Use of relative Patlak plot Ki′ images as an alternative to standard Patlak plot Ki images in clinical practice

The kinetic rate constant (Ki), derived from the Patlak slope, reflects 18F-FDG uptake and supports disease assessment. Standard Patlak Ki imaging requires prolonged dynamic acquisition and full arterial input function (IF), limiting clinical feasibility. The relative Patlak plot omits the early-phase IF and focuses on the linear phase of tracer kinetics using only late dynamic data and a partially sampled IF. This approach enables simplified parametric imaging without requiring full early-time data or population-based input functions (PBIFs). This study evaluated whether relative Ki′-images can substitute for standard Ki-images by assessing both quantitative agreement and visual appearance using a short axial field-of-view PET system. We analyzed data from 44 patients with lung or pancreatic disorders who underwent dynamic whole-body 18F-FDG PET/CT, followed by static SUV imaging. Standard Ki-images were generated using Patlak analysis from 7 to 60 min post-injection, based on full-time image-derived IF (0–60 min), with a scan duration of 53 min. Relative Ki′-images were reconstructed using three time schedules: (1) Ki′-49 min (t*=7 min post-injection; duration: 49 min), (2) Ki′-41 min (t*=15 min; duration: 41 min), and (3) Ki′-33 min (t*=24 min; duration: 33 min), each using partial IF (excluding data from injection to t*). Lesion conspicuity (score 0–3), SUV- and Ki/Ki′-parameters (SUVmax/mean, Ki-max/mean, Ki′-max/mean), and background noise were evaluated qualitatively (visual scale: 0–3) and quantitatively (standard deviation [SD] of liver Ki/Ki′) by two readers. Wilcoxon signed-rank test, Friedman’s test, McNemar’s test, and linear regression analyses were performed. Two readers assessed 35 positive and 9 negative scans (scores 1–3/0) for SUV, standard Ki, and relative Ki′-images. Correlations between standard Ki and Ki′ parameters were consistently strong (r²>0.97 for Ki-max and Ki-mean), with Ki′-49 min images showing the highest agreement (r²=0.98–0.99). Lesion conspicuity was comparable between standard Ki and Ki′-49 min/Ki′-41 min images, whereas Ki′-33 min images showed more downgrades (p < 0.05). Ki′-33 min also exhibited significantly higher background noise (p < 0.001), confirmed by liver SD. Relative Ki′-images showed excellent correlation with standard Ki-images in quantitative analysis, indicating feasibility for reduced-scan protocols. Ki′-49 min images retained accuracy but required longer scans. Future work should assess alternatives such as PBIF or dual-window protocols for clinical streamlining.

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Publication Details

Journal
EJNMMI Research
Published
2025-08-05
DOI
https://doi.org/10.1186/s13550-025-01295-7
Primary Topic
AI in cancer detection
Type
article
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article

Use of relative Patlak plot Ki′ images as an alternative to standard Patlak plot Ki images in clinical practice

Yutaro Kiyao, Fumiko Kanzaki, Kiyohisa Kamimura, Hirofumi Kawakami et al.
EJNMMI Research
AI in cancer detection
article

Use of relative Patlak plot Ki′ images as an alternative to standard Patlak plot Ki images in clinical practice

Yutaro Kiyao, Fumiko Kanzaki, Kiyohisa Kamimura, Hirofumi Kawakami, Atushi Tani, Mitsuho Hirahara, Yukari Eizuru, Koji Takumi, Takashi Yoshiura, Masatoyo Nakajo, Akie Katsuki
article en

Abstract

The kinetic rate constant (Ki), derived from the Patlak slope, reflects 18F-FDG uptake and supports disease assessment. Standard Patlak Ki imaging requires prolonged dynamic acquisition and full arterial input function (IF), limiting clinical feasibility. The relative Patlak plot omits the early-phase IF and focuses on the linear phase of tracer kinetics using only late dynamic data and a partially sampled IF. This approach enables simplified parametric imaging without requiring full early-time data or population-based input functions (PBIFs). This study evaluated whether relative Ki′-images can substitute for standard Ki-images by assessing both quantitative agreement and visual appearance using a short axial field-of-view PET system. We analyzed data from 44 patients with lung or pancreatic disorders who underwent dynamic whole-body 18F-FDG PET/CT, followed by static SUV imaging. Standard Ki-images were generated using Patlak analysis from 7 to 60 min post-injection, based on full-time image-derived IF (0–60 min), with a scan duration of 53 min. Relative Ki′-images were reconstructed using three time schedules: (1) Ki′-49 min (t*=7 min post-injection; duration: 49 min), (2) Ki′-41 min (t*=15 min; duration: 41 min), and (3) Ki′-33 min (t*=24 min; duration: 33 min), each using partial IF (excluding data from injection to t*). Lesion conspicuity (score 0–3), SUV- and Ki/Ki′-parameters (SUVmax/mean, Ki-max/mean, Ki′-max/mean), and background noise were evaluated qualitatively (visual scale: 0–3) and quantitatively (standard deviation [SD] of liver Ki/Ki′) by two readers. Wilcoxon signed-rank test, Friedman’s test, McNemar’s test, and linear regression analyses were performed. Two readers assessed 35 positive and 9 negative scans (scores 1–3/0) for SUV, standard Ki, and relative Ki′-images. Correlations between standard Ki and Ki′ parameters were consistently strong (r²>0.97 for Ki-max and Ki-mean), with Ki′-49 min images showing the highest agreement (r²=0.98–0.99). Lesion conspicuity was comparable between standard Ki and Ki′-49 min/Ki′-41 min images, whereas Ki′-33 min images showed more downgrades (p < 0.05). Ki′-33 min also exhibited significantly higher background noise (p < 0.001), confirmed by liver SD. Relative Ki′-images showed excellent correlation with standard Ki-images in quantitative analysis, indicating feasibility for reduced-scan protocols. Ki′-49 min images retained accuracy but required longer scans. Future work should assess alternatives such as PBIF or dual-window protocols for clinical streamlining.

EJNMMI ResearchVol. 15(1)
Kagoshima University (JP), General Electric (Spain) (ES), Tokyo Health Care University (JP), Kagoshima University Hospital (JP)
Kagoshima University, Japan Society for the Promotion of Science
Openalex Percentile: Top 90%
AI in cancer detection
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