An iterative reconstruction using median root prior and anatomical prior from the segmented μ-map for count-limited transmission data in PET imaging

Sakaguchi, Kei; Shinohara, Hiroyuki; Hashimoto, Takeyuki; Yokoi, Takashi; Uno, Kimiichi

doi:10.1007/s12149-007-0098-8

Cited by 7 publications

(5 citation statements)

References 14 publications

(33 reference statements)

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“…In order to avoid noise amplification, two types of priors (median and Gibbs type) [14] were included in the IDM, each with a neighbourhood of 1, which could be assigned different contributing weights. The algorithm for using the prior is illustrated in Additional file 1 [19] by means of a pseudo-code.…”

Section: Methodsmentioning

confidence: 99%

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Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

et al. 2017

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BackgroundAccurate quantification of PET studies depends on the spatial resolution of the PET data. The commonly limited PET resolution results in partial volume effects (PVE). Iterative deconvolution methods (IDM) have been proposed as a means to correct for PVE. IDM improves spatial resolution of PET studies without the need for structural information (e.g. MR scans). On the other hand, deconvolution also increases noise, which results in lower signal-to-noise ratios (SNR). The aim of this study was to implement IDM in combination with HighlY constrained back-PRojection (HYPR) denoising to mitigate poor SNR properties of conventional IDM.MethodsAn anthropomorphic Hoffman brain phantom was filled with an [18F]FDG solution of ~25 kBq mL−1 and scanned for 30 min on a Philips Ingenuity TF PET/CT scanner (Philips, Cleveland, USA) using a dynamic brain protocol with various frame durations ranging from 10 to 300 s. Van Cittert IDM was used for PVC of the scans. In addition, HYPR was used to improve SNR of the dynamic PET images, applying it both before and/or after IDM. The Hoffman phantom dataset was used to optimise IDM parameters (number of iterations, type of algorithm, with/without HYPR) and the order of HYPR implementation based on the best average agreement of measured and actual activity concentrations in the regions. Next, dynamic [11C]flumazenil (five healthy subjects) and [11C]PIB (four healthy subjects and four patients with Alzheimer’s disease) scans were used to assess the impact of IDM with and without HYPR on plasma input-derived distribution volumes (V T) across various regions of the brain.ResultsIn the case of [11C]flumazenil scans, Hypr-IDM-Hypr showed an increase of 5 to 20% in the regional V T whereas a 0 to 10% increase or decrease was seen in the case of [11C]PIB depending on the volume of interest or type of subject (healthy or patient). References for these comparisons were the V Ts from the PVE-uncorrected scans.ConclusionsIDM improved quantitative accuracy of measured activity concentrations. Moreover, the use of IDM in combination with HYPR (Hypr-IDM-Hypr) was able to correct for PVE without increasing noise.Electronic supplementary materialThe online version of this article (doi:10.1186/s13550-017-0284-1) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

“…Unfortunately, these methods also result in noise amplification, resulting in poor signal-to-noise ratios (SNR). Noise control may be achieved by performing spatial filtering during or after reconstruction [14], but this reduces noise at the cost of lower resolution.…”

Section: Introductionmentioning

confidence: 99%

Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

et al. 2017

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“…The algorithm used in this study to develop multi-directional images into voxel data was a simple average of the intensity values from multiple datasets. Other sophisticated algorithms [24,25] also have the potential to improve image quality of 3D reconstructions.…”

Section: Discussionmentioning

confidence: 99%

“…Various sophisticated algorithms have been developed to reconstruct 3D images from multiple plane data [24,25]. In this Figure 3.…”

Section: Reconstructed 3d Images Of Large-sized Branch Structuresmentioning

confidence: 99%

“…Various sophisticated algorithms have been developed to reconstruct 3D images from multiple plane data [24,25]. In this paper, a simple algorithm, that intensity data from six datasets were averaged, was employed to demonstrate the capability of the hardware and the method using the gel cube device for large-scale imaging.…”

Section: Reconstructed 3d Images Of Large-sized Branch Structuresmentioning

confidence: 99%

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Large Scale Imaging by Fine Spatial Alignment of Multi-Scanning Data with Gel Cube Device

2018

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Abstract:In vitro three-dimensional (3D) culturing is considered essential in many biological fields. However, the imaging of developed 3D formations is often difficult, especially if the size of the sample is relatively large. The z-resolution of fluorescent imaging is low using low magnification lenses (4× and 10×) due to large focal depths. This paper describes 3D culture platform enabling large scale 3D imaging by fine spatial alignment of the image dataset obtained from multiple directions. A gel cube device was employed to conduct the multi-scanning and then a self-fluorescent microstructure in a cubic frame allows us spatially align image dataset within a few pixels. By synthesizing data from multiple scans, the platform enables us to visualize millimeter-sized 3D sample structure and individual cellular actin filaments at the same time. Millimeter depth imaging of a developed bronchial tree was achieved with high z-resolution. The device, which is applicable to most microscopy systems, can enhance the image quality without modifying current systems.

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Effective Anatomical Priors for Emission Tomographic Reconstruction

et al. 2015

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An iterative reconstruction using median root prior and anatomical prior from the segmented μ-map for count-limited transmission data in PET imaging

Abstract: The proposed method is effective for reducing propagation of noise from transmission data to emission data without loss of the quantitative accuracy of the PET image.

Cited by 7 publications

References 14 publications

Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

Large Scale Imaging by Fine Spatial Alignment of Multi-Scanning Data with Gel Cube Device

Effective Anatomical Priors for Emission Tomographic Reconstruction

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