Purpose: To evaluate the validity of different approaches to determine the signal‐to‐noise ratio (SNR) in MRI experiments with multi‐element surface coils, parallel imaging, and different reconstruction filters.Materials and Methods: Four different approaches of SNR calculation were compared in phantom measurements and in vivo based on: 1) the pixel‐by‐pixel standard deviation (SD) in multiple repeated acquisitions; 2) the signal statistics in a difference image; and 3) and 4) the statistics in two separate regions of a single image employing either the mean value or the SD of background noise. Different receiver coil systems (with one and eight channels), acquisitions with and without parallel imaging, and five different reconstruction filters were compared.Results: Averaged over all phantom measurements, the deviations from the reference value provided by the multiple‐acquisitions method are 2.7% (SD 1.6%) for the difference method, 37.7% (25.9%) for the evaluation of the mean value of background noise, and 34.0% (38.1%) for the evaluation of the SD of background noise.Conclusion: The conventionally determined SNR based on separate signal and noise regions in a single image will in general not agree with the true SNR measured in images after the application of certain reconstruction filters, multichannel reconstruction, or parallel imaging. J. Magn. Reson. Imaging 2007.

Alzheimer's disease is characterized by the degeneration and loss of cholinergic neurones in the nucleus basalis Meynert, located within the substantia innominata at the ventral surface of the basal forebrain. An in vivo measure of morphological changes in the nucleus basalis Meynert would be of high relevance to better understand the structural correlate of cholinergic dysfunction in Alzheimer's disease. In this study, we applied a newly developed automated technique of image regression analysis, implemented through code written in Matlab 5.3 (MathWorks, Natick, MA), to the analysis of proton density weighted structural MRI of the basal forebrain from 13 patients with Alzheimer's disease (mean age = 77.5 years, SD = 4.4 years, 8 women) and 12 healthy elderly subjects (mean age = 62.3 years, SD = 5.6 years, 6 women). This technique allows searching a large portion of the substantia innominata for signal changes. We used corresponding MRI and histological sections of a post mortem brain to map the locations of basal forebrain cholinergic nuclei into the MRI standard space. Additionally, we used voxel-based morphometry, implemented in SPM2 (Wellcome Department of Imaging Neuroscience, London, UK) to determine correlations between signal changes in the substantia innominata and cortical grey matter atrophy in the patients with Alzheimer's disease. When matching the locations of signal reductions in the in vivo MRI to the template of basal nuclei based on the postmortem brain, signal intensity was decreased in areas corresponding to anterior lateral and anterior medial nucleus basalis Meynert and increased in the third ventricle, the transverse fissure and the optic tract in patients with Alzheimer's disease compared with controls. The reduction of the signal intensity in an area corresponding to the anterior lateral nucleus basalis Meynert was significantly correlated with reduced grey matter concentration in the bilateral prefrontal cortex, inferior parietal lobule and cingulate gyrus. Our findings suggest that signal changes occur in patients with Alzheimer's disease in the substantia innominata which may be related to the loss or degeneration of cholinergic neurones and correspond to regional cortical grey matter atrophy. If replicated in an independent sample, our technique may be useful to detect degeneration of basal forebrain cholinergic neurones in vivo.