| dc.contributor.author | Nunes, Rita G. | |
| dc.contributor.author | Nogueira, Luisa | |
| dc.contributor.author | Gaspar, Andreia S. | |
| dc.contributor.author | Adubeiro, Nuno | |
| dc.contributor.author | Brandão, Sofia | |
| dc.date.accessioned | 2022-05-18T16:05:52Z | |
| dc.date.available | 2022-05-18T16:05:52Z | |
| dc.date.issued | 2019-05 | |
| dc.description.abstract | This manuscript provides an overview of recent developments in Diffusion-Weighted Imaging (DWI) outside the brain, focusing on liver, breast, prostate, muskuloskeletal (MSK) and cardiac applications. A general introduction to cross-cutting acquisition and image processing challenges is first provided. These often include short T2 relaxation times, the need to image a large field-of-view with the resulting complications in shimming the B0 field and achieving good fat suppression. Some of the strategies developed for dealing with motion, namely cardiac and respiratory motion are described. Specific sections are then presented for each of the aforementioned organs. A motivation for the clinical applicability of DWI is first provided, followed by specific image acquisition and processing considerations. Quantitative imaging is becoming standard in clinical practice, and the Apparent Diffusion Coefficient is routinely estimated in the liver, breast and prostate. Application of alternative signal models in these organs is being explored, including both the Intravoxel Incoherent Motion and Diffusion Kurtosis models. Ongoing efforts are focused on evaluating the potential clinical added value of the extra parameters and on improving their repeatability. MSK and cardiac DWI have shown potential for assessing pathological changes in fiber architecture, but further validation is required to enable application in the clinical setting. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Nunes, R.G., Nogueira, L., Gaspar, A.S., Adubeiro, N., Brandão, S. (2019). Diffusion MRI Outside the Brain. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-05831-9_19 | pt_PT |
| dc.identifier.doi | 10.1007/978-3-030-05831-9_19 | pt_PT |
| dc.identifier.isbn | 978-3-030-05831-9 | |
| dc.identifier.isbn | 978-3-030-05830-2 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/20540 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.subject | Diffusion-weighted MRI | pt_PT |
| dc.subject | Liver | pt_PT |
| dc.subject | Breast | pt_PT |
| dc.subject | Prostate | pt_PT |
| dc.subject | Muskuloskeletal system | pt_PT |
| dc.subject | Cardiac | pt_PT |
| dc.subject | Applications | pt_PT |
| dc.title | Diffusion MRI outside the brain | pt_PT |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 249 | pt_PT |
| oaire.citation.startPage | 227 | pt_PT |
| oaire.citation.title | Computational Diffusion MRI, MICCAI 2019 | pt_PT |
| person.familyName | Nogueira | |
| person.familyName | Adubeiro | |
| person.givenName | Luisa | |
| person.givenName | Nuno | |
| person.identifier.ciencia-id | 0119-2885-C797 | |
| person.identifier.orcid | 0000-0002-1291-9490 | |
| person.identifier.orcid | 0000-0001-8657-3288 | |
| person.identifier.rid | AAC-1663-2021 | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | bookPart | pt_PT |
| relation.isAuthorOfPublication | c2991b07-7696-4d2b-ad5d-863790e33130 | |
| relation.isAuthorOfPublication | 35d1aa21-3c2a-4af6-8346-efa61cd513fc | |
| relation.isAuthorOfPublication.latestForDiscovery | c2991b07-7696-4d2b-ad5d-863790e33130 |
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