PET-enabled dual-energy CT: image reconstruction and a proof-of-concept computer simulation study

Wang, Guobao

doi:10.1088/1361-6560/abc5ca

Cited by 11 publications

(20 citation statements)

References 56 publications

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“…However, the intensity-based features do not always provide satisfactory results. As shown in [ 4 ] and later in this paper, the reconstructed GCT image by such a method suffers from artefacts.…”

Section: Introductionmentioning

confidence: 89%

“…The GCT estimate by standard MLAA is commonly noisy due to the limited counting statistics of PET emission data. To suppress noise, the kernel MLAA approach [4] incorporates the X-ray CT image as a priori information to guide the GCT reconstruction in the MLAA. It describes the intensity of the GCT μ j in pixel j as a linear representation in a transformed feature space μj=wTbold-italicϕfalse( fjfalse), where f j is the data point of pixel j that is extracted from x and ϕ ( f j ) is a mapping function that transforms the low-dimensional data point f j to a high-dimensional feature vector.…”

Section: Pet-enabled Dual-energy Ctmentioning

confidence: 99%

“…Substituting equation (2.9) into the MLAA formulation in equation (2.4) gives the following kernel MLAA optimization formulation, bold-italicλfalse^,bold-italicαfalse^=arg maxbold-italicλ≥0,bold-italicα≥0⁡L(yfalse|λ,Kα). The detail of the kernel MLAA algorithm is provided in [4]. Once bold-italicαfalse^ is obtained, the final estimate of the GCT image is obtained by bold-italicμfalse^=bold-italicKbold-italicαfalse^.…”

Section: Pet-enabled Dual-energy Ctmentioning

confidence: 99%

“…We have proposed a new dual-energy CT imaging method that is enabled using a standard time-of-flight PET/CT scan without change of scanner hardware or adding additional radiation dose or scan time [ 3 , 4 ]. Instead of using two different X-ray energies as commonly used by conventional DECT, the PET-enabled dual-energy CT method combines a radiotracer annihilation-generated high-energy ‘ γ -ray CT (GCT)’ at 511 keV with the already-available low-energy X-ray CT (usually ≤140 keV) to produce a pair of dual-energy CT images on PET/CT for multi-material decomposition.…”

Section: Introductionmentioning

confidence: 99%

“…While the noise would not compromise the performance of MLAA for PET attenuation correction, it may affect the quantitative accuracy of GCT for multi-material decomposition. To suppress noise, the kernel MLAA approach [ 4 ] has been developed by use of X-ray CT as image prior through a kernel framework and has demonstrated substantial improvements over standard MLAA.…”

Section: Introductionmentioning

confidence: 99%

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Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT

Wang

2021

Phil. Trans. R. Soc. A.

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Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PET-enabled dual-energy CT combines a low-energy X-ray CT image with a high-energy γ -ray CT (GCT) image reconstructed from time-of-flight PET emission data to enable dual-energy CT material decomposition on a PET/CT scanner. The maximum-likelihood attenuation and activity (MLAA) algorithm has been used for GCT reconstruction but suffers from noise. Kernel MLAA exploits an X-ray CT image prior through the kernel framework to guide GCT reconstruction and has demonstrated substantial improvements in noise suppression. However, similar to other kernel methods for image reconstruction, the existing kernel MLAA uses image intensity-based features to construct the kernel representation, which is not always robust and may lead to suboptimal reconstruction with artefacts. In this paper, we propose a modified kernel method by using an autoencoder convolutional neural network (CNN) to extract an intrinsic feature set from the X-ray CT image prior. A computer simulation study was conducted to compare the autoencoder CNN-derived feature representation with raw image patches for evaluation of kernel MLAA for GCT image reconstruction and dual-energy multi-material decomposition. The results show that the autoencoder kernel MLAA method can achieve a significant image quality improvement for GCT and material decomposition as compared to the existing kernel MLAA algorithm. A weakness of the proposed method is its potential over-smoothness in a bone region, indicating the importance of further optimization in future work. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

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Section: Introductionmentioning

confidence: 89%

Section: Pet-enabled Dual-energy Ctmentioning

confidence: 99%

Section: Pet-enabled Dual-energy Ctmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT

Wang

2021

Phil. Trans. R. Soc. A.

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Dose distributions of proton therapy plans are robust against lowering the resolution of CTs combined with increasing noise

Frensch,

Bäcker,

Jentzen

et al. 2024

Medical Physics

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BackgroundTreatment planning in radiation therapy (RT) is performed on image sets acquired with commercial x‐ray computed tomography (CT) scanners. Considering an increased frequency of verification scans for adaptive RT and the advent of alternatives to x‐ray CTs, there is a need to review the requirements for image sets used in RT planning.PurposeThis study aims to derive the required image quality (IQ) for the computation of the dose distribution in proton therapy (PT) regarding spatial resolution and the combination of spatial resolution and noise. The knowledge gained is used to explore the potential for dose reduction in tomography‐guided PT.MethodsMathematical considerations indicate that the required spatial resolution for dose computation is on the scale of the set‐up margins fed into the robust optimization. This hypothesis was tested by processing retrospectively 12 clinical PT cases, which reflect a variety of tumor localizations. Image sets were low‐pass filtered and were made noisy in a generic manner. Dose distributions on the modified CT scans were computed with a Monte‐Carlo dose engine. The similarity of these dose distributions with clinical ones was quantified with the gamma‐index (1 mm/1%). The potential reduction of the x‐ray exposure compared to the planning CT scan was estimated.ResultsDose distributions within the irradiated volume were robust against low‐pass filtering of the CTs with kernels up to a full‐width‐at‐half‐maximum of 4 mm, that is, the gamma pass rate (1 mm/1%) was 98%. The limit of the filter width was 6 mm for brain tumors and 8 mm for targets in the abdomen. These pass rates remained approximately unchanged if a limited amount of noise was added to the CT image sets. The estimated potential reductions of the x‐ray exposure were at least a factor of 20.ConclusionsThe requirements on IQ in terms of spatial resolution in combination with noise for computing the dose in PT are clearly lower than the IQ of current clinical planning. The results apply, for example, to ultra‐low dose x‐ray CTs, proton CTs with coarse spatial detection, and attenuation images from the joint reconstruction of time‐of‐flight PET scans.

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