Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions

• URL: http://arxiv.org/abs/2307.09624v2
• Date: Sun, 23 Jul 2023 23:06:23 GMT
• Title: Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions
• Authors: Huidong Xie, Bo Zhou, Xiongchao Chen, Xueqi Guo, Stephanie Thorn, Yi-Hwa Liu, Ge Wang, Albert Sinusas, Chi Liu
• Abstract summary: We propose a novel 3D transformer-based dual-domain network, called TIP-Net, for high-quality 3D cardiac SPECT image reconstructions. Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process.
• Score: 8.510419245628983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cardiovascular disease (CVD) is the leading cause of death worldwide, and myocardial perfusion imaging using SPECT has been widely used in the diagnosis of CVDs. The GE 530/570c dedicated cardiac SPECT scanners adopt a stationary geometry to simultaneously acquire 19 projections to increase sensitivity and achieve dynamic imaging. However, the limited amount of angular sampling negatively affects image quality. Deep learning methods can be implemented to produce higher-quality images from stationary data. This is essentially a few-view imaging problem. In this work, we propose a novel 3D transformer-based dual-domain network, called TIP-Net, for high-quality 3D cardiac SPECT image reconstructions. Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process by proposing a customized projection-to-image domain transformer. Then, given its reconstruction output and the original few-view reconstruction, we further refine the reconstruction using an image-domain reconstruction network. Validated by cardiac catheterization images, diagnostic interpretations from nuclear cardiologists, and defect size quantified by an FDA 510(k)-cleared clinical software, our method produced images with higher cardiac defect contrast on human studies compared with previous baseline methods, potentially enabling high-quality defect visualization using stationary few-view dedicated cardiac SPECT scanners.

Related papers

• Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction [8.730291904586656]
  Mesh reconstruction of the cardiac anatomy from medical images is useful for shape and motion measurements and biophysics simulations. Traditional voxel-based approaches rely on pre- and post-processing that compromises image fidelity. We propose a novel explicit differentiable voxelization and slicing (DVS) algorithm that allows gradient backpropagation to a mesh from its slices.
  arXiv  Detail & Related papers  (2024-09-03T17:19:31Z)
• CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imaging [78.734927709231]
  Sparse-view computed tomography (SVCT) reconstruction aims to acquire CT images based on sparsely-sampled measurements. Due to ill-posedness, implicit neural representation (INR) techniques may leave considerable holes'' (i.e., unmodeled spaces) in their fields, leading to sub-optimal results. We propose the Coordinate-based Continuous Projection Field (CoCPF), which aims to build hole-free representation fields for SVCT reconstruction.
  arXiv  Detail & Related papers  (2024-06-21T08:38:30Z)
• 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning [79.60829508459753]
  Current commercial Digital Subtraction Angiography (DSA) systems typically demand hundreds of scanning views to perform reconstruction. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. We propose to use a time-agnostic vessel probability field to solve this problem effectively.
  arXiv  Detail & Related papers  (2024-05-17T11:23:33Z)
• SdCT-GAN: Reconstructing CT from Biplanar X-Rays with Self-driven Generative Adversarial Networks [6.624839896733912]
  This paper presents a new self-driven generative adversarial network model (SdCT-GAN) for reconstruction of 3D CT images. It is motivated to pay more attention to image details by introducing a novel auto-encoder structure in the discriminator. LPIPS evaluation metric is adopted that can quantitatively evaluate the fine contours and textures of reconstructed images better than the existing ones.
  arXiv  Detail & Related papers  (2023-09-10T08:16:02Z)
• Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images [4.1448595037512925]
  We propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task.
  arXiv  Detail & Related papers  (2023-07-17T14:52:52Z)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency [12.1851913514097]
  We propose a dual-domain iterative network for end-to-end joint denoising and reconstruction from low-dose and few-angle projections of cardiac SPECT. Experiments using clinical MPI data show that our proposed method outperforms existing image-, projection-, and dual-domain techniques.
  arXiv  Detail & Related papers  (2023-05-17T16:09:49Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition [75.64791080418162]
  REGAS proposes a self-supervised method to synthesize the undersampled tomographic views and mitigate aliasing artifacts in reconstructed images. To address the large memory cost of deep neural networks on high resolution 4D data, REGAS introduces a novel Ray Path Transformation (RPT) that allows for distributed, differentiable forward projections.
  arXiv  Detail & Related papers  (2022-08-17T03:42:19Z)
• Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views [0.6524460254566904]
  Reconstructing cardiac anatomy in 3D can enable discovery of new biomarkers. Most ultrasound systems only have 2D imaging capabilities. We propose a pipeline to perform reconstruction of 3D anatomy from 2D standard cardiac views.
  arXiv  Detail & Related papers  (2022-07-27T10:05:46Z)
• Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern [49.240017254888336]
  Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties. A current drawback is the limited field-of-view provided by the conventionally applied 2D probes. We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
  arXiv  Detail & Related papers  (2020-11-10T09:27:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.