultrasonics symposium (IUS), pp 1–4. https://doi.org/10.1109/ULTSYM.2016.7728908 GasseMMilliozFRouxEGarciaDLiebgottHFribouletDHigh-quality plane wave compounding using convolutional neural networksIEEE Trans Ultrasonics Ferroelectr Freq Control201764101637163910.1109/TUFFC.2017.2736890 Nair AA, Tran TD, Reiter A, Bell MAL (2019) A generative adversarial neural network for beamforming ultrasound images: invited presentation. In: 2019 53rd Annual conference on information sciences and systems (CISS), pp 1–6. https://doi.org/10.1109/CISS.2019.8692835 Simson W, Göbl R, Paschali M, Krönke M, Scheidhauer K, Weber W, Navab N (2019) End-to-end learning-based ultrasound reconstruction. arXiv e-prints arXiv:1904.04696 Falkner S, Klein A, Hutter F (2018) BOHB: robust and efficient hyperparameter optimization at scale. In: Proceedings of the 35th international conference on machine learning, proceedings of machine learning research, vol 80, pp 1437–1446 Nair AA, Tran TD, Reiter A, Bell MAL (2018) A deep learning based alternative to beamforming ultrasound images. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 3359–3363 2197_CR4 M Gasse (2197_CR2) 2017; 64 2197_CR3 2197_CR1 2197_CR6 2197_CR5 |
| References_xml | – reference: GasseMMilliozFRouxEGarciaDLiebgottHFribouletDHigh-quality plane wave compounding using convolutional neural networksIEEE Trans Ultrasonics Ferroelectr Freq Control201764101637163910.1109/TUFFC.2017.2736890 – reference: Falkner S, Klein A, Hutter F (2018) BOHB: robust and efficient hyperparameter optimization at scale. In: Proceedings of the 35th international conference on machine learning, proceedings of machine learning research, vol 80, pp 1437–1446 – reference: Liebgott H, Rodriguez-Molares A, Cervenansky F, Jensen JA, Bernard O (2016) Plane-wave imaging challenge in medical ultrasound. In: 2016 IEEE International ultrasonics symposium (IUS), pp 1–4. https://doi.org/10.1109/ULTSYM.2016.7728908 – reference: Nair AA, Tran TD, Reiter A, Bell MAL (2018) A deep learning based alternative to beamforming ultrasound images. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 3359–3363 – reference: Simson W, Göbl R, Paschali M, Krönke M, Scheidhauer K, Weber W, Navab N (2019) End-to-end learning-based ultrasound reconstruction. arXiv e-prints arXiv:1904.04696, – reference: Nair AA, Tran TD, Reiter A, Bell MAL (2019) A generative adversarial neural network for beamforming ultrasound images: invited presentation. In: 2019 53rd Annual conference on information sciences and systems (CISS), pp 1–6. https://doi.org/10.1109/CISS.2019.8692835 – volume: 64 start-page: 1637 issue: 10 year: 2017 ident: 2197_CR2 publication-title: IEEE Trans Ultrasonics Ferroelectr Freq Control doi: 10.1109/TUFFC.2017.2736890 – ident: 2197_CR4 doi: 10.1109/CISS.2019.8692835 – ident: 2197_CR5 – ident: 2197_CR1 – ident: 2197_CR3 doi: 10.1109/ICASSP.2018.8461575 – ident: 2197_CR6 doi: 10.1109/ULTSYM.2016.7728908 |
| SSID | ssj0045735 |
| Score | 2.2874196 |
| Snippet | Purpose
We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw... We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we... PurposeWe investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw... |
| SourceID | pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1487 |
| SubjectTerms | Algorithms Computer Imaging Computer Science Contrast Media Deep Learning Diagnosis, Computer-Assisted - methods Feasibility Ground truth Health Informatics Humans Image Processing, Computer-Assisted - methods Image quality Image reconstruction Imaging In vivo methods and tests Information retrieval Machine learning Medicine Medicine & Public Health Models, Theoretical Optimization Pattern Recognition and Graphics Phantoms, Imaging Plane waves Radiology Reference Values Reproducibility of Results Short Communication Signal-To-Noise Ratio Surgery Target recognition Ultrasonic imaging Ultrasonic testing Ultrasonography Ultrasound Vision |
| Title | Deep learning-based reconstruction of ultrasound images from raw channel data |
| URI | https://link.springer.com/article/10.1007/s11548-020-02197-w https://www.ncbi.nlm.nih.gov/pubmed/32495155 https://www.proquest.com/docview/2432895591 https://www.proquest.com/docview/2409642307 https://pubmed.ncbi.nlm.nih.gov/PMC7419487 |
| Volume | 15 |
| WOSCitedRecordID | wos000537649700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1861-6429 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045735 issn: 1861-6410 databaseCode: RSV dateStart: 20060301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB7RghAXCuUVWiojcQNLcey14yOCVhxohXhUe4sS24GVlmzVtOzfZ8brZLW0RYJznMSPGc98mscH8Cq0Hh15LznaXokApVW8qZuct41qFV7KrY3ti08_mpOTcjq1n1JRWD9kuw8hyXhTr4vdyLvmBHfQLlnDl1twG81dSYQNn7-cDvevmphIqylKLbhWIk-lMtd_Y9McXfExr6ZK_hEvjWboaOf_FvAA7ie3k71dyclDuBW6XdgZKB1Y0vBduHucYu2P4Ph9CGcssUp852TuPIv4eew5yxYtu5zjjHsiZ2Kzn3g59YwqVth5vWRUVNyFOaMs1Mfw7ejw67sPPJEvcKeMuuAesbFUrTG5DhPReq1tWaPyaxWKUkingvPCE4m4QiPvGqdECGVeh7qcNHlRyyew3S268AzYpLZ5o3NvEV4q650tjA5SakdRz0LKDMRwBpVLncmJIGNerXsq09ZVuHVV3LpqmcHr8Z2zVV-Ov47eH462SjraV4WSiDYRUYkMXo6PUbsoZFJ3YXFJYxDiKUqWz-DpShLG30mi7UZ3LAOzISPjAOrcvfmkm_2IHbzRjbOIFDN4M0jKelo3r-L5vw3fg3tFFDZKiNuHbZSN8ALuuF8Xs_78ALbMtDyImvMb87ESCg |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwED_BQLAXBmOwwAAj8QaW4th14kcETEO0FYIx7S1KbAcqlXRaNvrvc-c6qcoACZ5zSfxxXz_dF8AL3zh05J3kaHslApRG8bqqU97UqlGolBsT2hefjPPptDg9NR9jUVjXZ7v3IcmgqdfFbuRdc4I7aJdMzpfX4YZCi0Ud8z99Pun1rxrlYaymKLTgWok0lsr8_hub5uiKj3k1VfKXeGkwQ4c7_7eBu3Anup3s9YpP7sE13-7CTj_SgUUJ34Vbkxhrvw-Tt96fsThV4isnc-dYwM9Dz1m2aNjlHFfc0XAmNvuOyqljVLHCzqslo6Li1s8ZZaHuwZfDd8dvjngcvsCtytUFd4iNpWryPNV-JBqntSkqFH6tfFYIaZW3TjgaIq7QyNvaKuF9kVa-KkZ1mlXyAWy1i9bvAxtVJq116gzCS2WcNVmuvZTaUtQzkzIB0d9BaWNnchqQMS_XPZXp6Eo8ujIcXblM4OXwztmqL8dfqQ_6qy2jjHZlpiSiTURUIoHnw2OULgqZVK1fXBINQjxFyfIJPFxxwvA7SWO70R1LIN_gkYGAOndvPmln30IHb3TjDCLFBF71nLJe1p938ejfyJ_B7aPjybgcv59-eAzbWWA8So47gC3kE_8EbtofF7Pu_GmQn59eDBQG |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bT9RAFD5BMIQXQbxQAR0T33RC25mddh6JuIEIGxKV8Na0c8FN1u6GXdy_7zmzbdcVNTE-97Sdy7l9OTeAN85bdOSt4Gh7BQIUL3lVVjH3lfQSlbLXoX3x1Xk2GOTX1_rypyr-kO3ehiQXNQ3UpameHU2sP1oWvpGnzQn6oI3SGZ8_gA1JifSE1z9dtbpY9rIwYjPJVcIVEjRlM7__xqppuudv3k-b_CV2GkxSf_v_N7MDjxp3lB0v-OcxrLl6F7bbUQ-skfxd2LxoYvBP4OLEuQlrpk3ccDKDlgVc3fWiZWPP7ka4-ikNbWLDb6i0powqWdhtOWdUbFy7EaPs1Kfwpf_h8_tT3gxl4EZmcsYtYmYhfZbFyvUSb5XSeYlKQUmX5okw0hmbWBouLtH4m8rIxLk8Ll2Z96o4LcUzWK_HtdsD1it1XKnYaoSdUluj00w5IZShaGgqRARJex-FaTqW0-CMUbHstUxHV-DRFeHoinkEb7t3Jot-HX-lPmivuWhkd1qkUiAKRaSVRPC6e4xSR6GUsnbjO6JB6CcpiT6C5wuu6H4naJw3umkRZCv80hFQR-_VJ_Xwa-jsje6dRgQZwbuWa5bL-vMuXvwb-SvYvDzpF-dng4_7sJUGvqOcuQNYRzZxh_DQfJ8Np7cvgyj9ABwdHOo |
| 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=Deep+learning-based+reconstruction+of+ultrasound+images+from+raw+channel+data&rft.jtitle=International+journal+for+computer+assisted+radiology+and+surgery&rft.au=Strohm%2C+Hannah&rft.au=Rothl%C3%BCbbers%2C+Sven&rft.au=Eickel%2C+Klaus&rft.au=G%C3%BCnther%2C+Matthias&rft.date=2020-09-01&rft.pub=Springer+International+Publishing&rft.issn=1861-6410&rft.eissn=1861-6429&rft.volume=15&rft.issue=9&rft.spage=1487&rft.epage=1490&rft_id=info:doi/10.1007%2Fs11548-020-02197-w&rft_id=info%3Apmid%2F32495155&rft.externalDocID=PMC7419487 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1861-6410&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1861-6410&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1861-6410&client=summon |