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Abstract
Background: Amyloid PET/CT is essential for quantifying amyloid-beta (Aβ) deposition in Alzheimer's disease (AD), with the Centiloid (CL) scale standardizing measurements across imaging centers. However, MRI-based CL pipelines face challenges: high cost, contraindications, and patient burden. To address these challenges, we developed a deep learning-based CT parcellation pipeline calibrated to the standard CL scale using CT images from PET/CT scans and evaluated its performance relative to standard pipelines.

Methods: A total of 306 participants (23 young controls [YCs] and 283 patients) underwent 18 F-florbetaben (FBB) PET/CT and MRI. Based on visual assessment, 207 patients were classified as Aβ-positive and 76 as Aβ-negative. PET images were processed using the CT parcellation pipeline and compared to FreeSurfer (FS) and standard pipelines. Agreement was assessed via regression analyses. Effect size, variance, and ROC analyses were used to compare pipelines and determine the optimal CL threshold relative to visual Aβ assessment.

Results: The CT parcellation showed high concordance with the FS and provided reliable CL quantification (R² = 0.99). Both pipelines demonstrated similar variance in YCs and effect sizes between YCs and ADCI. ROC analyses confirmed comparable accuracy and similar CL thresholds, supporting CT parcellation as a viable MRI-free alternative.

Conclusions: Our findings indicate that the CT parcellation pipeline achieves a level of accuracy similar to FS in CL quantification, demonstrating its reliability as an MRI-free alternative. In PET/CT, CT and PET are acquired sequentially within the same session on a shared bed and headrest, which helps maintain consistent positioning and adequate spatial alignment, reducing registration errors and supporting more reliable and precise quantification.

Affiliations

Yeo Jun Yoon # 1, Seungbeom Seo # 1, Sangwon Lee 2, Hyunkeong Lim 2, Kyobin Choo 3, Daesung Kim 4, Hyunkyung Han 4, Minjae So 1, Hosung Kang 5, Seongjin Kang 2, Dongwoo Kim 2 6, Young-Gun Lee 7, Dongho Shin 8, Tae Joo Jeon 9 10, Mijin Yun 11
1Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
2Department of Nuclear Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
3Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
4Department of Artificial Intelligence, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
5ELTEC College of Engineering, Ewha Woman's University, 52 Ewhayeodae- gil, Seodaemun-gu, Seoul, 03760, Republic of Korea.
6Department of Nuclear Medicine, Hallym University Sacred Heart Hospital, 22 Gwanpyeong-ro, Dongan-gu, Anyang, 14068, Republic of Korea.
7Department of Neurology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang, 10380, Republic of Korea.
8Massachusetts College of Pharmacy & Health Sciences, Boston, USA.
9Department of Nuclear Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. TJEONNM@yuhs.ac.
10Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonjuro, Gangnam-gu, Seoul, 06273, Republic of Korea. TJEONNM@yuhs.ac.
11Department of Nuclear Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. YUNMIJIN@yuhs.ac.
#Contributed equally.