DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Liu, Jiaming; Anirudh, Rushil; Thiagarajan, Jayaraman J.; He, Stewart; Mohan, K. Aditya; Kamilov, Ulugbek S.; Kim, Hyojin

doi:10.1109/iccv51070.2023.00963

Cited by 16 publications

(3 citation statements)

References 68 publications

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“…We reconstructed LA-CBCTs using five different methods (FDK, ADMM-TV, DOLCE, DiffusionMBIR, and PFGDM), based on the limited-angle acquisition scenarios simulated in section 2.3.1 . We adapted DOLCE (Liu et al 2023 ) and DiffusionMBIR (Chung et al 2023 ) in this study by modifying the projection geometry from parallel beam to cone beam. Compared with DiffusionMBIR and PFGDM, different DOLCE models need to be retrained for different limited-angle acquisition geometries.…”

Section: Methodsmentioning

confidence: 99%

“…It provides a powerful way to learn population-based imaging statistics and distributions and can serve as strong imaging priors in medical imaging studies (Song et al 2021 , Chung and Ye 2022 ). In terms of limited-angle CT (LA-CT) reconstruction, DiffusionMBIR (Chung et al 2023 ) and DOLCE (Liu et al 2023 ) are among the latest diffusion model-based methods that showed superior performance over the previous methods. By embedding the diffusion model as a denoiser in an alternating direction method of multipliers (ADMMs)-based iterative reconstruction framework, DiffusionMBIR uses the prior distribution learned in the diffusion model to enhance the LA-CT image quality.…”

Section: Introductionmentioning

confidence: 99%

“…By embedding the diffusion model as a denoiser in an alternating direction method of multipliers (ADMMs)-based iterative reconstruction framework, DiffusionMBIR uses the prior distribution learned in the diffusion model to enhance the LA-CT image quality. DOLCE (Liu et al 2023 ), on the other hand, similarly used the diffusion model to regularize an iterative model-based reconstruction, while conditioning the diffusion model with additional LA-CTs reconstructed by regularized least squares or FBP techniques. However, both the DOLCE technique and the DiffusionMBIR technique were developed for CT reconstruction using parallel beam geometries.…”

Section: Introductionmentioning

confidence: 99%

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Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

Xie,

Shao,

et al. 2024

Phys. Med. Biol.

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Objective. Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is highly desired for improved imaging efficiency, dose reduction, and better mechanical clearance. LA-CBCT reconstruction, however, suffers from severe under-sampling artifacts, making it a highly ill-posed inverse problem. Diffusion models can generate data/images by reversing a data-noising process through learned data distributions; and can be incorporated as a denoiser/regularizer in LA-CBCT reconstruction. In this study, we developed a diffusion model-based framework, prior frequency-guided diffusion model (PFGDM), for robust and structure-preserving LA-CBCT reconstruction. Approach. PFGDM uses a conditioned diffusion model as a regularizer for LA-CBCT reconstruction, and the condition is based on high-frequency information extracted from patient-specific prior CT scans which provides a strong anatomical prior for LA-CBCT reconstruction. Specifically, we developed two variants of PFGDM (PFGDM-A and PFGDM-B) with different conditioning schemes. PFGDM-A applies the high-frequency CT information condition until a pre-optimized iteration step, and drops it afterwards to enable both similar and differing CT/CBCT anatomies to be reconstructed. PFGDM-B, on the other hand, continuously applies the prior CT information condition in every reconstruction step, while with a decaying mechanism, to gradually phase out the reconstruction guidance from the prior CT scans. The two variants of PFGDM were tested and compared with current available LA-CBCT reconstruction solutions, via metrics including peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Main results. PFGDM outperformed all traditional and diffusion model-based methods. The mean(s.d.) PSNR/SSIM were 27.97(3.10)/0.949(0.027), 26.63(2.79)/0.937(0.029), and 23.81(2.25)/0.896(0.036) for PFGDM-A, and 28.20(1.28)/0.954(0.011), 26.68(1.04)/0.941(0.014), and 23.72(1.19)/0.894(0.034) for PFGDM-B, based on 120°, 90°, and 30° orthogonal-view scan angles respectively. In contrast, the PSNR/SSIM was 19.61(2.47)/0.807(0.048) for 30° for DiffusionMBIR, a diffusion-based method without prior CT conditioning. Significance. PFGDM reconstructs high-quality LA-CBCTs under very-limited gantry angles, allowing faster and more flexible CBCT scans with dose reductions.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

Xie,

Shao,

et al. 2024

Phys. Med. Biol.

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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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Unsupervised Bayesian generation of synthetic CT from CBCT using patient‐specific score‐based prior

Peng,

Gao,

Chang

et al. 2024

Medical Physics

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BackgroundCone‐beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image‐guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit (HU) values hinder their application in quantitative tasks such as target and organ segmentations and dose calculation. Therefore, acquiring CT‐quality images from the CBCT scans is essential to implement online ART in clinical settings.PurposeThis work aims to develop an unsupervised learning method using the patient‐specific diffusion model for CBCT‐based synthetic CT (sCT) generation to improve the image quality of CBCT.MethodsThe proposed method is in an unsupervised framework that utilizes a patient‐specific score‐based model as the image prior alongside a customized total variation (TV) regularization to enforce coherence across different transverse slices. The score‐based model is unconditionally trained using the same patient's planning CT (pCT) images to characterize the manifold of CT‐quality images and capture the unique anatomical information of the specific patient. The efficacy of the proposed method was assessed on images from anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and lung cancer. The performance of the proposed CBCT correction method was evaluated using quantitative metrics, including mean absolute error (MAE), non‐uniformity (NU), and structural similarity index measure (SSIM). Additionally, the proposed algorithm was benchmarked against other unsupervised learning‐based CBCT correction algorithms.ResultsThe proposed method significantly reduced various kinds of CBCT artifacts in the studies of H&N, pancreatic, and lung cancer patients. In the lung stereotactic body radiation therapy (SBRT) patient study, the MAE, NU, and SSIM were improved from 47 HU, 45 HU, and 0.58 in the original CBCT images to 13 HU, 14 dB, and 0.67 in the generated sCT images. Compared to other unsupervised learning‐based algorithms, the proposed method demonstrated superior performance in artifact reduction.ConclusionsThe proposed unsupervised method can generate sCT from CBCT with reduced artifacts and precise HU values, enabling CBCT‐guided segmentation and replanning for online ART.

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DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Cited by 16 publications

References 68 publications

Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

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

Unsupervised Bayesian generation of synthetic CT from CBCT using patient‐specific score‐based prior

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