Prediction of standard‐dose brain PET image by using MRI and low‐dose brain [<sup>18</sup>F]FDG PET images

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

doi:10.1118/1.4928400

Cited by 57 publications

(39 citation statements)

References 49 publications

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“…In our approach, the estimation uses the sparse coefficients learned in the common space, which has shown to be more effective through experiments using both image quality and clinical quantification measures. Compared to the results in [18], with the same data and experimental settings, superior performance is achieved by our method. Furthermore, only T1-weighted MRI was used in [18], while in our method, multi-modal MRI can be adaptively selected and utilized for improved estimation as compared to using only T1.…”

Section: Introductionmentioning

confidence: 69%

“…Compared to the results in [18], with the same data and experimental settings, superior performance is achieved by our method. Furthermore, only T1-weighted MRI was used in [18], while in our method, multi-modal MRI can be adaptively selected and utilized for improved estimation as compared to using only T1.…”

Section: Introductionmentioning

confidence: 69%

“…This actually brings the motivation of our method, in which we aim to estimate standard-dose alike PET images from L-PET images, which cannot be achieved by simple post-processing operations such as denoising. To our best knowledge, very few methods attempt to directly estimate the S-PET image from an LPET image, for example, using regression forest [17, 18]. Specifically, in [17] and [18], a regression forest can be trained to estimate a voxel value in an S-PET image, with L-PET voxel values in the neighborhood as input to the RF.…”

Section: Introductionmentioning

confidence: 99%

“…To our best knowledge, very few methods attempt to directly estimate the S-PET image from an LPET image, for example, using regression forest [17, 18]. Specifically, in [17] and [18], a regression forest can be trained to estimate a voxel value in an S-PET image, with L-PET voxel values in the neighborhood as input to the RF. The quality of the estimated S-PET images can be further improved by incremental refinement.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to [17, 18], in which the PET estimation is formulated as a regression problem, our approach tackles it as a sparse representation problem. The sparse representation is computed in an iteratively-refined common space for L-PET and S-PET images.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Zhang

Adeli

et al. 2016

IEEE Trans. on Image Process.

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Positron emission tomography (PET) images are widely used in many clinical applications such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both standard-dose and low-dose PET data into a common space and then performing patch based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level Canonical Correlation Analysis (mCCA) scheme to solve this problem. Specifically, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.

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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Zhang

Adeli

et al. 2016

IEEE Trans. on Image Process.

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Longitudinal amyloid and tau PET imaging in Alzheimer's disease: A systematic review of methodologies and factors affecting quantification

Bollack

Pemberton

Collij

et al. 2023

Alzheimer's & Dementia

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Deposition of amyloid and tau pathology can be quantified in vivo using positron emission tomography (PET). Accurate longitudinal measurements of accumulation from these images are critical for characterizing the start and spread of the disease. However, these measurements are challenging; precision and accuracy can be affected substantially by various sources of errors and variability. This review, supported by a systematic search of the literature, summarizes the current design and methodologies of longitudinal PET studies. Intrinsic, biological causes of variability of the Alzheimer's disease (AD) protein load over time are then detailed. Technical factors contributing to longitudinal PET measurement uncertainty are highlighted, followed by suggestions for mitigating these factors, including possible techniques that leverage shared information between serial scans. Controlling for intrinsic variability and reducing measurement uncertainty in longitudinal PET pipelines will provide more accurate and precise markers of disease evolution, improve clinical trial design, and aid therapy response monitoring.

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Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Huang

Zhou

et al. 2023

Interdisciplinary Medicine

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Many deep learning methods have been proposed to improve the quality of low‐dose PET images (LPET), which usually construct end‐to‐end networks with certain radiation dose inputs. However, these approaches have omitted the noise disparity in PET images, which may differ among manufacturers or populations. Therefore, we tend to exploit these noise differences among PET images to achieve adaptive restoration. We proposed a 3D noise level‐guided PET restoration network for LPET including (1) adaptive noise level‐aware subnetwork and (2) LPET restoration subnetwork. The first subnetwork aims to predict the noise level of the given LPET, while the second subnetwork treats the estimated noise level as a priori information to guide the restoration process from LPET to standard‐dose PET images. Experiments were performed on real human head and neck datasets while the peak signal‐to‐noise ratio and structural similarity index measure were used to evaluate LPET recovery performance. Moreover, we also compared the proposed network with several deep‐learning approaches. Experimental results demonstrate that our network with dual‐stage design can perform adaptive restoration for LPET, yielding better visual and quantitative results. In future work, we attempt to apply our method to other imaging tasks and adapt it for clinical practice.

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Prediction of standard‐dose brain PET image by using MRI and low‐dose brain [¹⁸F]FDG PET images

Cited by 57 publications

References 49 publications

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Longitudinal amyloid and tau PET imaging in Alzheimer's disease: A systematic review of methodologies and factors affecting quantification

Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

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Prediction of standard‐dose brain PET image by using MRI and low‐dose brain [18F]FDG PET images

Cited by 57 publications

References 49 publications

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Longitudinal amyloid and tau PET imaging in Alzheimer's disease: A systematic review of methodologies and factors affecting quantification

Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

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Prediction of standard‐dose brain PET image by using MRI and low‐dose brain [¹⁸F]FDG PET images