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In this second part of a two‐part paper, we intend to demonstrate the impact of the previously proposed advanced quality control pipeline. To understand its benefit and challenge the proposed methodology in a real scenario, we chose to compare the outcome when applying it to the analysis of two patient populations with significant but highly different types of fatigue: COVID‐19 and multiple sclerosis (MS).  31 P‐MRS was performed on a 3 T clinical MRI, in 19 COVID‐19 patients, 38 MS patients, and 40 matched healthy controls. Dynamic acquisitions using an MR‐compatible ergometer ran over a rest (40 s), exercise (2 min), and a recovery phase (6 min). Long and short  T  R  acquisitions were also made at rest for  T  1  correction. The advanced data quality control pipeline presented in Part 1 is applied to the selected patient cohorts to investigate its impact on clinical outcomes. We first used power and sample size analysis to estimate objectively the impact of adding the quality control score (QCS). Then, comparisons between patients and healthy control groups using the validated QCS were performed using unpaired  t  tests or Mann–Whitney tests ( p  &lt; 0.05). The application of the QCS resulted in increased statistical power, changed the values of several outcome measures, and reduced variability (standard deviation). A significant difference was found between the  T  1PCr  and  T  1Pi  values of MS patients and healthy controls. Furthermore, the use of a fixed correction factor led to systematically higher estimated concentrations of PCr and P i  than when using individually corrected factors. We observed significant differences between the two patient populations and healthy controls for resting [PCr]—MS only, [P i ], [ADP], [H 2 PO 4  − ], and pH—COVID‐19 only, and post‐exercise [PCr], [P i ], and [H 2 PO 4  − ]—MS only. The dynamic indicators  τ  PCr ,  τ  Pi ,  V  iPCr , and  V  max  were reduced for COVID‐19 and MS patients compared with controls. Our results show that QCS in dynamic  31 P‐MRS studies results in smaller data variability and therefore impacts study sample size and power. Although QCS resulted in discarded data and therefore reduced the acceptable data and subject numbers, this rigorous and unbiased approach allowed for proper assessment of muscle metabolites and metabolism in patient populations. The outcomes include an increased metabolite  T  1 , which directly affects the  T  1  correction factor applied to the amplitudes of the metabolite, and a prolonged  τ  PCr , indicating reduced muscle oxidative capacity for patients with MS and COVID‐19.

Alteration of skeletal muscle energy metabolism assessed by 31 P MRS in clinical routine: Part 2. Clinical application