4,375 research outputs found
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
During X-ray computed tomography (CT) scanning, metallic implants carrying
with patients often lead to adverse artifacts in the captured CT images and
then impair the clinical treatment. Against this metal artifact reduction (MAR)
task, the existing deep-learning-based methods have gained promising
reconstruction performance. Nevertheless, there is still some room for further
improvement of MAR performance and generalization ability, since some important
prior knowledge underlying this specific task has not been fully exploited.
Hereby, in this paper, we carefully analyze the characteristics of metal
artifacts and propose an orientation-shared convolution representation strategy
to adapt the physical prior structures of artifacts, i.e., rotationally
symmetrical streaking patterns. The proposed method rationally adopts
Fourier-series-expansion-based filter parametrization in artifact modeling,
which can better separate artifacts from anatomical tissues and boost the model
generalizability. Comprehensive experiments executed on synthesized and
clinical datasets show the superiority of our method in detail preservation
beyond the current representative MAR methods. Code will be available at
\url{https://github.com/hongwang01/OSCNet
Quad-Net: Quad-domain Network for CT Metal Artifact Reduction
Metal implants and other high-density objects in patients introduce severe
streaking artifacts in CT images, compromising image quality and diagnostic
performance. Although various methods were developed for CT metal artifact
reduction over the past decades, including the latest dual-domain deep
networks, remaining metal artifacts are still clinically challenging in many
cases. Here we extend the state-of-the-art dual-domain deep network approach
into a quad-domain counterpart so that all the features in the sinogram, image,
and their corresponding Fourier domains are synergized to eliminate metal
artifacts optimally without compromising structural subtleties. Our proposed
quad-domain network for MAR, referred to as Quad-Net, takes little additional
computational cost since the Fourier transform is highly efficient, and works
across the four receptive fields to learn both global and local features as
well as their relations. Specifically, we first design a Sinogram-Fourier
Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to
faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an
Image-Fourier Refinement Network (IFR-Net) which takes both an image and its
Fourier spectrum to improve a CT image reconstructed from the SFR-Net output
using cross-domain contextual information. Quad-Net is trained on clinical
datasets to minimize a composite loss function. Quad-Net does not require
precise metal masks, which is of great importance in clinical practice. Our
experimental results demonstrate the superiority of Quad-Net over the
state-of-the-art MAR methods quantitatively, visually, and statistically. The
Quad-Net code is publicly available at
https://github.com/longzilicart/Quad-Net
Potentials and caveats of AI in Hybrid Imaging
State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
Emerging neural reconstruction techniques based on tomography (e.g., NeRF,
NeAT, and NeRP) have started showing unique capabilities in medical imaging. In
this work, we present a novel Polychromatic neural representation (Polyner) to
tackle the challenging problem of CT imaging when metallic implants exist
within the human body. The artifacts arise from the drastic variation of
metal's attenuation coefficients at various energy levels of the X-ray
spectrum, leading to a nonlinear metal effect in CT measurements.
Reconstructing CT images from metal-affected measurements hence poses a
complicated nonlinear inverse problem where empirical models adopted in
previous metal artifact reduction (MAR) approaches lead to signal loss and
strongly aliased reconstructions. Polyner instead models the MAR problem from a
nonlinear inverse problem perspective. Specifically, we first derive a
polychromatic forward model to accurately simulate the nonlinear CT acquisition
process. Then, we incorporate our forward model into the implicit neural
representation to accomplish reconstruction. Lastly, we adopt a regularizer to
preserve the physical properties of the CT images across different energy
levels while effectively constraining the solution space. Our Polyner is an
unsupervised method and does not require any external training data.
Experimenting with multiple datasets shows that our Polyner achieves comparable
or better performance than supervised methods on in-domain datasets while
demonstrating significant performance improvements on out-of-domain datasets.
To the best of our knowledge, our Polyner is the first unsupervised MAR method
that outperforms its supervised counterparts.Comment: 19 page
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