The measurement of BMD by dual-energy X-ray absorptiometry (DXA) is the ''gold standard'' for diagnosing osteoporosis but does not directly reflect deterioration in bone microarchitecture. The trabecular bone score (TBS), a novel gray-level texture measurement that can be extracted from DXA images, correlates with 3D parameters of bone microarchitecture. Our aim was to evaluate the ability of lumbar spine TBS to predict future clinical osteoporotic fractures. A total of 29,407 women 50 years of age or older at the time of baseline hip and spine DXA were identified from a database containing all clinical results for the Province of Manitoba, Canada. Health service records were assessed for the incidence of nontraumatic osteoporotic fracture codes subsequent to BMD testing (mean follow-up 4.7 years). Lumbar spine TBS was derived for each spine DXA examination blinded to clinical parameters and outcomes. Osteoporotic fractures were identified in 1668 (5.7%) women, including 439 (1.5%) spine and 293 (1.0%) hip fractures. Significantly lower spine TBS and BMD were identified in women with major osteoporotic, spine, and hip fractures (all p < 0.0001). Spine TBS and BMD predicted fractures equally well, and the combination was superior to either measurement alone ( p < 0.001). Spine TBS predicts osteoporotic fractures and provides information that is independent of spine and hip BMD. Combining the TBS trabecular texture index with BMD incrementally improves fracture prediction in postmenopausal women. ß

The National Electrical Manufacturers Association (NEMA) standard NU 4-2008 for performance measurements of small-animal tomographs was recently published. Before this standard, there were no standard testing procedures for preclinical PET systems, and manufacturers could not provide clear specifications similar to those available for clinical systems under NEMA NU 2-1994 and 2-2001. Consequently, performance evaluation papers used methods that were modified ad hoc from the clinical PET NEMA standard, thus making comparisons between systems difficult. Methods We acquired NEMA NU 4-2008 performance data for a collection of commercial animal PET systems manufactured since 2000: micro- PET P4, microPET R4, microPET Focus 120, microPET Focus 220, Inveon, ClearPET, Mosaic HP, Argus (formerly eXplore Vista), VrPET, LabPET 8, and LabPET 12. The data included spatial resolution, counting-rate performance, scatter fraction, sensitivity, and image quality and were acquired using settings for routine PET. Results The data showed a steady improvement in system performance for newer systems as compared with first-generation systems, with notable improvements in spatial resolution and sensitivity. Conclusion Variation in system design makes direct comparisons between systems from different vendors difficult. When considering the results from NEMA testing, one must also consider the suitability of the PET system for the specific imaging task at hand.

We extend the multiple-scattering theory for elastic waves by taking into account the full vector character. The formalism for both the band structure calculation and the reflection and transmission calculations for finite slabs is presented. The latter is based on a double-layer scheme which obtains the reflection and transmission matrix elements for the multilayer slab from those of a single layer. As a demonstration of applications of the formalism, we calculate the band structures of elastic waves propagating in a three-dimensional periodic arrangement of spherical particles and voids, as well as the transmission coefficients through finite slabs. In contrast with the plane-wave method, the multiple-scattering approach exhibits advantages in handling specialized geometries ͑spherical geometry in the present case͒. We also present a comparison between theory and ultrasound experiment for a hexagonal-close-packed array of steel balls immersed in water. Excellent agreement is obtained.

Utilizing the publicly available neuroimaging database enabled by Alzheimer’s disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson’s Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

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