Objective To propose a dual-domain CBCT reconstruction framework(DualSFR-Net)based on generative projection interpolation to reduce artifacts in sparse-view cone beam computed tomography(CBCT)reconstruction.Methods The proposed method DualSFR-Net consists of a generative projection interpolation module,a domain transformation module,and an image restoration module.The generative projection interpolation module includes a sparse projection interpolation network(SPINet)based on a generative adversarial network and a full-view projection restoration network(FPRNet).SPINet performs projection interpolation to synthesize full-view projection data from the sparse-view projection data,while FPRNet further restores the synthesized full-view projection data.The domain transformation module introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes.The image restoration module includes an image restoration network FIRNet that fine-tunes the domain-transformed images to eliminate residual artifacts and noise.Results Validation experiments conducted on a dental CT dataset demonstrated that DualSFR-Net was capable to reconstruct high-quality CBCT images under sparse-view sampling protocols.Quantitatively,compared to the current best methods,the DualSFR-Net method improved the PSNR by 0.6615 and 0.7658 and increased the SSIM by 0.0053 and 0.0134 under 2-fold and 4-fold sparse protocols,respectively.Conclusion The proposed generative projection interpolation-based dual-domain CBCT sparse-view reconstruction method can effectively reduce stripe artifacts to improve image quality and enables efficient joint training for dual-domain imaging networks in sparse-view CBCT reconstruction.

Objective To propose a dual-domain CBCT reconstruction framework(DualSFR-Net)based on generative projection interpolation to reduce artifacts in sparse-view cone beam computed tomography(CBCT)reconstruction.Methods The proposed method DualSFR-Net consists of a generative projection interpolation module,a domain transformation module,and an image restoration module.The generative projection interpolation module includes a sparse projection interpolation network(SPINet)based on a generative adversarial network and a full-view projection restoration network(FPRNet).SPINet performs projection interpolation to synthesize full-view projection data from the sparse-view projection data,while FPRNet further restores the synthesized full-view projection data.The domain transformation module introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes.The image restoration module includes an image restoration network FIRNet that fine-tunes the domain-transformed images to eliminate residual artifacts and noise.Results Validation experiments conducted on a dental CT dataset demonstrated that DualSFR-Net was capable to reconstruct high-quality CBCT images under sparse-view sampling protocols.Quantitatively,compared to the current best methods,the DualSFR-Net method improved the PSNR by 0.6615 and 0.7658 and increased the SSIM by 0.0053 and 0.0134 under 2-fold and 4-fold sparse protocols,respectively.Conclusion The proposed generative projection interpolation-based dual-domain CBCT sparse-view reconstruction method can effectively reduce stripe artifacts to improve image quality and enables efficient joint training for dual-domain imaging networks in sparse-view CBCT reconstruction.

Objective We propose a dual-domain cone beam computed tomography(CBCT)reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction.Methods The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules:projection preprocessing,differentiable domain transform,and image post-processing.The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray.The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes,where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation,thus enabling precise learning of cone-beam region data.The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises.Results The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation.Compared with the latest methods,the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074,respectively.Conclusion The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

Objective We propose a dual-domain cone beam computed tomography(CBCT)reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction.Methods The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules:projection preprocessing,differentiable domain transform,and image post-processing.The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray.The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes,where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation,thus enabling precise learning of cone-beam region data.The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises.Results The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation.Compared with the latest methods,the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074,respectively.Conclusion The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

To investigate the feasibility and clinical application of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) technique in non-invasive assessment for early chronic allograft nephropathy (CAN).
 Methods: A total of 23 renal allograft recipients were recruited from inpatients or outpatients according to the inclusion and exclusion criteria for this study. Recipients were divided into a CAN group (n=12, pathologically confirmed early CAN patients) and a control group (n=11, volunteers with long-term stable renal function). Abdominal MRI was performed on patients of renal allograft with a multi-b value DWI sequence. IVIM2b-new software was used for obtaining the IVIM-DWI quantitative parameter pseudo-color maps and the values of IVIM-DWI of renal parenchyma, including the pure diffusion coefficient (D), perfusion correlation diffusion coefficient (D*) and perfusion fraction (f). The IVIM quantitative parameters between the two groups were compared using independent sample t test. ROC analysis was performed when the differences in parameter were statistically significant and the area under curve (AUC) was calculated.
 Results: In IVIM bi-exponential analysis, The D value was significantly decreased in the CAN group compared with the control group (P<0.05), whereas there are no significantly difference in value of D* and f between the two groups (all P>0.05). The AUC of D value for distinguishing the early CAN from the control were 0.784 with sensitivity and specificity at 58.3% and 90.9%, respectively.
 Conclusion: The IVIM-DWI quantitative parameter D can non-invasively assess early CAN to some extent. IVIM-DWI technique is expected to be an effective, easy and non-invasive method to detect early CAN, and assist early diagnose as well as dynamically monitor CAN.
Allografts ; Diffusion Magnetic Resonance Imaging ; Humans ; Kidney Diseases ; surgery ; Kidney Transplantation ; Magnetic Resonance Imaging ; Motion