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However, a significant cost is associated with manufacturing the densely packed photodetectors required for the extended\u2010coverage systems. Despite improved performance allowing ultralow dose or ultrafast scans, the financial barrier remains, limiting clinical\u00a0utilisation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To mitigate the cost limitations, alternative sparse system configurations with strategically placed inter\u2010detector gaps have been proposed, allowing an extended field\u2010of\u2010view PET design with detector costs similar to a standard PET system, albeit at the expense of image\u00a0quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address the challenges posed by sparse detector configurations, particularly the heavy undersampling of PET measurements, we propose a deep sinogram restoration network to fill in the missing sinogram data. The network, a modified Residual U\u2010Net, is trained end\u2010to\u2010end using standard clinical PET scans from a GE Signa PET\/MR. The training involves simulating the removal of 50% of the detectors in chessboard patterns of varying sizes, leading to incomplete sinograms with significant count losses (thus retaining only 25% of all lines of response).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model successfully recovers missing counts in incomplete sinograms, with a mean absolute error consistently below two events per pixel for typical injected radioactivity, outperforming 2D interpolation of incomplete sinograms based on mean absolute error and structural similarity in both sinogram and reconstructed image domain. Notably, the predicted sinograms exhibit a smoothing effect, leading to reconstructed images lacking sharpness in finer details. Despite these limitations, the model demonstrates a substantial capacity for compensating for the undersampling caused by the sparse detector\u00a0configuration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This proof\u2010of\u2010concept study suggests that sparse detector configurations, combined with deep learning techniques, offer a viable alternative to conventional PET scanner designs. This approach supports the development of cost\u2010effective, total body PET scanners, allowing a significant step forward in medical imaging\u00a0technology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70293","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T17:13:20Z","timestamp":1772990000000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Filling of incomplete sinograms from sparse PET detector configurations using a residual U\u2010Net"],"prefix":"10.1002","volume":"53","author":[{"given":"Klara","family":"Leffler","sequence":"first","affiliation":[{"name":"Department of Mathematics and Mathematical Statistics Ume\u00e5 University Ume\u00e5 Sweden"},{"name":"Department of Mathematical Sciences University of Copenhagen Copenhagen Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi Tommaso","family":"Luppino","sequence":"additional","affiliation":[{"name":"UiT Machine Learning Group, Department of Physics and Technology UiT The Arctic University of Norway Troms\u00f8 Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Kuttner","sequence":"additional","affiliation":[{"name":"UiT Machine Learning Group, Department of Physics and Technology UiT The Arctic University of Norway Troms\u00f8 Norway"},{"name":"The PET Imaging Center University Hospital of North Norway Troms\u00f8 Norway"},{"name":"Nuclear Medicine and Radiation Biology Research Group, Department of Clinical Medicine UiT The Arctic University of Norway Troms\u00f8 Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karin","family":"S\u00f6derkvist","sequence":"additional","affiliation":[{"name":"Department of Diagnostics and Intervention, Oncology Ume\u00e5 University Ume\u00e5 Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Axelsson","sequence":"additional","affiliation":[{"name":"Department of Diagnostics and Intervention, Radiation Physics Ume\u00e5 University Ume\u00e5 Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.120.250597"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.121.261972"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.119.231845"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.119.229997"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/acc722"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40658\u2010020\u201000290\u20102"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259\u2010021\u201005293\u20104"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/trpms.2020.3027257"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ac6aa1"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11307\u2010018\u20101250\u20107"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14046"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20220357"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259\u2010022\u201005981\u20109"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/53\/3\/015"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/tns.2009.2027437"},{"key":"e_1_2_9_17_1","doi-asserted-by":"crossref","unstructured":"AklMA BouhaliO ToufiqueY KarpJS VandenbergheS.Monte Carlo sensitivity study of a long axial FOV PET scanner with patient adaptive rings. 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