ComBAT Harmonization for diffusion MRI: Challenges and Best Practices
원문 초록
Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review ComBAT's mathematical foundation, outlining these assumptions, and exploring their implications for the demographic composition necessary for optimal results. Through a series of experiments involving a slightly modified version of ComBAT called Pairwise-ComBAT tailored for normative modeling applications, we assess the impact of various population characteristics, including population size, age distribution, the absence of certain covariates, and the magnitude of additive and multiplicative factors. Based on these experiments, we present five essential recommendations that should be carefully considered to enhance consistency and supporting reproducibility, two essential factors for open science, collaborative research, and real-life clinical deployment.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Exploring the robustness of TractOracle methods in RL-based tractography
An Arbitrary-Modal Fusion Network for Volumetric Cranial Nerves Tract Segmentation
Equivariant Spherical CNNs for Accurate Fiber Orientation Distribution Estimation in Neonatal Diffusion MRI with Reduced Acquisition Time
DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI Tractography
DDCSR: A Novel End-to-End Deep Learning Framework for Cortical Surface Reconstruction from Diffusion MRI
FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI
토론
이 논문과 연결된 포럼 글이 아직 없습니다.