Several consensus papers by MRS experts have addressed data collection, analysis, and reporting standards. Despite this, the usage of the MRSinMRS standardized reporting criteria remain sparsely utilized, impeding research rigor and reproducibility. To overcome this, the ‘Reproducibility Made Easy’ software automates table population and methods section generation, streamlining the process with a single raw dataset, removing manual data entry.
We would love to extend this tool to other modalities of MR as well. Let us know if you want to join us in this effort!
One of the main reasons why MRS is still not widely used for multi-site clinical studies is the enormous diversity in data processing methods. Virtually every lab has tailor-made 'in-house' code that they have sometimes cultivated for decades. Unfortunately, MRS metabolite concentration estimates are extremely sensitive to details of data processing, modeling, and quantification workflows.
This project aims to create a standardized library of basic and advanced data processing classes in the most widely used programming languages (Python, MATLAB, R, C++). The foundational building blocks of this library will be the basic MRS processing steps (zero-filling, line-broadening, concatenating and splitting transients, etc.). As the library grows, more complex methods will be incorporated.
We will build all classes around the new NIfTI-MRS data storage specification to ensure optimal interoperability and translatability of processing workflows.