3D Unet Generative Adversarial Network for Attenuation Correction of SPECT Images

Nguyen, Trung Thanh; Nguyen, Thanh; Hoang, Minh Dang; Thai, Ha Nguyen; Duc, Thuan Nguyen

doi:10.1109/sigtelcom49868.2020.9199018

Cited by 13 publications

(11 citation statements)

References 17 publications

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“…Regarding attenuation correction in the image domain, Nguyen et al proposed a 3D Unet-GAN network that takes 3D patches (90 × 90 × 28 voxels) of non-AC images as input to directly predict attenuation corrected MPI-SPECT images [100]. The performance of the proposed network was compared with 2D Unet and 3D Unet models, wherein the 3D Unet-GAN model exhibited superior performance with NMAE = 0.034 and mean square error (MSE) = 294.97.…”

Section: Quantitative Imagingmentioning

confidence: 99%

The promise of artificial intelligence and deep learning in PET and SPECT imaging

et al. 2021

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This review sets out to discuss the foremost applications of artificial intelligence (AI), particularly deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. To this end, the underlying limitations/challenges of these imaging modalities are briefly discussed followed by a description of AI-based solutions proposed to address these challenges. This review will focus on mainstream generic fields, including instrumentation, image acquisition/formation, image reconstruction and low-dose/fast scanning, quantitative imaging, image interpretation (computer-aided detection/ diagnosis/prognosis), as well as internal radiation dosimetry. A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided. Finally, the challenges, opportunities, and barriers to full-scale validation and adoption of AI-based solutions for improvement of image quality and quantitative accuracy of PET and SPECT images in the clinic are discussed.

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Section: Quantitative Imagingmentioning

confidence: 99%

The promise of artificial intelligence and deep learning in PET and SPECT imaging

et al. 2021

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“…However, because attenuation map generation requires cross‐modality transformation, one must be aware of potential pitfalls such as misalignment between subjects, field‐of‐view differences between modalities, modality‐specific artifacts, positional differences, and organ displacement during the scan 22 . For myocardial‐perfusion SPECT 23–25 and brain PET, 26 there have been reports of AC images being generated directly from NAC images. These studies were able to achieve highly accurate AC using GAN and CNN‐based networks, and thus suggested that accurate AC could be achieved using deep learning without the need for generating an attenuation map.…”

Section: Introductionmentioning

confidence: 99%

Development of attenuation correction methods using deep learning in brain‐perfusion single‐photon emission computed tomography

et al. 2021

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Purpose Computed tomography (CT)‐based attenuation correction (CTAC) in single‐photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo‐CT images has previously been reported, but it is limited because of cross‐modality transformation, resulting in misalignment and modality‐specific artifacts. This study aimed to develop a deep learning‐based approach using non‐attenuation‐corrected (NAC) images and CTAC‐based images for training to yield AC images in brain‐perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang’s AC (ChangAC). Methods In total, 236 patients who underwent brain‐perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U‐Net (U‐NetAC), respectively. ChangAC, AutoencoderAC, and U‐NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed‐rank sum test and Bland‐Altman analysis. Results U‐NetAC had the highest visual evaluation score. The NMSE results for the U‐NetAC were the lowest, followed by AutoencoderAC and ChangAC (P < 0.001). Bland‐Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30–40% in all brain regions. AutoencoderAC and U‐NetAC produced mean errors of <1% and maximum errors of 3%, respectively. Conclusion New deep learning‐based AC methods for AutoencoderAC and U‐NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U‐NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo‐CT images. To verify our models’ generalizability, external validation is required.

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“…Nguyen et al developed a generative adversarial network (GAN) to simulate AC images from non-AC data with data from 491 patients for training and 112 for testing. 11 The generator network, based on the UNet architecture which is a specialized convolutional autoencoder, simulated AC images while the discriminator network was tasked with differentiating the simulated images from actual AC images. 11 The goal of such architecture is to make the images indistinguishable.…”

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confidence: 99%

“…11 The generator network, based on the UNet architecture which is a specialized convolutional autoencoder, simulated AC images while the discriminator network was tasked with differentiating the simulated images from actual AC images. 11 The goal of such architecture is to make the images indistinguishable. The UNet-GAN achieved structural similarity index of 0.946 compared to true AC images and outperformed UNet alone.…”

mentioning

confidence: 99%

“…The UNet-GAN achieved structural similarity index of 0.946 compared to true AC images and outperformed UNet alone. 11 In this issue, Chen et al present a novel method to incorporate multiple imaging parameters within the reconstruction process. 12 The proposed dual squeeze-and-excitation residual dense network (DuRDN) incorporates gender, body mass index (BMI), and images from 3 scatter windows together with non-AC images in order to predict AC images.…”

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See 1 more Smart Citation

Artificial intelligence-based attenuation correction; closer to clinical reality?

Miller

Slomka

2022

Journal of Nuclear Cardiology

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3D Unet Generative Adversarial Network for Attenuation Correction of SPECT Images

Cited by 13 publications

References 17 publications

The promise of artificial intelligence and deep learning in PET and SPECT imaging

The promise of artificial intelligence and deep learning in PET and SPECT imaging

Development of attenuation correction methods using deep learning in brain‐perfusion single‐photon emission computed tomography

Artificial intelligence-based attenuation correction; closer to clinical reality?

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