{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T20:45:03Z","timestamp":1781901903192,"version":"3.54.5"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T00:00:00Z","timestamp":1719273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001794","name":"University of Queensland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001794","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Cross-modality image estimation can be performed using generative adversarial networks (GANs). To date, SPECT image estimation from another medical imaging modality using this technique has not been considered. We evaluate the estimation of SPECT from MRI and PET, and additionally assess the necessity for cross-modality image registration for GAN training.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We estimated interictal SPECT from PET and MRI as a single-channel input, and as a multi-channel input to the GAN. We collected data from 48 individuals with epilepsy and converted them to 3D isotropic images for consistence across the modalities. Training and testing data were prepared in native and template spaces. The Pix2pix framework within the GAN network was adopted. We evaluated the addition of the structural similarity index metric to the loss function in the GAN implementation. Root-mean-square error, structural similarity index, and peak signal-to-noise ratio were used to assess how well SPECT images were able to be synthesised.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>High quality SPECT images could be synthesised in each case. On average, the use of native space images resulted in a 5.4% percentage improvement in SSIM than the use of images registered to template space. The addition of structural similarity index metric to the GAN loss function did not result in improved synthetic SPECT images. Using PET in either the single channel or dual channel implementation led to the best results, however MRI could produce SPECT images close in quality.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Synthesis of SPECT from MRI or PET can potentially reduce the number of scans needed for epilepsy patient evaluation and reduce patient exposure to radiation.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2024.1383773","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T10:40:25Z","timestamp":1719398425000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Image synthesis of interictal SPECT from MRI and PET using machine learning"],"prefix":"10.3389","volume":"15","author":[{"given":"Azin Shokraei","family":"Fard","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David C.","family":"Reutens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stuart C.","family":"Ramsay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven J.","family":"Goodman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soumen","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viktor","family":"Vegh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1212\/wnl.50.2.445","article-title":"Subtraction ictal SPECT co-registered to MRI improves clinical usefulness of SPECT in localizing the surgical seizure focus","volume":"50","author":"O'Brien","year":"1998","journal-title":"Neurology"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"2358","DOI":"10.1007\/s00259-018-4080-6","article-title":"PISCOM: a new procedure for epilepsy combining ictal SPECT and interictal PET","volume":"45","author":"Perissinotti","year":"2018","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"105556","DOI":"10.1016\/j.compbiomed.2022.105556","article-title":"From CNNs to GANs for cross-modality medical image estimation","volume":"146","author":"Shokraei Fard","year":"2022","journal-title":"Comput Biol Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1002\/mp.12155","article-title":"MR-based synthetic CT generation using a deep convolutional neural network method","volume":"44","author":"Han","year":"2017","journal-title":"Med Phys"},{"key":"ref5","author":"Nie","year":"2016"},{"key":"ref6","author":"Roy","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"11","author":"Goodfellow","year":"2020","journal-title":"Comm of the ACM"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"6977","DOI":"10.1016\/j.jksuci.2022.02.018","article-title":"The effect of loss function on conditional generative adversarial networks","volume":"34","author":"Abu-Srhan","year":"2022","journal-title":"J King Saud University-Computer Info Sci"},{"key":"ref9","author":"Chartsias","year":"2017"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"3565","DOI":"10.1002\/mp.13617","article-title":"MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks","volume":"46","author":"Lei","year":"2019","journal-title":"Med Phys"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"2720","DOI":"10.1109\/TBME.2018.2814538","article-title":"Medical image synthesis with deep convolutional adversarial networks","volume":"65","author":"Nie","year":"2018","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref12","author":"Wolterink","year":"2017"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"101684","DOI":"10.1016\/j.compmedimag.2019.101684","article-title":"MedGAN: medical image translation using GANs","volume":"79","author":"Armanious","year":"2020","journal-title":"Comput Med Imaging Graph"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.engappai.2018.11.013","article-title":"Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection","volume":"78","author":"Ben-Cohen","year":"2019","journal-title":"Eng Appl Artif Intell"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.2967\/jnumed.117.199414","article-title":"Generation of structural MR images from amyloid PET: application to MR-less quantification","volume":"59","author":"Choi","year":"2018","journal-title":"J Nucl Med"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"185001","DOI":"10.1088\/1361-6560\/aada6d","article-title":"Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy","volume":"63","author":"Maspero","year":"2018","journal-title":"Phys Med Biol"},{"key":"ref17","author":"Wei","year":"2018"},{"key":"ref18","author":"Huo","year":"2018"},{"key":"ref19","author":"Pan","year":"2018"},{"key":"ref20","first-page":"210802160","article-title":"MRI to PET cross-modality translation using globally and locally aware GAN (GLA-GAN) for multi-modal diagnosis of Alzheimer's disease","author":"Sikka","year":"2021","journal-title":"arXiv preprint arXiv"},{"key":"ref21","author":"Grabner","year":"2006"},{"key":"ref22","author":"Isola","year":"2017"},{"key":"ref23","author":"Abobakr","year":"2019"},{"key":"ref24","author":"Gatys","year":"2016"},{"key":"ref25","first-page":"170406191","article-title":"Softmax gan","author":"Lin","year":"2017","journal-title":"arXiv preprint arXiv"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1148\/radiol.2018180940","article-title":"Ultra\u2013low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs","volume":"290","author":"Chen","year":"2019","journal-title":"Radiology"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1109\/TRPMS.2020.2986414","article-title":"Micro-networks for robust MR-guided low count PET imaging","volume":"5","author":"da Costa-Luis","year":"2020","journal-title":"IEEE transactions on radiation and plasma medical sciences"},{"key":"ref28","first-page":"1","article-title":"Pairwise feature-based generative adversarial network for incomplete multi-modal Alzheimer\u2019s disease diagnosis","author":"Ye","year":"2022","journal-title":"Vis Comput"},{"key":"ref29","author":"Wei","year":"2018"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1093\/brain\/124.9.1683","article-title":"Presurgical evaluation of epilepsy","volume":"124","author":"Rosenow","year":"2001","journal-title":"Brain"},{"key":"ref31","first-page":"1896","article-title":"Ictal and interictal brain SPECT imaging in epilepsy using technetium-99m-ECD","volume":"35","author":"Gr\u00fcnwald","year":"1994","journal-title":"J Nucl Med"},{"key":"ref32","first-page":"437","article-title":"Practical recommendations for gradient-based training of deep architectures","volume-title":"Neural networks","author":"Bengio","year":"2012"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TMI.2006.891486","article-title":"A review of methods for correction of intensity inhomogeneity in MRI","volume":"26","author":"Vovk","year":"2007","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2024.1383773\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T10:40:33Z","timestamp":1719398433000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2024.1383773\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,25]]},"references-count":33,"alternative-id":["10.3389\/fneur.2024.1383773"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2024.1383773","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,25]]},"article-number":"1383773"}}