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

An, Le; Zhang, Pei; Adeli, Ehsan; Wang, Yan; Ma, Guoqing; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

doi:10.1109/tip.2016.2567072

Cited by 53 publications

(39 citation statements)

References 43 publications

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“…We also compare our method with state-of-the-art MCCA method [20], which has achieved the best performance in the literature. The MCCA method, which belongs to the category of patch-based sparse learning, adopts the data-driven scheme and can iteratively refine the estimation results of the SPET images.…”

Section: Resultsmentioning

confidence: 99%

“…It also allows a certain modality to be missing, thus including huge clinical data for training. Recently, An et al [20] proposed the data-driven multilevel canonical correlation analysis (MCCA) scheme to map the SPET and the LPET image data into a common space, where the patch-based sparse representation was then utilized to generate the coupled LPET and SPET dictionaries. These sparse-learning-based methods consist of several steps generally, including patch extraction, encoding, and reconstruction.…”

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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

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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“…In this work, we use the data for 398 subjects in the ADNI dataset. Each subject has an MRI modality and a corresponding PET modality [1,30]. These subjects cover four different prodromal stages (NC, sMCI, pMCI, AD).…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Many clinical applications [35,37], such as tumor detection and brain disease diagnosis [1,21,30,32], require high-quality multimodality data in order to achieve good diagnostic results, since different modalities of a subject provide complementary information. While standardized methods for clinical tests have been developed to collect multi-modality data, there are some practical concerns in the process of obtaining some important and informative modalities.…”

Section: Introductionmentioning

confidence: 99%

Deep Adversarial Learning for Multi-Modality Missing Data Completion

Cai

Wang

Gao

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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114

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Multi-modality data are widely used in clinical applications, such as tumor detection and brain disease diagnosis. Different modalities can usually provide complementary information, which commonly leads to improved performance. However, some modalities are commonly missing for some subjects due to various technical and practical reasons. As a result, multi-modality data are usually incomplete, raising the multi-modality missing data completion problem. In this work, we formulate the problem as a conditional image generation task and propose an encoder-decoder deep neural network to tackle this problem. Specifically, the model takes the existing modality as input and generates the missing modality. By employing an auxiliary adversarial loss, our model is able to generate high-quality missing modality images. At the same time, we propose to incorporate the available category information of subjects in training to enable the model to generate more informative images. We evaluate our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, where positron emission tomography (PET) modalities are missing. Experimental results show that the trained network can generate high-quality PET modalities based on existing magnetic resonance imaging (MRI) modalities, and provide complementary information to improve the detection and tracking of the Alzheimer's disease. Our results also show that the proposed methods generate higher quality images than baseline methods as measured by various image quality statistics. CCS CONCEPTS • Computing methodologies → Neural networks; • Applied computing → Life and medical sciences; Imaging;

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

Cited by 53 publications

References 43 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

Deep Adversarial Learning for Multi-Modality Missing Data Completion

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