Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study
Abstract Background To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocard...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-09-01
|
| Series: | EJNMMI Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13550-022-00933-8 |
| _version_ | 1811273605518983168 |
|---|---|
| author | Takato Tanaka Masatoyo Nakajo Hirofumi Kawakami Eriko Motomura Tomofumi Fujisaka Satoko Ojima Yasumasa Saigo Takashi Yoshiura |
| author_facet | Takato Tanaka Masatoyo Nakajo Hirofumi Kawakami Eriko Motomura Tomofumi Fujisaka Satoko Ojima Yasumasa Saigo Takashi Yoshiura |
| author_sort | Takato Tanaka |
| collection | DOAJ |
| description | Abstract Background To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic 18F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10–60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40–60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon’s rank sum test, McNemar’s test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables. Results Both readers similarly rated 51 scans as positive (scores = 1–3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001). Conclusions Short-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF. |
| first_indexed | 2024-04-12T23:02:19Z |
| format | Article |
| id | doaj.art-46cb272a543341b2b879a0aa67944a4d |
| institution | Directory Open Access Journal |
| issn | 2191-219X |
| language | English |
| last_indexed | 2024-04-12T23:02:19Z |
| publishDate | 2022-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EJNMMI Research |
| spelling | doaj.art-46cb272a543341b2b879a0aa67944a4d2022-12-22T03:13:00ZengSpringerOpenEJNMMI Research2191-219X2022-09-0112111010.1186/s13550-022-00933-8Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility studyTakato Tanaka0Masatoyo Nakajo1Hirofumi Kawakami2Eriko Motomura3Tomofumi Fujisaka4Satoko Ojima5Yasumasa Saigo6Takashi Yoshiura7Department of Radiation Technology, Kagoshima University HospitalDepartment of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima UniversityAcademic Department, GE Healthcare JapanDepartment of Radiation Technology, Kagoshima University HospitalDepartment of Radiation Technology, Kagoshima University HospitalDepartment of Cardiovascular Medicine and Hypertension, Graduate School of Medical and Dental Sciences, Kagoshima UniversityDepartment of Radiation Technology, Kagoshima University HospitalDepartment of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima UniversityAbstract Background To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic 18F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10–60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40–60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon’s rank sum test, McNemar’s test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables. Results Both readers similarly rated 51 scans as positive (scores = 1–3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001). Conclusions Short-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF.https://doi.org/10.1186/s13550-022-00933-8Dynamic 18F-FDG-PET/CTPatlak Ki imagePopulation-based input functionIndividual patient-based input functionBayesian penalized likelihood reconstruction |
| spellingShingle | Takato Tanaka Masatoyo Nakajo Hirofumi Kawakami Eriko Motomura Tomofumi Fujisaka Satoko Ojima Yasumasa Saigo Takashi Yoshiura Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study EJNMMI Research Dynamic 18F-FDG-PET/CT Patlak Ki image Population-based input function Individual patient-based input function Bayesian penalized likelihood reconstruction |
| title | Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study |
| title_full | Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study |
| title_fullStr | Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study |
| title_full_unstemmed | Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study |
| title_short | Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study |
| title_sort | short time window patlak imaging using a population based arterial input function and optimized bayesian penalized likelihood reconstruction a feasibility study |
| topic | Dynamic 18F-FDG-PET/CT Patlak Ki image Population-based input function Individual patient-based input function Bayesian penalized likelihood reconstruction |
| url | https://doi.org/10.1186/s13550-022-00933-8 |
| work_keys_str_mv | AT takatotanaka shorttimewindowpatlakimagingusingapopulationbasedarterialinputfunctionandoptimizedbayesianpenalizedlikelihoodreconstructionafeasibilitystudy AT masatoyonakajo shorttimewindowpatlakimagingusingapopulationbasedarterialinputfunctionandoptimizedbayesianpenalizedlikelihoodreconstructionafeasibilitystudy AT hirofumikawakami shorttimewindowpatlakimagingusingapopulationbasedarterialinputfunctionandoptimizedbayesianpenalizedlikelihoodreconstructionafeasibilitystudy AT erikomotomura shorttimewindowpatlakimagingusingapopulationbasedarterialinputfunctionandoptimizedbayesianpenalizedlikelihoodreconstructionafeasibilitystudy AT tomofumifujisaka shorttimewindowpatlakimagingusingapopulationbasedarterialinputfunctionandoptimizedbayesianpenalizedlikelihoodreconstructionafeasibilitystudy AT satokoojima shorttimewindowpatlakimagingusingapopulationbasedarterialinputfunctionandoptimizedbayesianpenalizedlikelihoodreconstructionafeasibilitystudy AT yasumasasaigo shorttimewindowpatlakimagingusingapopulationbasedarterialinputfunctionandoptimizedbayesianpenalizedlikelihoodreconstructionafeasibilitystudy AT takashiyoshiura shorttimewindowpatlakimagingusingapopulationbasedarterialinputfunctionandoptimizedbayesianpenalizedlikelihoodreconstructionafeasibilitystudy |