Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network

Xie, Nuobei; Gong, Kuang; Guo, Ning; Qin, Zhixing; Wu, Zhiyong; Liu, Huafeng; Li, Quanzheng

doi:10.1016/j.neuroimage.2021.118380

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…This indirect method is prone to producing low signal-to-noise ratio parametric images due to the difficulty in modelling noise in the image space [8]. On the other hand, direct methods of reconstruction transform parametric images from the raw projection data in a single step and provide improved noise modelling capabilities [9][10][11]. However, the convergence of algorithms associated with direct methods is computationally intensive and time-consuming, necessitating the need for offline computation.…”

Section: Introductionmentioning

confidence: 99%

ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages

Vashistha,

Moradi,

Hammond

et al. 2024

EJNMMI Res

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Background The indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation. Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework is the use of state-of-the-art arterial input function estimation via temporal arterial blood sampling, which is an invasive procedure, or an additional magnetic resonance imaging (MRI) scan for selecting a region where arterial blood signal can be measured from the PET image. We propose a novel machine learning approach for reconstructing high-quality parametric brain images from histoimages produced from time-of-flight PET data without requiring invasive arterial sampling, an MRI scan, or paired training data from standard field-of-view scanners. Result The proposed is tested on a simulated phantom and five oncological subjects undergoing an 18F-FDG-PET scan of the brain using Siemens Biograph Vision Quadra. Kinetic parameters set in the brain phantom correlated strongly with the estimated parameters (K1, k2 and k3, Pearson correlation coefficient of 0.91, 0.92 and 0.93) and a mean squared error of less than 0.0004. In addition, our method significantly outperforms (p < 0.05, paired t-test) the conventional nonlinear least squares method in terms of contrast-to-noise ratio. At last, the proposed method was found to be 37% faster than the conventional method. Conclusion We proposed a direct non-invasive DL-based reconstruction method and produced high-quality parametric maps of the brain. The use of histoimages holds promising potential for enhancing the estimation of parametric images, an area that has not been extensively explored thus far. The proposed method can be applied to subject-specific dynamic PET data alone.

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Section: Introductionmentioning

confidence: 99%

ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages

Vashistha,

Moradi,

Hammond

et al. 2024

EJNMMI Res

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“…The frame-wise registration could be easily integrated into the parametric analysis routine after image reconstruction and before compartment model fitting, especially in the continuous-bed-motion (CBM) mode [35], without the need of raw data or listmode reconstruction framework. A recent work deployed frame-wise registration in their proposed deep learning framework to estimate motioncorrected direct Patlak images from indirect Patlak images, and was validated on dynamic brain data with rigid motion [36]. Several studies have investigated frame registration mainly on head PET [37], [38], [39], but also have not been implemented on whole-body dynamic human PET data yet.…”

Section: Introductionmentioning

confidence: 99%

Inter-Pass Motion Correction for Whole-Body Dynamic PET and Parametric Imaging

Guo

Chen

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

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Whole-body dynamic FDG-PET imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, interpass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in wholebody dynamic PET. 27 subjects underwent a 90-min whole-body FDG CBM PET scan on a Biograph mCT (Siemens Healthineers), acquiring 9 over-the-heart single-bed passes and subsequently 19 CBM passes (frames). The inter-pass motion correction was executed using non-rigid image registration with multi-resolution, B-spline free-form deformations. The parametric images were then generated by Patlak analysis. The overlaid Patlak slope Ki and y-intercept V b images were visualized to qualitatively evaluate motion impact and correction effect. The normalized weighted mean squared Patlak fitting errors (NFE) were compared in the whole body, head, and hypermetabolic regions of interest (ROI). In Ki images, ROI statistics were collected and malignancy discrimination capacity was estimated by the area under the receiver operating characteristic curve (AUC). After the interpass motion correction was applied, the spatial misalignment appearance between Ki and V b images was successfully reduced. Voxel-wise normalized fitting error maps showed global error reduction after motion correction. The NFE in the whole body (p = 0.0013), head (p = 0.0021), and ROIs (p = 0.0377) significantly decreased. The visual performance of each hypermetabolic ROI in Ki images was enhanced, while 3.59% and 3.67% average absolute percentage changes were observed in mean and maximum Ki values, respectively, across all evaluated ROIs. The estimated mean Ki values had substantial changes with motion correction

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“…With the Patlak plot method, the K i parameter, which is the net uptake rate constant, is used most often. PET Patlak parametric images have been generated based on direct reconstruction using different methods (e.g., the kernel method [ 8 – 10 ], deep image prior with the alternating direction of multipliers method (ADMM) [ 11 – 14 ], the hybrid approach [ 15 ], and a method with only a deep network [ 16 ]). These methods make the reconstruction process much longer when obtaining parametric images, and some methods do not work well for real patient data due to the fact that they conduct training with simulated data.…”

Section: Introductionmentioning

confidence: 99%

Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images

Liang,

Zhou,

Chen

et al. 2023

EJNMMI Phys

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Background Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (Ki) of tissues using graphical methods (such as Patlak plots). Ki is more stable than the standard uptake value and has a good reference value for clinical diagnosis. However, the long scanning time required for obtaining dynamic PET images, usually an hour, makes this method less useful in some ways. There is a tradeoff between the scan durations and the signal-to-noise ratios (SNRs) of Ki images. The purpose of our study is to obtain approximately the same image as that produced by scanning for one hour in just half an hour, improving the SNRs of images obtained by scanning for 30 min and reducing the necessary 1-h scanning time for acquiring dynamic PET images. Methods In this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the Ki parameter image. Results A quantitative analysis showed that the network-generated Ki parameter maps improved the structural similarity index measure and peak SNR by averages of 2.27% and 7.04%, respectively, and decreased the root mean square error (RMSE) by 16.3% compared to those generated with a scan time of 30 min. Conclusions The proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.

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Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network

Cited by 8 publications

References 35 publications

ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages

ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages

Inter-Pass Motion Correction for Whole-Body Dynamic PET and Parametric Imaging

Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images

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