  Background: Implementing a standardized 31P-MRS dynamic acquisition protocol
to evaluate skeletal muscle energy metabolism and monitor muscle
fatigability1,2, while being compatible with various longitudinal clinical
studies on diversified patient cohorts, requires a high level of technicality
and expertise. Furthermore, processing data to obtain reliable results also
demands a great degree of expertise from the operator. In this two-part
article, we present an advanced quality control approach for data acquired
using a dynamic 31P-MRS protocol. The aim is to provide decision support to the
operator in order to assist in data processing and obtain reliable results
based on objective criteria. We present first in part one, an advanced data
quality control (QC) approach of a dynamic 31P-MRS protocol. Part two is an
impact study demonstrating the added value of the QC approach to explore
clinical results derived from two patient populations with significant fatigue:
COVID19 and multiple sclerosis (MS). Experimental: 31P-MRS was performed on a
3T clinical MRI in 175 subjects from clinical and healthy control populations
conducted in a University Hospital. An advanced data QC Score (QCS) was
developed using multiple objective criteria. The criteria were based on current
recommendations from the literature enriched by new proposals based on clinical
experience. The QCS was designed to indicate valid and corrupt data and guide
necessary objective data editing to extract as much valid physiological data as
possible. Dynamic acquisitions using an MR-compatible ergometer ran over a
rest(40s), exercise(2min), and a recovery phase(6min). Results: Using QCS
enabled rapid identification of subjects with data anomalies allowing the user
to correct the data series or reject them partially or entirely as well as
identify fully valid datasets. Overall, the use of the QCS resulted in the
automatic classification of 45% of the subjects including 58 participants that
had data with no criterion violation and 21 participants with violations that
resulted in the rejection of all dynamic data. The remaining datasets were
inspected manually with guidance allowing acceptance of full datasets from an
additional 80 participants and recovery phase data from an additional 16
subjects. Overall, more anomalies occurred with patient data (35% of datasets)
compared to healthy controls (15% of datasets). Conclusion: This paper
describes typical difficulties encountered during the dynamic acquisition of
31P-MRS. Based on these observations, a standardized data quality control
pipeline was created and implemented in both healthy and patient populations.
The QC scoring ensures a standardized data rejection procedure and rigorous
objective analysis of dynamic 31P-MRS data obtained from patients. The
contribution of this methodology contributes to efforts made to standardize the
practices of the 31P-MRS that has been underway for a decade, with the ultimate
goal of making it an empowered tool for clinical research.
