g., Refs. [34], [35], [36], [37] and [38]). Currently, the most investigated approach is to assign attenuation values based on tissue class assigned through segmentation of the MR image data. Segmentation of MR images is a maturing field, so there are several readily applicable techniques available for this purpose (see, e.g., Ref. [39]). While soft tissues are typically easily segmented from MR images, the automatic differentiation of bone (with little

to no proton signal when acquired using conventional MR methods) and air (no proton signal) is more complicated. To address this problem, some investigators have developed atlas-based methods which rely on multiple MR image sets that have been averaged to form a high signal-to-noise ratio (SNR) template to which attenuation coefficients are assigned to the various tissue regions [38] and [40]. Patient-specific attenuation correction is then performed by warping AG-014699 concentration the new MRI data check details to the atlas followed by assigning the attenuation coefficients. Another potential approach for segmenting bone via MRI would be to employ so-called ultra-short echo time imaging which enables the acquisition of data with echo times less than 100 μs, thereby allowing for the visualization of the very short T2 of cortical bone [41]. A recent contribution compared the relative merits of segmentation- and atlas-based methods [38]. The segmentation approach was based on a whole-body

Dixon fat–water segmentation [42] in which the MR images were partitioned into five tissue classes (not including bone) and each class was assigned an appropriate linear attenuation coefficient. The atlas-based method employs a previously acquired database of aligned MRI–CT data sets that are then registered to the test data set in order to assign attenuation coefficients on a continuous scale. The two approaches were then compared in healthy as well

as disease sites. In the healthy-appearing tissues, the average mean errors of the SUV were 14.1% and 7.7% for the segmentation- and atlas-based second approaches, respectively. For the lesion sites, the errors were 7.5% and 5.7%, respectively. The authors concluded that the atlas-based approach was superior and that this was due to the reduced errors made in areas near the bones and lungs. A potential limitation of MR-based attenuation approaches is that methods developed for the head and brain are less likely to provide robust performance in whole-body MR imaging. First, despite continued improvements, there remain technical challenges for routinely obtaining high-resolution, artifact-free MR images of the abdomen and chest. In this region, MR examinations tend to be targeted to specific studies where the improved contrast resolution of MR can solve specific diagnostic dilemmas, for example, the evaluation of liver lesions, rather than as a routine tool for abdominal examinations, the province of CT.