By Maxime Descoteaux, Rachid Deriche, Denis Le Bihan, Jean-François Mangin, Cyril Poupon (auth.), Jerry L. Prince, Dzung L. Pham, Kyle J. Myers (eds.)
This booklet constitutes the refeered complaints of the twenty first overseas convention on details Processing in clinical Imaging, IPMI 2009, held in Williamsburg, VA, united states, in July 2009
The 26 revised complete papers and 33 revised poster papers awarded have been conscientiously reviewed and chosen from one hundred fifty submissions. The papers are equipped in topical sections on diffusion imaging, puppy imaging, picture registration, practical networks, area curves, tractography, microscopy, exploratory analyses, good points and detection, photograph guided surgical procedure, form research, movement, and segmentation and validation.
Read or Download Information Processing in Medical Imaging: 21st International Conference, IPMI 2009, Williamsburg, VA, USA, July 5-10, 2009. Proceedings PDF
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Additional info for Information Processing in Medical Imaging: 21st International Conference, IPMI 2009, Williamsburg, VA, USA, July 5-10, 2009. Proceedings
A combination with vertical bed movement can position the high-resolution detector optimally at close contact with most part of the animal. The transaxial view of our target PET system is shown in Fig. 1. 2 Spatial Resolution We measure the spatial resolution of the proposed PET system by simulating a point source on a uniform disc phantom as shown in Fig. 1(b). 1 mm in diameter was positioned at various radial offsets along five line segmentations: OA, OB, OC, OD, and OE. The activity ratio between the point source and the background disc was 100 : 1.
Psys ∈ RN ×M is the system matrix modeling the probability that a photon pair emitted from one voxel is detected by a detector pair in the absence of photon attenuation and detector efficiency variations. High-Resolution Adaptive PET Imaging 29 It includes both the solid angle effect and crystal penetration effect. Here we used a numerical method previously proposed in  which is capable of handling arbitrary geometry. It models the block structure of the PET scanner and takes into account the photon penetration and gaps between detectors.
Thus the lower bound approximation has the advantage of deﬁning a metric cost. Intuitively, for a number of landmarks suﬃciently larger than the intrinsic dimension of the manifold, Clustering of the Human Skeletal Muscle Fibers 19 the distance vector representation will provide a good characterization of the points on the manifold. In order to apply the clustering framework to ﬁber tracts, we will deﬁne a Euclidean structure over the ﬁber domain. For this purpose, we show in the next section how to map the ﬁbers to a Hilbert space and derive corresponding metrics that will provide a local approximation of the geodesic distance.