{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:15:48Z","timestamp":1760145348492,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Operational Program Competitiveness, Entrepreneurship and Innovation","award":["TAEDK-06189","2018SE01300001"],"award-info":[{"award-number":["TAEDK-06189","2018SE01300001"]}]},{"name":"Precision Medicine and Climate Change National Research Networks Infrastructures","award":["TAEDK-06189","2018SE01300001"],"award-info":[{"award-number":["TAEDK-06189","2018SE01300001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for various iteration numbers, using small-animal dynamic PET data obtained from a Mediso nanoScan\u00ae PET\/CT scanner. For this purpose, Patlak graphical kinetic analysis was employed to noninvasively quantify the myocardial metabolic rate of glucose (MRGlu) in seven male C57BL\/6 mice (n=7). All analytic reconstructions were performed via software for tomographic image reconstruction. The analysis of all PET-reconstructed images was conducted with PMOD software (version 3.506, PMOD Technologies LLC, F\u00e4llanden, Switzerland) using the inferior vena cava as the image-derived input function. Statistical significance was determined by employing the one-way analysis of variance test. The results revealed that the differences between the values of MRGlu obtained via SRT versus FBP, and the variants of he Tera-Tomo 3D algorithm were not statistically significant (p &gt; 0.05). Overall, the SRT appears to perform similarly to the other algorithms investigated, providing a valid alternative analytic method for preclinical dynamic PET studies.<\/jats:p>","DOI":"10.3390\/jimaging10070170","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T08:48:29Z","timestamp":1721206109000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noninvasive Quantification of Glucose Metabolism in Mice Myocardium Using the Spline Reconstruction Technique"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8376-5492","authenticated-orcid":false,"given":"Alexandros","family":"Vrachliotis","sequence":"first","affiliation":[{"name":"Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece"},{"name":"Clinical, Experimental Surgery & Translational Research, Biomedical Research Foundation (BRFAA), Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5490-3219","authenticated-orcid":false,"given":"Anastasios","family":"Gaitanis","sequence":"additional","affiliation":[{"name":"Clinical, Experimental Surgery & Translational Research, Biomedical Research Foundation (BRFAA), Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7701-5644","authenticated-orcid":false,"given":"Nicholas E.","family":"Protonotarios","sequence":"additional","affiliation":[{"name":"Mathematics Research Center, Academy of Athens, 11527 Athens, Greece"},{"name":"Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, National Center for Scientific Research \u201cDemokritos\u201d, 15341 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1283-0883","authenticated-orcid":false,"given":"George A.","family":"Kastis","sequence":"additional","affiliation":[{"name":"Mathematics Research Center, Academy of Athens, 11527 Athens, Greece"},{"name":"Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, National Center for Scientific Research \u201cDemokritos\u201d, 15341 Athens, Greece"}]},{"given":"Lena","family":"Costaridou","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s13014-020-01519-1","article-title":"Recent advances of PET imaging in clinical radiation oncology","volume":"15","author":"Unterrainer","year":"2020","journal-title":"Radiat. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1038\/jcbfm.2012.20","article-title":"The development, past achievements, and future directions of brain PET","volume":"32","author":"Jones","year":"2012","journal-title":"J. Cereb. Blood. Flow Metab."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1016\/j.jcmg.2017.07.008","article-title":"MR\/PET imaging of the cardiovascular system","volume":"10","author":"Robson","year":"2017","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41398-020-0768-z","article-title":"PET imaging shows no changes in TSPO brain density after IFN-\u03b1 immune challenge in healthy human volunteers","volume":"10","author":"Nettis","year":"2020","journal-title":"Transl. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"E118","DOI":"10.1148\/radiol.2020200770","article-title":"FDG PET\/CT of COVID-19","volume":"296","author":"Zou","year":"2020","journal-title":"Radiology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"157","DOI":"10.2967\/jnmt.111.098632","article-title":"Small-animal PET: What is it, and why do we need it?","volume":"40","author":"Yao","year":"2012","journal-title":"J. Nucl. Med. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1177\/00912700122010357","article-title":"Fundamentals of positron emission tomography and applications in preclinical drug development","volume":"41","author":"Cherry","year":"2001","journal-title":"J. Clin. Pharmacol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s00259-020-04843-6","article-title":"Kinetic modeling and parametric imaging with dynamic PET for oncological applications: General considerations, current clinical applications, and future perspectives","volume":"48","author":"Pan","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.2967\/jnumed.112.110114","article-title":"Repeatable noninvasive measurement of mouse myocardial glucose uptake with 18F-FDG: Evaluation of tracer kinetics in a type 1 diabetes model","volume":"54","author":"Thorn","year":"2013","journal-title":"J. Nucl. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"567","DOI":"10.2967\/jnumed.109.065938","article-title":"18F-FDG PET imaging of myocardial viability in an experienced center with access to 18F-FDG and integration with clinical management teams: The Ottawa-FIVE substudy of the PARR 2 trial","volume":"51","author":"Abraham","year":"2010","journal-title":"J. Nucl. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1038\/jcbfm.2015.104","article-title":"Noninvasive quantification of cerebral metabolic rate for glucose in rats using 18F-FDG PET and standard input function","volume":"35","author":"Hori","year":"2015","journal-title":"J. Cereb. Blood. Flow Metab."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"117961","DOI":"10.1016\/j.neuroimage.2021.117961","article-title":"Estimation of the net influx rate Ki and the cerebral metabolic rate of glucose MRglc using a single static [18F] FDG PET scan in rats","volume":"233","author":"Bertoglio","year":"2021","journal-title":"Neuroimage"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.2967\/jnumed.113.127381","article-title":"Image-derived input function from the vena cava for 18F-FDG PET studies in rats and mice","volume":"55","author":"Lanz","year":"2014","journal-title":"J. Nucl. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.2967\/jnumed.115.160820","article-title":"Impact of image-derived input function and fit time intervals on patlak quantification of myocardial glucose uptake in mice","volume":"56","author":"Thackeray","year":"2015","journal-title":"J. Nucl. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"132","DOI":"10.2967\/jnumed.112.107474","article-title":"Quantification of brain glucose metabolism by 18F-FDG PET with real-time arterial and image-derived input function in mice","volume":"54","author":"Alf","year":"2013","journal-title":"J. Nucl. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1007\/s11307-010-0449-z","article-title":"Extraction of input function from rat [18F] FDG PET images","volume":"13","author":"Kudomi","year":"2011","journal-title":"Mol. Imaging Biol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1109\/23.34585","article-title":"Analytic 3D image reconstruction using all detected events","volume":"36","author":"Kinahan","year":"1989","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TMI.1982.4307558","article-title":"Maximum likelihood reconstruction for emission tomography","volume":"1","author":"Shepp","year":"1982","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/42.363108","article-title":"Accelerated image reconstruction using ordered subsets of projection data","volume":"13","author":"Hudson","year":"1994","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"529","DOI":"10.2217\/iim.10.49","article-title":"Image reconstruction for PET\/CT scanners: Past achievements and future challenges","volume":"2","author":"Tong","year":"2010","journal-title":"Imaging Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, B., and Liu, H. (2020). FBP-Net for direct reconstruction of dynamic PET images. Phys. Med. Biol., 65.","DOI":"10.1088\/1361-6560\/abc09d"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.compmedimag.2009.07.006","article-title":"PET image reconstruction: A stopping rule for the MLEM algorithm based on properties of the updating coefficients","volume":"34","author":"Gaitanis","year":"2010","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.cmpb.2009.11.011","article-title":"Studying the properties of the updating coefficients in the OSEM algorithm for iterative image reconstruction in PET","volume":"99","author":"Gaitanis","year":"2010","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, X., Wedekind, F., Kroll, T., Oskamp, A., Beer, S., Drzezga, A., Ermert, J., Neumaier, B., Bauer, A., and Elmenhorst, D. (2020). Image-derived input functions for quantification of A1 adenosine receptors availability in mice brains using PET and [18F] CPFPX. Front. Physiol., 10.","DOI":"10.3389\/fphys.2019.01617"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1109\/TMI.1986.4307775","article-title":"On the determination of functions from their integral values along certain manifolds","volume":"5","author":"Radon","year":"1986","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Scherzer, O. (2015). Mathematical Methods in PET and SPECT Imaging. Handbook of Mathematical Methods in Imaging, Springer.","DOI":"10.1007\/978-1-4939-0790-8"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20180509","DOI":"10.1098\/rsif.2018.0509","article-title":"The attenuated spline reconstruction technique for single photon emission computed tomography","volume":"15","author":"Protonotarios","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"042501","DOI":"10.1118\/1.4867862","article-title":"Evaluation of the spline reconstruction technique for PET","volume":"41","author":"Kastis","year":"2014","journal-title":"Med. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5970","DOI":"10.1118\/1.4931409","article-title":"The SRT reconstruction algorithm for semiquantification in PET imaging","volume":"42","author":"Kastis","year":"2015","journal-title":"Med. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Vrachliotis, A., Kastis, G.A., Protonotarios, N.E., Fokas, A.S., Nekolla, S.G., Anagnostopoulos, C.D., Costaridou, L., and Gaitanis, A. (2022). Evaluation of the spline reconstruction technique for preclinical PET imaging. Comput. Methods Programs Biomed., 217.","DOI":"10.1016\/j.cmpb.2022.106668"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1007\/s11307-016-1035-9","article-title":"Investigation of image reconstruction parameters of the Mediso nanoScan PC small-animal PET\/CT scanner for two different positron emitters under NEMA NU 4-2008 standards","volume":"19","author":"Gaitanis","year":"2017","journal-title":"Mol. Imaging Biol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/42.563660","article-title":"Exact and approximate rebinning algorithms for 3-D PET data","volume":"16","author":"Defrise","year":"1997","journal-title":"IEEE Trans.Med. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Thielemans, K., Tsoumpas, C., Mustafovic, S., Beisel, T., Aguiar, P., Dikaios, N., and Jacobson, M.W. (2012). STIR: Software for tomographic image reconstruction release 2. Phys.Med. Biol., 57.","DOI":"10.1088\/0031-9155\/57\/4\/867"},{"key":"ref_34","unstructured":"Magdics, M., Szirmay-Kalos, L., Szlavecz, \u00c1., Hesz, G., Beny\u00f3, B., Cserkaszky, \u00c1., Lantos, J., L\u00e9gr\u00e1dy, D., Czifrus, S., and Wirth, A. (2010, January 8\u201311). TeraTomo project: A fully 3D GPU based reconstruction code for exploiting the imaging capability of the NanoPET\/CT system. Proceedings of the World Molecular Imaging Congress, Kyoto, Japan."},{"key":"ref_35","unstructured":"PMOD Technologies LLC, F\u00e4llanden, Switzerland (2024, May 15). PMOD\u2014Biomedical Image Quantification, version 3.506; 2013. Available online: https:\/\/www.pmod.com\/web\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1038\/jcbfm.1985.87","article-title":"Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations","volume":"5","author":"Patlak","year":"1985","journal-title":"J. Cereb. Blood Flow. Metab."},{"key":"ref_37","unstructured":"MedCalc Software BVBA, Ostend, Belgium (2024, April 10). MedCalc Statistical Software, version 18.9.1; 2018. Available online: http:\/\/www.medcalc.org."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lajtos, I., Czernin, J., Dahlbom, M., Daver, F., Emri, M., Farshchi-Heydari, S., Forgacs, A., Hoh, C.K., Joszai, I., and Krizsan, A.K. (2014). Cold wall effect eliminating method to determine the contrast recovery coefficient for small animal PET scanners using the NEMA NU-4 image quality phantom. Phys. Med. Biol., 59.","DOI":"10.1088\/0031-9155\/59\/11\/2727"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Protonotarios, N.E., Fokas, A.S., Vrachliotis, A., Marinakis, V., Dikaios, N., and Kastis, G.A. (2022). Reconstruction of preclinical PET images via Chebyshev polynomial approximation of the sinogram. Appl. Sci., 12.","DOI":"10.3390\/app12073335"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40658-020-0282-7","article-title":"EANM Dosimetry Committee series on standard operational procedures for internal dosimetry for 131I mIBG treatment of neuroendocrine tumours","volume":"7","author":"Gear","year":"2020","journal-title":"EJNMMI Phys."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"800","DOI":"10.2967\/jnumed.110.085092","article-title":"Effects of administration route, dietary condition, and blood glucose level on kinetics and uptake of 18F-FDG in mice","volume":"52","author":"Wong","year":"2011","journal-title":"J. Nuc. Med."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/7\/170\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:17:51Z","timestamp":1760109471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/7\/170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,16]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["jimaging10070170"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10070170","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2024,7,16]]}}}