نتایج جستجو برای: ordered subsets expectation maximization
تعداد نتایج: 139368 فیلتر نتایج به سال:
あらまし 本論文では,複数の楽器音が混在したモノラルの音楽音響信号に対して,メロディーとベースの音 高(基本周波数)を推定する手法を提案する.従来の音高推定手法や音源分離手法は,たかだか三つの音の混合音 しか扱うことができず,市販の CDによるジャズやポピュラー音楽の音響信号には有効に機能しなかった.本手 法は,混合音下で安定に抽出できない基本周波数成分には依存せず,意図的に制限した周波数帯域(メロディー は中高域,ベースは低域)にある高調波成分が支持する最も優勢な音高を求める.その際,音源数を仮定せずに あらゆる音高の高調波構造が混在しているとみなして混合音をモデル化し,EM(Expectation-Maximization) アルゴリズムにより各高調波構造が相対的にどれくらい優勢かを推定する.更に,マルチエージェントモデルを 導入し,各エージェントが音高の時間的な軌跡を追跡するこ...
We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing data. A bounding and maximization process is given to speci cally optimize conditional likelihood instead of the usual joint likelihood. We apply the method to conditioned mixture models and use bounding techniques to ...
A maximum likelihood approach to emission image reconstruction from projections. Maximum likelihood from incomplete data via the EM algorithm. " A theoretical study of some maximum likelihood algorithms for emission and transmission tomography. Attenuation compensation of cone beam SPECT images using maximum likelihood reconstruction. Three-dimensional SPECT reconstruction of combined cone beam...
Iterative statistical reconstruction methods are becoming the standard in positron emission tomography (PET). Conventional maximum-likelihood expectation-maximization (MLEM) and ordered-subsets (OSEM) algorithms act on data which has been pre-processed into corrected, evenly-spaced histograms; however, such pre-processing corrupts the Poisson statistics. Recent advances have incorporated attenu...
Most CT reconstruction system x-ray computed tomography (CT) is a well established visualization technique in medicine and nondestructive testing. However, since CT scanning requires sampling of radiographic projections from different viewing angles, common CT systems with mechanically moving parts are too slow for dynamic imaging, for instance of multiphase flows or live animals. A large numbe...
The main objective of this work is the development and evaluation of iterative image reconstruction (IIR) methods for multitracer dynamic positron emission tomography (mdPET) studies. The ordered subsets (OS) technique applied for the acceleration of the maximum likelihood expectation maximization (ML-EM) IIR algorithm, is here extended to include the weighted-least squares (WLS), image space r...
List-mode processing is an efficient way of dealing with the sparse nature of PET data sets, and is the processing method of choice for time-of-flight (ToF) PET. We present a novel method of computing line projection operations required for list-mode ordered subsets expectation maximization (OSEM) for fully 3-D PET image reconstruction on a graphics processing unit (GPU) using the compute unifi...
PURPOSE In order to locally validate the technique, a retrospective review of a cohort of randomly selected single-photon emission computed tomography (SPECT) bone scans reconstructed with ordered subsets expectation maximization (OSEM) and Evolution for Bone was undertaken. MATERIALS AND METHODS Thirty consecutive bone SPECT patient data sets (17 spine, nine pelvis, and four spine and pelvis...
Quantitative positron emission tomography (PET) using statistical techniques requires: d̂flk..»;„-.IK:. system geometric model that represents the probability of detecting an emission from each impgf. , pixel at each detector-pair, and (b) an iterative algorithm that reconstructs image as quaatitativfi , tt. measurements of radiotracer distribution in vivo. Conventional implementations of iterat...
We implemented a fully-3D ordered subsets expectation maximization (OSEM) algorithm with compensation of attenuation, distance-dependent blurring (DDB) and sensitivity modeling for SPECT performed with cone-beam collimator (CBC). The experimentally obtained detector response to point sources across FOV was fitted to a twodimensional Gauss function with its width (FWHM) varied linearly with the ...
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