Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

Wang, Yan; Zhang, Pei; An, Le; Ma, Guoqing; Kang, Jiayin; Shi, Feng; Wu, Xi; Zhou, Jiliu; Lalush, David S.; Lin, Weili; Shen, Dinggang

doi:10.1088/0031-9155/61/2/791

Cited by 74 publications

(41 citation statements)

References 39 publications

(10 reference statements)

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“…The estimation often requires the input of the tracer-free MRI scan and relies on the sparse learning technique. For example, in [14], the mapping-based sparse representation (m-SR) was adopted for SPET image reconstruction. To speed up the process, the patch-selection-based dictionary construction method was used to build a relatively small but representative dictionary, which can heavily reduce the processing time.…”

Section: Related Workmentioning

confidence: 99%

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Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI

et al. 2017

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Positron emission tomography (PET) is an essential technique in many clinical applications such as tumor detection and brain disorder diagnosis. In order to obtain high-quality PET images, a standard-dose radioactive tracer is needed, which inevitably causes the risk of radiation exposure damage. For reducing the patient’s exposure to radiation and maintaining the high quality of PET images, in this paper, we propose a deep learning architecture to estimate the high-quality standard-dose PET (SPET) image from the combination of the low-quality low-dose PET (LPET) image and the accompanying T1-weighted acquisition from magnetic resonance imaging (MRI). Specifically, we adapt the convolutional neural network (CNN) to account for the two channel inputs of LPET and T1, and directly learn the end-to-end mapping between the inputs and the SPET output. Then, we integrate multiple CNN modules following the auto-context strategy, such that the tentatively estimated SPET of an early CNN can be iteratively refined by subsequent CNNs. Validations on real human brain PET/MRI data show that our proposed method can provide competitive estimation quality of the PET images, compared to the state-of-the-art methods. Meanwhile, our method is highly efficient to test on a new subject, e.g., spending ~2 seconds for estimating an entire SPET image in contrast to ~16 minutes by the state-of-the-art method. The results above demonstrate the potential of our method in real clinical applications.

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Section: Related Workmentioning

confidence: 99%

“…Multi-modality data has been proven to provide complementary and effective information for increasing the quality of each single modality [13, 14]. It is shown in the literature that the anatomical or the structural information (e.g., from CT or MRI [15, 16]) contributes to better SPET image quality.…”

Section: Introductionmentioning

confidence: 99%

Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI

et al. 2017

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“…We compare our method with the following state-of-the-art multi-modality based PET estimation methods: (1) mapping based sparse representation method (m-SR) [2], (2) tripled dictionary learning method (t-DL) [4], (3) multi-level CCA method (m-CCA) [5], and (4) auto-context CNN method [3]. The averaged PSNR are given in Fig.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis

Wang

Zhou

Wang

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Positron emission topography (PET) has been substantially used in recent years. To minimize the potential health risks caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality full-dose PET image from the low-dose one to reduce the radiation exposure while maintaining the image quality. In this paper, we propose a locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the full-dose PET image from both the low-dose one and the accompanying T1-weighted MRI to incorporate anatomical information for better PET image synthesis. This paper has the following contributions. First, we propose a new mechanism to fuse multi-modality information in deep neural networks. Different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolute the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not appropriate. To address this issue, we propose a method that is locality adaptive for multimodality fusion. Second, to learn this locality adaptive fusion, we utilize 1 × 1 × 1 kernel so that the number of additional parameters incurred by our method is kept minimum. This also naturally produces a fused image which acts as a pseudo input for the subsequent learning stages. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in an end-to-end trained 3D conditional GANs model developed by us. Our 3D GANs model generates high quality PET images by employing large-sized image patches and hierarchical features. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.

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“…Post-reconstruction methods that use image filtering or sparse methods to predict standard-dose PET from low-dose PET 11 have also succeeded in denoising PET images. Common image filtering techniques, such as nonlocal means and block matching, are well established in the field.…”

Section: Introductionmentioning

confidence: 99%

Full‐count PET recovery from low‐count image using a dilated convolutional neural network

et al. 2020

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Purpose: Positron emission tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using a novel dilated convolutional neural network (CNN) to recover full-count images from low-count images. Methods: We adopted similar hierarchical structures as the conventional U-Net and incorporated dilated kernels in each convolution to allow the network to observe larger, more robust features within the image without the requirement of downsampling and upsampling internal representations. Our dNet was trained alongside a U-Net for comparison. Both models were evaluated using a leaveone-out cross-validation procedure on a dataset of 35 subjects (~3500 slabs), which were obtained from an ongoing 18 F-Fluorodeoxyglucose (FDG) study. Low-count PET data (10% count) were generated by randomly selecting one-tenth of all events in the associated listmode file. Analysis was done on the static image from the last 10 minutes of emission data. Both low-count PET and full-count PET were reconstructed using ordered subset expectation maximization (OSEM). Objective image quality metrics, including mean absolute percent error (MAPE), peak signal-to-noise ratio (PSNR), and structural similarity index metric (SSIM), were used to analyze the deep learning methods. Both deep learning methods were further compared to a traditional Gaussian filtering method. Further, region of interest (ROI) quantitative analysis was also used to compare U-Net and dNet architectures. Results: Both the U-Net and our proposed network were successfully trained to synthesize fullcount PET images from the generated low-count PET images. Compared to low-count PET and Gaussian filtering, both deep learning methods improved MAPE, PSNR, and SSIM. Our dNet also systematically outperformed U-Net on all three metrics (MAPE: 4.99 AE 0.68 vs 5.31 AE 0.76, P < 0.01; PSNR: 31.55 AE 1.31 dB vs 31.05 AE 1.39, P < 0.01; SSIM: 0.9513 AE 0.0154 vs 0.9447 AE 0.0178, P < 0.01). ROI quantification showed greater quantitative improvements using dNet over U-Net. Conclusion: This study proposed a novel approach of using dilated convolutions for recovering fullcount PET images from low-count PET images.

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Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

Cited by 74 publications

References 39 publications

Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI

Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI

Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis

Full‐count PET recovery from low‐count image using a dilated convolutional neural network

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