Direct localization and delineation of human pedunculopontine nucleus based on a self-supervised magnetic resonance image super-resolution method

被引:2
|
作者
Li, Jun [1 ]
Guan, Xiaojun [2 ]
Wu, Qing [1 ]
He, Chenyu [1 ]
Zhang, Weimin [1 ]
Lin, Xiyue [1 ]
Liu, Chunlei [3 ,4 ]
Wei, Hongjiang [5 ,6 ]
Xu, Xiaojun [2 ,9 ]
Zhang, Yuyao [1 ,7 ,8 ]
机构
[1] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
[2] Zhejiang Univ, Affiliated Hosp 2, Dept Radiol, Sch Med, Hangzhou, Peoples R China
[3] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA USA
[4] Univ Calif Berkeley, Helen Wills Neurosci Inst, Berkeley, CA USA
[5] Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China
[6] Shanghai Jiao Tong Univ, Inst Med Robot, Shanghai, Peoples R China
[7] ShanghaiTech Univ, Ihuman Inst, Shanghai, Peoples R China
[8] ShanghaiTech Univ, Sch Informat Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China
[9] Zhejiang Univ, Affiliated Hosp 2, Dept Radiol, Sch Med, 88 Jiefang Rd, Hangzhou 310009, Zhejiang, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
brainstem; deep brain stimulation; direct targeting; implicit representation; pedunculopontine nucleus; quantitative susceptibility mapping; self-supervised image super-resolution; DEEP BRAIN-STIMULATION; SUBTHALAMIC NUCLEUS; PARKINSONS-DISEASE; ATLAS; CONNECTIVITY; VALIDATION; PROTOCOL; ANATOMY; REGION; GAIT;
D O I
10.1002/hbm.26311
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The pedunculopontine nucleus (PPN) is a small brainstem structure and has attracted attention as a potentially effective deep brain stimulation (DBS) target for the treatment of Parkinson's disease (PD). However, the in vivo location of PPN remains poorly described and barely visible on conventional structural magnetic resonance (MR) images due to a lack of high spatial resolution and tissue contrast. This study aims to delineate the PPN on a high-resolution (HR) atlas and investigate the visibility of the PPN in individual quantitative susceptibility mapping (QSM) images. We combine a recently constructed Montreal Neurological Institute (MNI) space unbiased QSM atlas (MuSus-100), with an implicit representation-based self-supervised image super-resolution (SR) technique to achieve an atlas with improved spatial resolution. Then guided by a myelin staining histology human brain atlas, we localize and delineate PPN on the atlas with improved resolution. Furthermore, we examine the feasibility of directly identifying the approximate PPN location on the 3.0-T individual QSM MR images. The proposed SR network produces atlas images with four times the higher spatial resolution (from 1 to 0.25 mm isotropic) without a training dataset. The SR process also reduces artifacts and keeps superb image contrast for further delineating small deep brain nuclei, such as PPN. Using the myelin staining histological atlas as guidance, we first identify and annotate the location of PPN on the T1-weighted (T1w)-QSM hybrid MR atlas with improved resolution in the MNI space. Then, we relocate and validate that the optimal targeting site for PPN-DBS is at the middle-to-caudal part of PPN on our atlas. Furthermore, we confirm that the PPN region can be identified in a set of individual QSM images of 10 patients with PD and 10 healthy young adults. The contrast ratios of the PPN to its adjacent structure, namely the medial lemniscus, on images of different modalities indicate that QSM substantially improves the visibility of the PPN both in the atlas and individual images. Our findings indicate that the proposed SR network is an efficient tool for small-size brain nucleus identification. HR QSM is promising for improving the visibility of the PPN. The PPN can be directly identified on the individual QSM images acquired at the 3.0-T MR scanners, facilitating a direct targeting of PPN for DBS surgery.
引用
收藏
页码:3781 / 3794
页数:14
相关论文
共 50 条
  • [21] Cross-View Recurrence-Based Self-Supervised Super-Resolution of Light Field
    Sheng, Hao
    Wang, Sizhe
    Yang, Da
    Cong, Ruixuan
    Cui, Zhenglong
    Chen, Rongshan
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 33 (12) : 7252 - 7266
  • [22] Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization
    Li, Bin
    Keikhosravi, Adib
    Loeffler, Agnes G.
    Eliceiri, Kevin W.
    MEDICAL IMAGE ANALYSIS, 2021, 68
  • [23] STRESS: Super-Resolution for Dynamic Fetal MRI Using Self-supervised Learning
    Xu, Junshen
    Turk, Esra Abaci
    Grant, P. Ellen
    Golland, Polina
    Adalsteinsson, Elfar
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VII, 2021, 12907 : 197 - 206
  • [24] Toward extreme face super-resolution in the wild: A self-supervised learning approach
    Sidiya, Ahmed Cheikh
    Li, Xin
    FRONTIERS IN COMPUTER SCIENCE, 2022, 4
  • [25] Self-supervised depth super-resolution with contrastive multiview pre-training
    Qiao, Xin
    Ge, Chenyang
    Zhao, Chaoqiang
    Tosi, Fabio
    Poggi, Matteo
    Mattoccia, Stefano
    NEURAL NETWORKS, 2023, 168 : 223 - 236
  • [26] Learning Mutual Modulation for Self-supervised Cross-Modal Super-Resolution
    Dong, Xiaoyu
    Yokoya, Naoto
    Wang, Longguang
    Uezato, Tatsumi
    COMPUTER VISION, ECCV 2022, PT XIX, 2022, 13679 : 1 - 18
  • [27] Looking beyond input frames: Self-supervised adaptation for video super-resolution
    Yoo, Jinsu
    Nam, Jihoon
    Baik, Sungyong
    Kim, Tae Hyun
    PATTERN RECOGNITION, 2024, 154
  • [28] Self-Supervised Super-Resolution for Anisotropic MR Images with and Without Slice Gap
    Remedios, Samuel W.
    Han, Shuo
    Zuo, Lianrui
    Carass, Aaron
    Pham, Dzung L.
    Prince, Jerry L.
    Dewey, Blake E.
    SIMULATION AND SYNTHESIS IN MEDICAL IMAGING, SASHIMI 2023, 2023, 14288 : 118 - 128
  • [29] Self-supervised multicontrast super-resolution for diffusion-weighted prostate MRI
    Gundogdu, Batuhan
    Medved, Milica
    Chatterjee, Aritrick
    Engelmann, Roger
    Rosado, Avery
    Lee, Grace
    Oren, Nisa C.
    Oto, Aytekin
    Karczmar, Gregory S.
    MAGNETIC RESONANCE IN MEDICINE, 2024, 92 (01) : 319 - 331
  • [30] SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution
    Qian, Xiao
    Jiang, Tai-Xiang
    Zhao, Xi-Le
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2023, 16 : 215 - 227