Preconditioned alternating projection algorithm for solving the penalized-likelihood SPECT reconstruction problem

Li, Si; Zhang, Jiahan; Król, Andrzej; Schmidtlein, C. Ross; Feiglin, David; Xu, Yuesheng

doi:10.1016/j.ejmp.2017.05.001

Cited by 5 publications

(4 citation statements)

References 31 publications

(54 reference statements)

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“…Indeed, similar techniques will also be applied in SPECT, where available count statistics in oncology imaging are often limited, and there is an inability to extend already long scan times. Improved iterative reconstruction models using penalized likelihood [79,80] and improved resolution modelling will help, particularly if they overcome overshoot 'Gibbs' artefacts [81]. Additionally, developments with scatter correction algorithms will help move the technology from subtractive techniques, which are inexact and increase image noise [82] compared to those incorporated into image reconstruction [60], which do not increase image noise.…”

Section: Reconstruction and Correctionsmentioning

confidence: 99%

What scans we will read: imaging instrumentation trends in clinical oncology

et al. 2020

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Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized noninvasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as "data", andthrough the wider adoption of advanced analysis, including machine learning approaches and a "big data" conceptmove to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multidimensional data from multiple sources, which mandate the use of advanced and interactive visualization and

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Section: Reconstruction and Correctionsmentioning

confidence: 99%

What scans we will read: imaging instrumentation trends in clinical oncology

et al. 2020

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“…Pock and Chambolle [39] studied simple diagonal preconditioning matrices for first order primal-dual algorithms in convex optimization. In the field of medical imaging, the expectation-maximization (EM) preconditioner [8,23,26,27] and its improved version [29,43] were designed for the development of fast ECT reconstruction algorithms. Numerical experiments in the works mentioned above demonstrated the effectiveness of these preconditioners for ECT reconstruction problems.…”

Section: Introductionmentioning

confidence: 99%

A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm

Chen¹,

Lu²,

Ma³

et al. 2022

Inverse Problems

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The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Speciﬁcally, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization deﬁned on the CAUG for the resulting illposed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the outperformance of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with quadratic regularization on the CAUG, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid.

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“…El objetivo del presente trabajo es comparar el desempeño de varios métodos modernos de reducción de ruido basados en variación total en el contexto de la SPECT, para su posible implementación en la rutina clínica de la MN. Métodos de este tipo han sido anteriormente utilizados exitosamente en SPECT (12).…”

Section: Introduccionunclassified

Reducción de ruido en imágenes de SPECT utilizando métodos de variación total

Machado

Pérez-Díaz

Orozco-Morales

et al. 2022

Braz J Exp Des Data Anal Inferential Stats

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La reducción del ruido en imágenes de Tomografía Computarizada por Emisión de Fotón Único (SPECT) sigue siendo un campo de investigación en la actualidad. El ruido produce una degradación en la calidad de las imágenes, lo cual puedeafectar la precisión de los diagnósticos médicos. En esta investigación se comparan 11 métodos modernos basados en variación total, para la reducción del ruido en imágenes de SPECT. Se utilizan un corte tomográfico del maniquí de Jaszczak, el cual es procesado por todos los métodos. Para evaluar la calidad de las imágenes procesadas, se emplearon métricas objetivas de calidad de imagen. Se realiza un análisis estadístico no paramétrico de los resultados, y se obtuvo que, si se requiere mantener un alto contraste imagen, se sugiere emplear los métodos FISTA o DCRG-PPXA; en cambio, para mantener la mejor resolución espacial posible, se debe elegir a TVNLR o RTV. Por su parte, los métodos PDHG y GPCL, remueven una alta cantidad de ruido, aportando excelentes valores tanto de relación señal a ruido como de relación contraste a ruido.

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Preconditioned alternating projection algorithm for solving the penalized-likelihood SPECT reconstruction problem

Cited by 5 publications

References 31 publications

What scans we will read: imaging instrumentation trends in clinical oncology

What scans we will read: imaging instrumentation trends in clinical oncology

A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm

Reducción de ruido en imágenes de SPECT utilizando métodos de variación total

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