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EUVIMED is the European alternative to PubMed: a central, multilingual research platform for medicine, nursing, life sciences and healthcare. It brings together international and European literature sources, study registries, open-access full texts, citations and retraction notices in one search. Unlike pure bibliographic databases, EUVIMED supports the entire research process – from discovery and appraisal with LIVIA and CLARA to traceable evidence synthesis. European in focus, transparent, interoperable and designed for science and healthcare.

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Lokaler Crossref-Datenbestand · journal-article

Learning-enhanced hybrid planar-SPECT dosimetry for voxel-level Monte Carlo dose estimation in radionuclide therapy

Zhengkun Dong, Xiao Jin, Xiangxi Meng, Wei Liu, Nan Li, Zhi Yang, Qiushi Ren, Jiangyuan Yu, Zhaoheng Xie

Physics in Medicine & Biology · 2026

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Abstract Objective. Quantitative imaging-based dosimetry is essential for optimizing [177Lu]Lu-DOTATATE peptide receptor radionuclide therapy (PRRT), yet full multi–time-point (MTP) SPECT/CT is difficult to implement in routine clinical practice. This study proposes a hybrid planar–SPECT framework for organ-level TIA estimation and STP-constrained voxel-level Monte Carlo dose analysis from a single post-therapy SPECT/CT acquisition. Approach. The proposed framework integrates single-time-point (STP) SPECT/CT with serial planar imaging to derive organ-level time-integrated activities (TIAs). A physics-informed support vector regression (SVR) model is incorporated to refine planar-to-SPECT scaling factors and improve the robustness of planar-derived activity quantification. The resulting organ TIAs are used to scale the fixed within-organ STP SPECT pattern and generate voxelized TIA maps as source distributions for Monte Carlo (MC)-based dose calculation. The method is evaluated against reference MTP SPECT/CT data in 11 patients and further applied to a separate cohort of 23 patients to explore vertebral dose heterogeneity and its association with hematologic toxicity. Main results. Hybrid dosimetry showed good agreement with MTP SPECT/CT, with organ-level errors of approximately 10–15%. The SVR-based refinement modestly reduced scaling bias, particularly in anatomically challenging structures. Voxel-level vertebral dose heterogeneity metrics showed moderate but exploratory correlations with hematologic toxicity (ρ ≈ 0.4–0.5) after false-discovery-rate correction, whereas conventional organ-averaged dose metrics showed no significant association. Significance. The proposed hybrid planar–SPECT framework supports clinically feasible organ-level dosimetry from a reduced acquisition protocol. The resulting voxelized maps provide STP-constrained approximate source distributions for MC dose calculation and exploratory characterization of spatial dose heterogeneity, rather than reconstructions of time-varying MTP voxel kinetics. The SVR improvement observed in the 11-patient internal validation remains preliminary, requiring larger cohorts for robustness and external validation for generalizability.

Bibliografischer Nachweis

Publikationsdaten

Autor:innen
Zhengkun Dong, Xiao Jin, Xiangxi Meng, Wei Liu, Nan Li, Zhi Yang, Qiushi Ren, Jiangyuan Yu, Zhaoheng Xie
Quelle
Physics in Medicine & Biology
Publikation
2026-01-01
Band / Ausgabe
Nicht angegeben
Seiten
Nicht angegeben
ISSN / ISBN
0031-9155, 1361-6560
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Zitierfähiger Nachweis

Zhengkun Dong, Xiao Jin, Xiangxi Meng, Wei Liu, Nan Li, Zhi Yang, Qiushi Ren, Jiangyuan Yu, Zhaoheng Xie (2026). Learning-enhanced hybrid planar-SPECT dosimetry for voxel-level Monte Carlo dose estimation in radionuclide therapy. Physics in Medicine & Biology. https://doi.org/10.1088/1361-6560/aea39b
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