This study proposes an innovative 3D diffusion-based model called the Cycle-consistency Geometric-integrated X-ray to Computed Tomography Denoising Diffusion Probabilistic Model (X-CBCT-DDPM). The X-CBCT-DDPM is designed to effectively reconstruct volumetric Cone-Beam CBCTs (CBCTs) from a single X-ray projection from any angle, reducing the number of required projections and minimizing patient radiation exposure in acquiring volumetric images. In contrast to the traditional DDPMs, the X-CBCT-DDPM utilizes dual DDPMs: one for generating full-view x-ray projections and another for volumetric CBCT reconstruction. These dual networks synergistically enhance each other's learning capabilities, leading to improved reconstructed CBCT quality with high anatomical accuracy. The proposed patient-specific X-CBCT-DDPM was tested using 4DCBCT data from ten patients, with each patient's dataset comprising ten phases of 3D CBCTs to simulate CBCTs and Cone-Beam X-ray projections. For model training, eight phases of 3D CBCTs from each patient were utilized, with one for validation purposes and the remaining one reserved for final testing. The X-CBCT-DDPM exhibits superior performance to DDPM, conditional Generative Adversarial Networks (GAN), and Vnet, in terms of various metrics, including a Mean Absolute Error (MAE) of 36.36±4.04, Peak Signal-to-Noise Ratio (PSNR) of 32.83±0.98, Structural Similarity Index (SSIM) of 0.91±0.01, and Fréchet Inception Distance (FID) of 0.32±0.02. These results highlight the model's potential for ultra-sparse projection-based CBCT reconstruction.
This study aims to enhance the resolution of Magnetic Resonance Imaging (MRI) using a cutting-edge diffusion probabilistic Deep Learning (DL) technique, addressing the challenges posed by long image acquisition times and limited scanning dimensions. In this research, we propose a novel approach utilizing a probabilistic DL model to synthesize High-Resolution MRI (HR-MRI) images from Low-Resolution (LR) inputs. The proposed model consists of two main steps. In the forward process, Gaussian noise is systematically introduced to LR images through a Markov chain. In the reverse process, a U-Net model is trained using a loss function based on Kullback-Leibler divergence, which maximizes the likelihood of producing ground truth images. We assess the effectiveness of our method on T2-FLAIR images from 120 brain patients in the public BraTS2020 T2-FLAIR database. To gauge performance, we compare our approach with a clinical bicubic model (referred to as Bicubic) and Conditional Generative Adversarial Networks (CGAN). On the BraTS2020 dataset, our framework enhances the Peak Signal-to-Noise Ratio (PSNR) of LR images by 7%, whereas CGAN results in a 3% reduction. The corresponding Multi-scale Structural similarity (MSSIM) values for the proposed method and CGAN are 0.972±0.017 and 0.966±0.024. In this study, we have examined the potential of a diffusion probabilistic DL framework to elevate MRI image resolution. Our proposed method demonstrates the capability to generate high-quality HR images while avoiding issues such as mode collapse or learning multimodal distributions, which are commonly observed in CGAN-based approaches. This framework has the potential to significantly reduce MRI acquisition times for HR imaging, thereby mitigating the risk of motion artifacts and crosstalk.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.