Cross-Tracer and Cross-Scanner Transfer Learning-Based Attenuation Correction for Brain SPECT

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-03-08 DOI:10.1109/TRPMS.2024.3374207
Hao Sun;Yu Du;Ching-Ni Lin;Han Jiang;Wenbo Huang;Pai-Yi Chiu;Guang-Uei Hung;Lijun Lu;Greta S. P. Mok
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Abstract

This study aims to investigate robust attenuation correction (AC) by generating attenuation maps $(\mu $ -maps) from nonattenuation-corrected (NAC) brain SPECT data using transfer learning (TL). Four sets of brain SPECT data ( $4\times 30$ ) were retrospectively collected: S-TRODAT-1, S-ECD, G-TRODAT-1, and G-ECD. A 3-D attention-based conditional generative adversarial network was pretrained using 22 paired 3-D NAC SPECT images and corresponding CT $\mu $ -maps for four patient groups. Various numbers ( $n\,\,=$ 4–22) of paired NAC SPECT and corresponding $\mu $ -maps from S-TRODAT-1 were then used to fine-tune (FT) the other three pretrained deep learning (DL) networks, i.e., S-ECD, G-TRODAT-1, and G-ECD. All patients in S-TRODAT-1 group were tested on their own network (DL-AC), and on the pretrained models with FT (FT-AC) and without FT (NFT-AC). The FT-AC methods used 22 (FT22), 12 (FT12), 8 (FT8), and 4 (FT4) paired data for FT, respectively. Our results show that FT22 and FT12 could outperform DL-AC for cross-tracer S-ECD and cross-scanner G-TRODAT-1 using CT-based AC (CT-AC) as the reference. FT22 also outperforms DL-AC for cross-tracer+cross-scanner G-ECD. FT8 performs comparably to DL-AC, while FT4 is worse than DL-AC but still better than NAC and NFT-AC in each group. Attenuation map generation is feasible for brain SPECT based on cross-tracer and/or cross-scanner FT-AC using a smaller number of patient data. The FT-AC performance improves as the number of data used for FT increases.
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基于跨示踪器和跨扫描仪转移学习的脑 SPECT 衰减校正
本研究旨在利用迁移学习(TL)从非衰减校正(NAC)脑SPECT数据生成衰减图(\mu $ -maps),从而研究稳健衰减校正(AC)。我们回顾性地收集了四组脑SPECT数据(4\times 30$):S-TRODAT-1、S-ECD、G-TRODAT-1 和 G-ECD。使用四组患者的22个成对三维NAC SPECT图像和相应的CT $\mu $ -地图,对基于三维注意力的条件生成对抗网络进行了预训练。然后使用S-TRODAT-1中不同数量($n\\,=$ 4-22)的配对NAC SPECT和相应的$\mu $ -maps来微调(FT)其他三个预训练的深度学习(DL)网络,即S-ECD、G-TRODAT-1和G-ECD。S-TRODAT-1组的所有患者都在自己的网络(DL-AC)上进行了测试,并在有FT(FT-AC)和无FT(NFT-AC)的预训练模型上进行了测试。FT-AC 方法分别使用了 22(FT22)、12(FT12)、8(FT8)和 4(FT4)个配对数据进行 FT。结果表明,在交叉示踪 S-ECD 和交叉扫描仪 G-TRODAT-1 中,以基于 CT 的交流(CT-AC)为参考,FT22 和 FT12 的效果优于 DL-AC。在交叉示踪+交叉扫描 G-ECD 方面,FT22 也优于 DL-AC。FT8 的表现与 DL-AC 相当,而 FT4 则不如 DL-AC,但在每组中仍优于 NAC 和 NFT-AC。基于跨示踪剂和/或跨扫描仪 FT-AC 的脑 SPECT 可使用较少的患者数据生成衰减图。随着用于 FT 的数据数量增加,FT-AC 性能也会提高。
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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