Deep learning-based sinogram extension method for interior computed tomography

Ketola, Juuso; Heino, Helinä; Juntunen, Mikael; Nieminen, Miika T.; Inkinen, Satu I.

doi:10.1117/12.2580886

Cited by 5 publications

(7 citation statements)

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“…In a phantom study, Mawlawi et al 9 adopted the approach proposed by Hsieh et al 17 and showed a maximum variation of 6.3% in PET activity concentration after applying the eFOV algorithm. Deep learning techniques have also achieved great success in the eFOV algorithm, [25][26][27][28] but the high demands on training datasets and computing power make it impossible to apply in all scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…22,23 Models based on DL techniques can generate clinically useful images from raw images, including CT eFOV images. 24 Ketola et al 25 used a U-net convolutional neural network to extend the truncated sinogram (TS) and reconstructed the extended sinogram using filtered back projection (FBP). Fournié et al 26 increased the FOV by linearly extrapolating external channels in the sinogram space and applied U-net in the image space to reduce artifacts in the reconstructed image.…”

Section: Introductionmentioning

confidence: 99%

“…Models based on DL techniques can generate clinically useful images from raw images, including CT eFOV images 24 . Ketola et al 25 . used a U‐net convolutional neural network to extend the truncated sinogram (TS) and reconstructed the extended sinogram using filtered back projection (FBP).…”

Section: Introductionmentioning

confidence: 99%

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Application of geometric shape‐based CT field‐of‐view extension algorithms in an all‐digital positron emission tomography/computed tomography system

Hu,

Li,

Yang

et al. 2023

Medical Physics

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BackgroundComputed tomography (CT)‐based positron emission tomography (PET) attenuation correction (AC) is a commonly used method in PET AC. However, the CT truncation caused by the subject's limbs outside the CT field‐of‐view (FOV) leads to errors in PET AC.PurposeIn order to enhance the quantitative accuracy of PET imaging in the all‐digital DigitMI 930 PET/CT system, we assessed the impact of FOV truncation on its image quality and investigated the effectiveness of geometric shape‐based FOV extension algorithms in this system.MethodsWe implemented two geometric shape‐based FOV extension algorithms. By setting the data from different numbers of detector channels on either side of the sinogram to zero, we simulated various levels of truncation. Specific regions of interest (ROI) were selected, and the mean values of these ROIs were calculated to visually compare the differences between truncated CT, CT extended using the FOV extension algorithms, and the original CT. Furthermore, we conducted statistical analyses on the mean and standard deviation of residual maps between truncated/extended CT and the original CT at different levels of truncation. Subsequently, similar data processing was applied to PET images corrected using original CT and those corrected using simulated truncated and extended CT images. This allowed us to evaluate the influence of FOV truncation on the images produced by the DigitMI 930 PET/CT system and assess the effectiveness of the FOV extension algorithms.ResultsTruncation caused bright artifacts at the CT FOV edge and a slight increase in pixel values within the FOV. When using truncated CT data for PET AC, the PET activity outside the CT FOV decreased, while the extension algorithm effectively reduced these effects. Patient data showed that the activity within the CT FOV decreased by 60% in the truncated image compared to the base image, but this number could be reduced to at least 17.3% after extension.ConclusionThe two geometric shape‐based algorithms effectively eliminate CT truncation artifacts and restore the true distribution of CT shape and PET emission data outside the FOV in the all‐digital DigitMI 930 PET/CT system. These two algorithms can be used as basic solutions for CT FOV extension in all‐digital PET/CT systems.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of geometric shape‐based CT field‐of‐view extension algorithms in an all‐digital positron emission tomography/computed tomography system

Hu,

Li,

Yang

et al. 2023

Medical Physics

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“…This makes our network stand out from pure image-to-image transformations such as FBPConvNet. The DGAN approach includes several improvements to our previous U-Net-based sinogram extrapolation approach [58]. Recently, a method for processing truncated cone-beam CT data with GANs was presented [43].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, as a sinogram extension strategy, we utilized adaptive detruncation, and subsequently reconstructed the extended sinogram with FBP (ES-ADT) [57]. Two DL models were also used as reference methods: Image-to-image transform from truncated FBP reconstruction to full FBP reconstruction with FBPConvNet [35], and U-Net-based sinogram extrapolation followed with FBP reconstruction (ES-UNet) [58]. The same training data were used to train these networks for 200 epochs.…”

Section: Reference Methodsmentioning

confidence: 99%

Generative adversarial networks improve interior computed tomography angiography reconstruction

Ketola

Heino

Juntunen

et al. 2021

Biomed. Phys. Eng. Express

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In interior computed tomography (CT), the x-ray beam is collimated to a limited field-of-view (FOV) (e.g. the volume of the heart) to decrease exposure to adjacent organs, but the resulting image has a severe truncation artifact when reconstructed with traditional filtered back-projection (FBP) type algorithms. In some examinations, such as cardiac or dentomaxillofacial imaging, interior CT could be used to achieve further dose reductions. In this work, we describe a deep learning (DL) method to obtain artifact-free images from interior CT angiography. Our method employs the Pix2Pix generative adversarial network (GAN) in a two-stage process: (1) An extended sinogram is computed from a truncated sinogram with one GAN model, and (2) the FBP reconstruction obtained from that extended sinogram is used as an input to another GAN model that improves the quality of the interior reconstruction. Our double GAN (DGAN) model was trained with 10 000 truncated sinograms simulated from real computed tomography angiography slice images. Truncated sinograms (input) were used with original slice images (target) in training to yield an improved reconstruction (output). DGAN performance was compared with the adaptive de-truncation method, total variation regularization, and two reference DL methods: FBPConvNet, and U-Net-based sinogram extension (ES-UNet). Our DGAN method and ES-UNet yielded the best root-mean-squared error (RMSE) (0.03±0.01), and structural similarity index (SSIM) (0.92±0.02) values, and reference DL methods also yielded good results. Furthermore, we performed an extended FOV analysis by increasing the reconstruction area by 10% and 20%. In both cases, the DGAN approach yielded best results at RMSE (0.03±0.01 and 0.04±0.01 for the 10% and 20% cases, respectively), peak signal-to-noise ratio (PSNR) (30.5±2.6 dB and 28.6±2.6 dB), and SSIM (0.90±0.02 and 0.87±0.02). In conclusion, our method was able to not only reconstruct the interior region with improved image quality, but also extend the reconstructed FOV by 20%.

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Deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications

Ylisiurua,

Sipola,

Nieminen

et al. 2024

Physica Medica

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Deep learning-based sinogram extension method for interior computed tomography

Cited by 5 publications

References 0 publications

Application of geometric shape‐based CT field‐of‐view extension algorithms in an all‐digital positron emission tomography/computed tomography system

Application of geometric shape‐based CT field‐of‐view extension algorithms in an all‐digital positron emission tomography/computed tomography system

Generative adversarial networks improve interior computed tomography angiography reconstruction

Deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications

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