Parallel Imaging Reconstruction in Public Datasets Biases Downstream Analysis in Retrospective Sampling Studies

Motivation: We explore the “Implicit Data Crime” of datasets whose subsampled k-space is filled using parallel imaging. These datasets are treated as fully-sampled, but their points derive from (1)prospective sampling, and (2)reconstruction of un-sampled points, creating artificial data correlations given low SNR or high acceleration. Goal(s): How will downstream tasks, including reconstruction algorithm comparison and optimal trajectory design, be biased by effects of parallel imaging on a prospectively undersampled dataset?Approach: Comparing reconstruction performance using data that are fully sampled with data that are completed using the SENSE algorithm. Results: Utilizing parallel imaging filled k-space results in biased downstream perception of algorithm performance. Impact: This study demonstrates evidence of overly-optimistic bias resulting from the use of k-space filled in with parallel imaging as ground truth data. Researchers should be aware of this possibility and carefully examine the computational pipeline behind datasets they use.