Imaging using magnetic resonance (MR) provides superior softtissue contrast resolution over other screening techniques used in medicine. Analysis of the images is generally performed by spatial-domain-analysis-based processing techniques, yet, as with most techniques, improvements to the identification and separation of signal sources are being sought continuously. A recent approach that has demonstrated potential and promise1 considers a set of multiple MR frames as a single multispectral image, where each spectral band in the set is acquired during a particular pulse sequence. Independent component analysis (ICA) is then used to enhance tissue contrast. The technique is capable of blindly separating unknown signal sources into statistically independent components without prior knowledge. For the approach to be effective, however, there can be no more than one gaussian signal source in the data.2 Since noise in MR images is nongaussian,3 ICA seems a very appropriate analysis technique for magnetic resonance imaging (MRI). While ICA has shown its strengths in MRI, it also suffers from a drawback, known as ‘overcomplete ICA.’ It occurs when p, the number of unknown signal sources (generally the number of tissue types), is greater than the number of combined images, L, used in the separation. This condition often occurs in brain imaging usingMRI. The number of tissues of interest, such as cerebral spinal fluid (CSF), gray matter (GM), white matter (WM), skull, skin, andmuscle, is always greater than the number of MR pulse sequences (e.g., spin-lattice and spin-spin relaxation times, proton density) obtainable. Under such circumstances, the three independent components resulting from ICA must accommodate p > 3 signal sources, and thus more than one signal source must be accommodated in a single component. In this case, the full Figure 1. (a) Simulated MR and (b) ’ground truth’ images of braintissue substances shown in (a). Images were generated by the simulator at McGill University (http://www.bic.mni.mcgill.ca/brainweb/). PDW1: Proton-density weighted, T1W1: Spin-lattice weighted, T2W1: Spin-spin weighted.