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Conventionally, robust polarimetric parameters are estimated after averaging multiple measurements of intensity for each pair of probing and detected polarised light. Long multi-shot averaging, however, is not compatible with real-time in vivo imaging, and the current performance of polarimetric data processing hinders the translation to clinical practice.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>A learning-based denoising framework is tailored for fast, single-shot, noisy acquisitions of polarimetric intensities. Also, performance-optimised image processing tools are devised for the derivation of clinically relevant parameters. The combination recovers accurate polarimetric parameters from fast acquisitions with near-real-time performance, under the assumption of pseudo-Gaussian polarimetric acquisition noise.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The denoising framework is trained, validated, and tested on experimental data comprising tumour-free and diseased human brain samples in different conditions. Accuracy and image quality indices showed significant (<jats:inline-formula>\n                <jats:alternatives>\n                  <jats:tex-math>$$p &lt; 0.05$$<\/jats:tex-math>\n                  <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>p<\/mml:mi>\n                      <mml:mo>&lt;<\/mml:mo>\n                      <mml:mn>0.05<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math>\n                <\/jats:alternatives>\n              <\/jats:inline-formula>) improvements on testing data for a fast single-pass denoising versus the state-of-the-art and high polarimetric image quality standards. The computational time is reported for the end-to-end processing.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion:<\/jats:title>\n            <jats:p>The end-to-end image processing achieved real-time performance for a localised field of view (<jats:inline-formula>\n                <jats:alternatives>\n                  <jats:tex-math>$$\\approx 6.5\\ \\text {mm}^2$$<\/jats:tex-math>\n                  <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>\u2248<\/mml:mo>\n                      <mml:mn>6.5<\/mml:mn>\n                      <mml:mspace\/>\n                      <mml:msup>\n                        <mml:mtext>mm<\/mml:mtext>\n                        <mml:mn>2<\/mml:mn>\n                      <\/mml:msup>\n                    <\/mml:mrow>\n                  <\/mml:math>\n                <\/jats:alternatives>\n              <\/jats:inline-formula>). The denoised polarimetric intensities produced visibly clear directional patterns of neuronal fibre tracts in line with reference polarimetric image quality standards; directional disruption was kept in case of neoplastic lesions. The presented advances pave the way towards feasible oncological neurosurgical translations of novel, label-free, interventional feedback.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-024-03090-6","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T18:01:55Z","timestamp":1710871315000},"page":"1033-1043","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Near-real-time Mueller polarimetric image processing for neurosurgical intervention"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5705-7839","authenticated-orcid":false,"given":"Stefano","family":"Moriconi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-9519","authenticated-orcid":false,"given":"Omar","family":"Rodr\u00edguez-N\u00fa\u00f1ez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0911-2792","authenticated-orcid":false,"given":"\u00c9l\u00e9a","family":"Gros","sequence":"additional","affiliation":[]},{"given":"Leonard A.","family":"Felger","sequence":"additional","affiliation":[]},{"given":"Theoni","family":"Maragkou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9128-0364","authenticated-orcid":false,"given":"Ekkehard","family":"Hewer","sequence":"additional","affiliation":[]},{"given":"Angelo","family":"Pierangelo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9048-9158","authenticated-orcid":false,"given":"Tatiana","family":"Novikova","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Schucht","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8250-6117","authenticated-orcid":false,"given":"Richard","family":"McKinley","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,19]]},"reference":[{"issue":"8","key":"3090_CR1","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1002\/jbio.201600152","volume":"10","author":"J Qi","year":"2017","unstructured":"Qi J, Elson DS (2017) Mueller polarimetric imaging for surgical and diagnostic applications: a review. 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