![]() In addition to fiducial landmarks, other quantitative and semi-quantitative methodologies for assessment have been used. 9 An on-line data base of MR imaging and sonography brain volumes also contains 19–40 landmarks per patient to assess registration accuracy. External, invasive skull-implanted markers have been used as fiducial landmarks to assess CT-MR imaging and PET-MR imaging registrations, 4 the 8 corner voxels of a box around the head have been used to assess MR-MR brain registrations, 8 and 256 anatomic landmarks throughout the brain have been used to assess intersubject MR-MR registrations. Within the academic research world, various prior methods have been used to assess the performance of registration algorithms. This situation is to the ultimate detriment of the patient undergoing treatments that rely on these algorithms being highly accurate. 7 Such knowledge of the behavior of a commercial product can improve its use in the clinic, but if commercial vendors do not use objective metrics to characterize thoroughly the performance of their products and then make these results available, rational choices and improvements cannot be made. They demonstrated that whole-brain volume registrations could have errors ranging from 0.7 to 2 mm, depending on the region of the brain, and they therefore recommended using a volume of interest to improve local registrations when a particular area was important. In a study that used a widely accepted, neurosurgical commercial package, Hoelper et al 7 placed 25 anatomic landmarks in T1 and T2 brain volumes to test the registration error for 39 patients, a rare example of a publicly available assessment for a commercial registration solution. Performance assessments of these products from commercial vendors would be clinically useful, however, such information is seldom available. Furthermore, due to the proprietary nature of these commercial algorithms, the explicit transformations are often not disclosed thus, there are relatively few publicly available figures of merit to assess the performance of these heavily used commercial solutions that are essential to clinical neuroimaging. Research publications about new registration methods for MR images of the brain nearly always include quantitative assessments of their performance, while commercial registration solutions are often released without disclosing the performance metrics of the vendor. Most commercially available MR image–analysis software packages have some implementation of image registration, and such techniques have a thorough, well-documented grounding in the literature. Image registration is an essential step in the analysis of brain MR imaging data from multiple images because it ensures the spatial correspondence of anatomy across complementary information sources for diagnosis and treatment. ![]()
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