Noninvasive Estimation of Macro-Parameters by Deep Learning

Wang, Bo; Ruan, Dengfeng; Liu, Huafeng

doi:10.1109/trpms.2020.2979017

Cited by 11 publications

(12 citation statements)

References 32 publications

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“… , , and are the kinetic parameters reflecting the speed of directional tracer exchanges between different tissue compartments or between plasma and tissue, which are different among ROIs, individuals and tracers. The input functions of F-FDG [ 40 ], C-FMZ [ 41 ] and C-acetate [ 18 ] were generated by different models. By applying Gaussian randomization to the kinetic parameters and parameters of input function, we simulated individual differences in physiological states.…”

Section: Methodsmentioning

confidence: 99%

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

et al. 2023

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Background Simultaneous dual-tracer positron emission tomography (PET) imaging can observe two molecular targets in a single scan, which is conducive to disease diagnosis and tracking. Since the signals emitted by different tracers are the same, it is crucial to separate each single tracer from the mixed signals. The current study proposed a novel deep learning-based method to reconstruct single-tracer activity distributions from the dual-tracer sinogram. Methods We proposed the Multi-task CNN, a three-dimensional convolutional neural network (CNN) based on a framework of multi-task learning. One common encoder extracted features from the dual-tracer dynamic sinogram, followed by two distinct and parallel decoders which reconstructed the single-tracer dynamic images of two tracers separately. The model was evaluated by mean squared error (MSE), multiscale structural similarity (MS-SSIM) index and peak signal-to-noise ratio (PSNR) on simulated data and real animal data, and compared to the filtered back-projection method based on deep learning (FBP-CNN). Results In the simulation experiments, the Multi-task CNN reconstructed single-tracer images with lower MSE, higher MS-SSIM and PSNR than FBP-CNN, and was more robust to the changes in individual difference, tracer combination and scanning protocol. In the experiment of rats with an orthotopic xenograft glioma model, the Multi-task CNN reconstructions also showed higher qualities than FBP-CNN reconstructions. Conclusions The proposed Multi-task CNN could effectively reconstruct the dynamic activity images of two single tracers from the dual-tracer dynamic sinogram, which was potential in the direct reconstruction for real simultaneous dual-tracer PET imaging data in future.

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Section: Methodsmentioning

confidence: 99%

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

et al. 2023

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“…Follow-up work [ 121 ] used a patient-specific neural network trained with a simulated dictionary using parameters in the neighbourhood of parameter estimates derived from the first pass temporal data. Similarly, Wang et al [ 122 ] used a neural network trained with simulated data to directly estimate biokinetic parameters from voxel-wise time-activity curves. Finally, Angelis et al [ 123 ] incorporated stimulus-induced neural activations using the neurotransmitter PET model by Morris et al [ 124 ] into a simulated dictionary and evaluated the ability of a neural network to reproduce the activation signals.…”

Section: Review Of Deep Learning-based Resolution Enhancementmentioning

confidence: 99%

“…Finally, Angelis et al [ 123 ] incorporated stimulus-induced neural activations using the neurotransmitter PET model by Morris et al [ 124 ] into a simulated dictionary and evaluated the ability of a neural network to reproduce the activation signals. The approaches taken by [ 120 – 122 ] produce denoised dynamic PET images which may improve the quality of dynamic information available for clinical decision-making and the approach taken in [ 123 ] extends this to include dynamic changes in tracer kinetics due to an external stimulus. However, considerations must also be made to ensure the simulated dictionary of time-activity curves includes all possibilities which may occur in practice.…”

Section: Review Of Deep Learning-based Resolution Enhancementmentioning

confidence: 99%

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement

Pain

Egan

Chen

2022

Eur J Nucl Med Mol Imaging

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Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.

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“…One solution anchors a PBIF with IDIF information derived from whole-body PET scanning ( Naganawa, 2020 ). Deep learning has also been leveraged to obtain blood-free quantification of [ 18 F]FDG; however, to our knowledge this has yet to be validated with human scans ( Wang et al, 2020 ). Further, machine learning applied to precompiled electronic health record (EHR) data has been combined with SIME of the input function to quantify [ 18 F]FDG without the use of any blood samples ( Roccia, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Source‐to‐Target Automatic Rotating Estimation (STARE) – A publicly‐available, blood‐free quantification approach for PET tracers with irreversible kinetics: Theoretical framework and validation for [18F]FDG

et al. 2022

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Noninvasive Estimation of Macro-Parameters by Deep Learning

Cited by 11 publications

References 32 publications

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement

Source‐to‐Target Automatic Rotating Estimation (STARE) – A publicly‐available, blood‐free quantification approach for PET tracers with irreversible kinetics: Theoretical framework and validation for [18F]FDG

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