By CORNELIUS T LEONDES
This scholarly set of well-harmonized volumes offers quintessential and whole assurance of the fascinating and evolving topic of scientific imaging platforms. prime specialists at the foreign scene take on the newest state-of-the-art recommendations and applied sciences in an in-depth yet eminently transparent and readable approach.Complementing and intersecting each other, every one quantity bargains a finished remedy of important value to the topic parts. The chapters, in flip, tackle issues in a self-contained demeanour with authoritative introductions, precious summaries, and precise reference lists. commonly well-illustrated with figures all through, the 5 volumes as an entire in attaining a distinct intensity and breath of coverage.As a cohesive complete or self reliant of each other, the volumes will be bought as a suite or separately.
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Extra resources for MEDICAL IMAGING SYSTEMS TECHNOLOGY - VOLUME 2: MODALITIES
6). ∆B = b(1 + αz) (x − x)2 + (y − y)2 . (6) We can obtain the target equation for the NMR signal written in Eq. (7) by scanning the quadratic ﬁeld gradient in the x–y plane as in the Fresnel transform imaging technique. ∞ v(x , y ) = P ρ(x, y, z) exp −jγbτ (1 + αz) (x −x)2 + (y −y)2 −∞ dx dy dz, (7) where ρ(x, y, z) represents the 3D-distribution of the spin density in the subject and P is a constant. In order to obtain a signal that approximates the form of Eq. (7), we used four-types of magnetic ﬁelds written as Eqs.
6) We can obtain the target equation for the NMR signal written in Eq. (7) by scanning the quadratic ﬁeld gradient in the x–y plane as in the Fresnel transform imaging technique. ∞ v(x , y ) = P ρ(x, y, z) exp −jγbτ (1 + αz) (x −x)2 + (y −y)2 −∞ dx dy dz, (7) where ρ(x, y, z) represents the 3D-distribution of the spin density in the subject and P is a constant. In order to obtain a signal that approximates the form of Eq. (7), we used four-types of magnetic ﬁelds written as Eqs. (8) to (11) when the phase encoding and read-out directions are set in the x- and y-directions, respectively.
La Rivi` ere 97. B. De Man, J. Nuyts, P. Dupont, G. Marchal and P. Suetens, An iterative maximumlikelihood polychromatic algorithm for CT, IEEE Trans. Med. Imag. 20 (2001) 999– 1008. 98. I. A. Elbakri and J. A. Fessler, Statistical image reconstruction for polyenergetic X-ray computed tomography, IEEE Trans. Med. Imag. 21 (2002) 89–99. 99. J. F. Williamson, B. R. Whiting, J. Benac, R. J. Murphy, G. J. Blaine, J. A. O’Sullivan, D. G. Politte and D. L. Snyder, Prospects for quantitative computed tomography imaging in the presence of foreign metal bodies using statistical image reconstruction, Med.