World Wide Web interface for advanced SPECT reconstruction algorithms implemented on a remote massively parallel computer

Formiconi, Andreas Robert; Passeri, Alessandro; Guelfi, Maria Renza; Masoni, Marco; Pupi, Alberto; Meldolesi, U; Malfetti, Paolo; Calori, Luigi; Guidazzoli, Antonella

doi:10.1016/s1386-5056(97)00089-0

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Although the implementation did not indicate a linear scalability, the speedup achieved was 64Â, relative to an optimal programmed implementation to be executed in a reduced instruction set computing (RISC) architecture (64 Â 64 processor). Formiconi et al [28] also presented a parallel implementation of the EM algorithm; however, their approach was combined with ML estimates and applied in order to reconstruct images from SPECT data. The authors designed their implementation on the basis of a multiple instruction, multiple data stream (MIMD) parallel programming model and used a World Wide Web (WWW) interface.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…The area of medical image processing and analysis has contributed to significant medical advances [7,23,50,81,83,88,101] by integrating systems and techniques that support more efficient clinical diagnosis. These systems and techniques are based on images acquired by different imaging modalities such as, endoscopy [52], X-ray [88], microscopy [47,68], computed tomography (CT) [26,57], optical coherence tomography (OCT) [67], magnetic resonance (MR) [2,15], functional magnetic resonance (fMR) [3,97], magnetic resonance elastography (MRE) [20], positron emission tomography (PET) [17,42,43], single photon emission computed tomography (SPECT) [28], and 3D ultrasound computer tomography (USCT) [7].…”

Section: Introductionmentioning

confidence: 99%

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Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Gulo

Sementille

Tavares

2017

J Real-Time Image Proc

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Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

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Section: Image Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Gulo

Sementille

Tavares

2017

J Real-Time Image Proc

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“…Interactive applications of the Internet for medical imaging education have primarily been for diagnostic or visualization purposes [19][20][21][22][23], for medical image database development [24][25][26], and for developing radiologists' programming skills [27,28]. The main objective of these developments has been to describe medical image quality, the Internet itself, and its capabilities in radiological or other diagnostic education.…”

Section: Introductionmentioning

confidence: 99%

An online interactive simulation system for medical imaging education

Dikshit

et al. 2005

Computerized Medical Imaging and Graphics

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“…Even if OSEM now represents one of the most popular iterative algorithms used in SPECT imaging, according to some phantom and simulation studies (Pupi et al, 1990;Boccacci et al, 1999) and experience in the clinical setting (Formiconi et al, 1997;Nobili et al, 1998;Rodriguez et al, 2000), the preconditioned form of WLS-CG (WLS-PCG) proposed in the RECLBL library (Huesman et al, 1977) is able to generate satisfactory reconstructions that are as accurate as those offered by ML-EM and OSEM, provided that the noise level is not dramatically high. As far as the choice of the optimal iteration number is concerned, the simulation study by Boccacci et al (1999) performed on the basis of a 7 Mcount acquisition gives about 9 iterations in the case of sequential 2D reconstructions, and about 15 iterations for fully 3D reconstructions.…”

Section: Introductionmentioning

confidence: 99%

Application of Krylov subspaces to SPECT imaging

Calvini¹,

Bertero

2002

Int J Imaging Syst Tech

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ABSTRACT:The application of the conjugate gradient (CG) algorithm to the problem of data reconstruction in SPECT imaging indicates that most of the useful information is already contained in Krylov subspaces of small dimension, ranging from 9 (two-dimensional case) to 15 (three-dimensional case). On this basis, a new, proposed approach can be basically summarized as follows: construction of a basis spanning a Krylov subspace of suitable dimension and projection of the projector-backprojector matrix (a 10 6 ϫ 10 6 matrix in the three-dimensional case) onto such a subspace. In this way, one is led to a problem of low dimensionality, for which regularized solutions can be easily and quickly obtained. The required SPECT activity map is expanded as a linear combination of the basis elements spanning the Krylov subspace and the regularization acts by modifying the coefficients of such an expansion. By means of a suitable graphical interface, the tuning of the regularization parameter(s) can be performed interactively on the basis of the visual inspection of one or some slices cut from a reconstruction.

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World Wide Web interface for advanced SPECT reconstruction algorithms implemented on a remote massively parallel computer

Cited by 10 publications

References 7 publications

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

An online interactive simulation system for medical imaging education

Application of Krylov subspaces to SPECT imaging

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