14 research outputs found
A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data
Differential phase-contrast computed tomography (DPC-CT) is a powerful
analysis tool for soft-tissue and low-atomic-number samples. Limited by the
implementation conditions, DPC-CT with incomplete projections happens quite
often. Conventional reconstruction algorithms are not easy to deal with
incomplete data. They are usually involved with complicated parameter selection
operations, also sensitive to noise and time-consuming. In this paper, we
reported a new deep learning reconstruction framework for incomplete data
DPC-CT. It is the tight coupling of the deep learning neural network and DPC-CT
reconstruction algorithm in the phase-contrast projection sinogram domain. The
estimated result is the complete phase-contrast projection sinogram not the
artifacts caused by the incomplete data. After training, this framework is
determined and can reconstruct the final DPC-CT images for a given incomplete
phase-contrast projection sinogram. Taking the sparse-view DPC-CT as an
example, this framework has been validated and demonstrated with synthetic and
experimental data sets. Embedded with DPC-CT reconstruction, this framework
naturally encapsulates the physical imaging model of DPC-CT systems and is easy
to be extended to deal with other challengs. This work is helpful to push the
application of the state-of-the-art deep learning theory in the field of
DPC-CT
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
The radiation dose in computed tomography (CT) examinations is harmful for
patients but can be significantly reduced by intuitively decreasing the number
of projection views. Reducing projection views usually leads to severe aliasing
artifacts in reconstructed images. Previous deep learning (DL) techniques with
sparse-view data require sparse-view/full-view CT image pairs to train the
network with supervised manners. When the number of projection view changes,
the DL network should be retrained with updated sparse-view/full-view CT image
pairs. To relieve this limitation, we present a fully unsupervised score-based
generative model in sinogram domain for sparse-view CT reconstruction.
Specifically, we first train a score-based generative model on full-view
sinogram data and use multi-channel strategy to form highdimensional tensor as
the network input to capture their prior distribution. Then, at the inference
stage, the stochastic differential equation (SDE) solver and data-consistency
step were performed iteratively to achieve fullview projection. Filtered
back-projection (FBP) algorithm was used to achieve the final image
reconstruction. Qualitative and quantitative studies were implemented to
evaluate the presented method with several CT data. Experimental results
demonstrated that our method achieved comparable or better performance than the
supervised learning counterparts.Comment: 11 pages, 12 figure
Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
Diffusion models have emerged as potential tools to tackle the challenge of
sparse-view CT reconstruction, displaying superior performance compared to
conventional methods. Nevertheless, these prevailing diffusion models
predominantly focus on the sinogram or image domains, which can lead to
instability during model training, potentially culminating in convergence
towards local minimal solutions. The wavelet trans-form serves to disentangle
image contents and features into distinct frequency-component bands at varying
scales, adeptly capturing diverse directional structures. Employing the Wavelet
transform as a guiding sparsity prior significantly enhances the robustness of
diffusion models. In this study, we present an innovative approach named the
Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for
sparse-view CT reconstruction. Specifically, we establish a unified
mathematical model integrating low-frequency and high-frequency generative
models, achieving the solution with optimization procedure. Furthermore, we
perform the low-frequency and high-frequency generative models on wavelet's
decomposed components rather than sinogram or image domains, ensuring the
stability of model training. Our method rooted in established optimization
theory, comprising three distinct stages, including low-frequency generation,
high-frequency refinement and domain transform. Our experimental results
demonstrate that the proposed method outperforms existing state-of-the-art
methods both quantitatively and qualitatively
Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data
In x-ray computed tomography (CT), the achievable image resolution is typically limited by several pre-fixed characteristics of the x-ray source and detector. Structuring the x-ray beam using a mask with alternating opaque and transmitting septa can overcome this limit. However, the use of a mask imposes an undersampling problem: to obtain complete datasets, significant lateral sample stepping is needed in addition to the sample rotation, resulting in high x-ray doses and long acquisition times. Cycloidal CT, an alternative scanning scheme by which the sample is rotated and translated simultaneously, can provide high aperture-driven resolution without sample stepping, resulting in a lower radiation dose and faster scans. However, cycloidal sinograms are incomplete and must be restored before tomographic images can be computed. In this work, we demonstrate that high-quality images can be reconstructed by applying the recently proposed Mixed Scale Dense (MS-D) convolutional neural network (CNN) to this task. We also propose a novel training approach by which training data are acquired as part of each scan, thus removing the need for large sets of pre-existing reference data, the acquisition of which is often not practicable or possible. We present results for both simulated datasets and real-world data, showing that the combination of cycloidal CT and machine learning-based data recovery can lead to accurate high-resolution images at a limited dose