Diffusion-weighted MRI studies generally lose signal intensity to physiological motion, which can adversely affect quantification/diagnosis. Averaging over multiple repetitions, often used to improve image quality, does not eliminate the signal loss. In this article, PCATMIP, a combined principal component analysis and temporal maximum intensity projection approach, is developed to address this problem. Data are first acquired for a fixed number of repetitions. Assuming that physiological fluctuations of image intensities locally are likely temporally correlated unlike random noise, a local moving boxcar in the spatial domain is used to reconstruct low-noise images by considering the most relevant principal components in the temporal domain. Subsequently, a temporal maximum intensity projection yields a high signal-intensity image. Numerical and experimental studies were performed for validation and to determine optimal parameters for increasing signal intensity and minimizing noise. Subsequently, a combined principal component analysis and temporal maximum intensity projection approach was used to analyze diffusion-weighted porcine liver MRI scans. In these scans, the variability of apparent diffusion coefficient values among repeated measurements was reduced by 59% relative to averaging, and there was an increase in the signal intensity with higher intensity differences observed at higher b-values. In summary, a combined principal component analysis and temporal maximum intensity projection approach is a postprocessing approach that corrects for bulk motion-induced signal loss and improves apparent diffusion coefficient measurement reproducibility.
Copyright © 2010 Wiley-Liss, Inc.