Evidence map›Paper›PMID 30291718›Full record

ArticleMedical physics2018

Infimal convolution-based regularization for SPECT reconstruction.

Jiahan Zhang, Si Li, Andrzej Krol, C Ross Schmidtlein, Edward Lipson, David Feiglin, Yuesheng Xu

Abstract read
In one paragraph

Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jiahan ZhangDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, 27713, USA.
Si LiSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China.
Andrzej KrolDepartment of Radiology, Department of Pharmacology, SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
C Ross SchmidtleinDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Edward LipsonDepartment of Physics, Syracuse University, Syracuse, NY, 13244, USA.
David FeiglinDepartment of Radiology, Department of Pharmacology, SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
Yuesheng XuDepartment of Mathematics and Statistics, Old Dominion University, Norfolk, VA, 23529, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
National Institutes of Health/National Cancer Institute P30 CA008748National Key R&D Program 2016YFB0200602Natural Science Foundation of China 11471013Natural Science Foundation of China 11601537Natural Science Foundation of China 11771464NCI NIH HHS P30 CA008748US National Science Foundation DMS-1522332
6 · The paper itself

Abstract

purposeTotal variation (TV) regularization is efficient in suppressing noise, but is known to suffer from staircase artifacts. The goal of this work was to develop a regularization method using the infimal convolution of the first- and the second-order derivatives to reduce or even prevent staircase artifacts in the reconstructed images, and to investigate if the advantage in noise suppression by this TV-type regularization can be translated into dose reduction.

methodsIn the present work, we introduce the infimal convolution of the first- and the second-order total variation (ICTV) as the regularization term in penalized maximum likelihood reconstruction. The preconditioned alternating projection algorithm (PAPA), previously developed by the authors of this article, was employed to produce the reconstruction. Using Monte Carlo-simulated data, we evaluate noise properties and lesion detectability in the reconstructed images and compare the results with conventional total variation (TV) and clinical EM-based methods with Gaussian post filter (GPF-EM). We also evaluate the quality of ICTV regularized images obtained for lower photon number data, compared with clinically used photon number, to verify the feasibility of radiation-dose reduction to patients by use of the ICTV reconstruction method.

resultsBy comparison with GPF-EM reconstructed images, we have found that the ICTV-PAPA method can achieve a lower background variability level while maintaining the same level of contrast. Images reconstructed by the ICTV-PAPA method with 80,000 counts per view exhibit even higher channelized Hotelling observer (CHO) signal-to-noise ratio (SNR), as compared to images reconstructed by the GPF-EM method with 120,000 counts per view.

conclusionsIn contrast to the TV-PAPA method, the ICTV-PAPA reconstruction method avoids substantial staircase artifacts, while producing reconstructed images with higher CHO SNR and comparable local spatial resolution. Simulation studies indicate that a 33% dose reduction is feasible by switching to the ICTV-PAPA method, compared with the GPF-EM clinical standard.

Indexed as

Tomography, Emission-Computed, Single-PhotonArtifactsHumansImage Processing, Computer-AssistedPhantoms, ImagingSignal-To-Noise Ratiofixed-point proximity methodsinfimal convolutionnoise suppressionpenalized maximum likelihood optimization total variation regularizationSPECT reconstructionstaircase artifact

Identifiers

PMID30291718
PMCPMC6785018

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.