The term reproducible research refers to the idea that the ultimate product of academic research is the paper along with the full computational environment used to produce the results in the paper such as the code, data, etc., that can be used to reproduce the results and create new work based on the research.

“An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.” —— David Donoho

See my codes on GitHub: JianCheng@GitHub, Milan@GitHub, and DiffusionMRITool@GitHub.

Diffusion MRI Tool

DMRITool is an open source toolbox for diffusion MRI data processing. It is written in C++ with matlab mex interface. The source code is in github. Most of the methods I proposed for diffusion MRI have been or will be released in DMRITool. DMRITool has implemented:

Brain Age Estimation

TSAN-brain-age-estimation: Pytorch codes for the paper “Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss”, IEEE transactions on Medical Imaging, 2021.

LRTV for Image Superresolution

LRTV: a Matlab toolbox implemented Low-Rank Total Variation for image super-resolution proposed in the paper “LRTV: MR Image Super-Resolution with Low-Rank and Total Variation Regularizations”, IEEE Transactions on Medical Imaging, 2015. a python toolbox to covert a bibtex file into an html file. It considers additional fields (e.g., note, code, etc.) in bibtex. It also can show corresponding google scholar citations related with bibtex entries by parsing the google scholar profile. See my publications as an example.