Comput Math Methods Med - 3D alternating direction TV-based cone-beam CT reconstruction with efficient GPU implementation.

Tópicos

{ algorithm(1844) comput(1787) effici(935) }
{ method(984) reconstruct(947) comput(926) }
{ problem(2511) optim(1539) algorithm(950) }
{ result(1111) use(1088) new(759) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
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{ imag(2830) propos(1344) filter(1198) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
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{ control(1307) perform(991) simul(935) }
{ general(901) number(790) one(736) }
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Resumo

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of 256 ? 256 ? 256 and 36 projections of the size of 512 ? 512.

Resumo Limpo

iter imag reconstruct iir sparsityexploit method total variat tv minim claim potenti larg reduct sampl requir howev comput complex becom heavi burden especi d reconstruct situat order improv perform iter reconstruct effici iir algorithm conebeam comput tomographi cbct gpu implement propos paper first place algorithm base altern direct total variat use local linear proxim techniqu propos cbct reconstruct appli proxim techniqu avoid horribl pseudoinvers comput big matrix make propos algorithm applic effici cbct imag iter algorithm simpl converg simul real ct data reconstruct result indic propos algorithm fast accur gpu implement show excel acceler ratio compar cpu comput without lose numer accuraci runtim new d algorithm second per loop imag size project size

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