Imaging foundation model for universal enhancement of non-ideal measurement CT

• URL: http://arxiv.org/abs/2410.01591v1
• Date: Wed, 2 Oct 2024 14:25:02 GMT
• Title: Imaging foundation model for universal enhancement of non-ideal measurement CT
• Authors: Yuxin Liu, Rongjun Ge, Yuting He, Zhan Wu, Chenyu You, Shuo Li, Yang Chen,
• Abstract summary: Non-ideal measurement computed tomography (NICT) sacrifices optimal imaging standards for new advantages in CT imaging. With the reduction of imaging standards, the image quality has also been reduced, limiting the clinical acceptability. We propose a multi-scale integrated Transformer AMPlifier (TAMP) to bridge the image quality degradation with minimal data cost.
• Score: 23.678515579203694
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Non-ideal measurement computed tomography (NICT), which sacrifices optimal imaging standards for new advantages in CT imaging, is expanding the clinical application scope of CT images. However, with the reduction of imaging standards, the image quality has also been reduced, extremely limiting the clinical acceptability. Although numerous studies have demonstrated the feasibility of deep learning for the NICT enhancement in specific scenarios, their high data cost and limited generalizability have become large obstacles. The recent research on the foundation model has brought new opportunities for building a universal NICT enhancement model - bridging the image quality degradation with minimal data cost. However, owing to the challenges in the collection of large pre-training datasets and the compatibility of data variation, no success has been reported. In this paper, we propose a multi-scale integrated Transformer AMPlifier (TAMP), the first imaging foundation model for universal NICT enhancement. It has been pre-trained on a large-scale physical-driven simulation dataset with 3.6 million NICT-ICT image pairs, and is able to directly generalize to the NICT enhancement tasks with various non-ideal settings and body regions. Via the adaptation with few data, it can further achieve professional performance in real-world specific scenarios. Our extensive experiments have demonstrated that the proposed TAMP has significant potential for promoting the exploration and application of NICT and serving a wider range of medical scenarios.

Related papers

• Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction [34.83421198668549]
  Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images. We propose RISE-MAR, which integrates radiologists' feedback into the semi-supervised learning process. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo ground-truths.
  arXiv  Detail & Related papers  (2025-01-26T17:32:58Z)
• SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation [20.026663367994356]
  Large amounts of full-body CT images provide the opportunity to pre-train powerful models. It remains unclear in which conditions these pre-trained models can be transferred to various downstream medical segmentation tasks. We collected 87 public datasets varying in modality, target, and sample size to evaluate the transfer ability of full-body CT pre-trained models.
  arXiv  Detail & Related papers  (2024-11-21T19:00:01Z)
• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  Synthetic Data Generation based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions. Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images [13.655407979403945]
  We propose a novel deep learning network designed to accurately classify lung cancer subtype with multi-dimensional and multi-modality images. The strength of the proposed model lies in its ability to dynamically process both paired CT-pathological image sets and independent CT image sets. We also develop a contrastive constraint module, which quantitatively maps the cross-modality associations through network training.
  arXiv  Detail & Related papers  (2024-07-18T01:42:00Z)
• Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization [0.13108652488669734]
  We propose a novel generative method for domain generalization in histopathology images. Our method employs a generative, self-supervised Vision Transformer to dynamically extract characteristics of image patches. Experiments conducted on two distinct histopathology datasets demonstrate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2024-07-03T08:20:27Z)
• Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial [8.393536317952085]
  We propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed in a New Zealand clinical trial. We present a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and real-world data.
  arXiv  Detail & Related papers  (2024-03-19T00:07:48Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution [47.38982697349244]
  Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc. We propose incorporating active learning into dense regression models to address this problem. Active learning allows models to select the most informative samples for labeling, reducing the overall annotation cost while improving performance.
  arXiv  Detail & Related papers  (2023-05-27T16:33:43Z)
• This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation [7.430724826764835]
  A novel Variational Autoencoder architecture is proposed, namely "This Intestine Does not Exist" (TIDE) The proposed architecture comprises multiscale feature extraction convolutional blocks and residual connections, which enable the generation of high-quality and diverse datasets. Contrary to the current approaches, which are oriented towards the augmentation of the available datasets, this study demonstrates that using TIDE, real WCE datasets can be fully substituted.
  arXiv  Detail & Related papers  (2023-02-04T11:49:38Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism [4.998352078907441]
  Low radiation dose may result in increased noise and artifacts, which greatly affected the clinical diagnosis. To obtain high-quality Total-body Low-dose CT (LDCT) images, previous deep-learning-based research work has introduced various network architectures. In this paper, we propose a novel intra-task knowledge transfer method that leverages the distilled knowledge from NDCT images.
  arXiv  Detail & Related papers  (2021-12-01T06:46:38Z)
• CT-SGAN: Computed Tomography Synthesis GAN [4.765541373485143]
  We propose the CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes when trained on a small dataset of chest CT-scans. We show that CT-SGAN can significantly improve lung detection accuracy by pre-training a nodule on a vast amount of synthetic data.
  arXiv  Detail & Related papers  (2021-10-14T22:20:40Z)
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation [47.78425002879612]
  3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of CNNs with the robustness of SSMs.
  arXiv  Detail & Related papers  (2021-04-07T01:15:06Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)
• STAN-CT: Standardizing CT Image using Generative Adversarial Network [10.660781755744312]
  We present an end-to-end solution called STAN-CT for CT image standardization and normalization. STAN-CT consists of two components: 1) a novel Generative Adversarial Networks (GAN) model that is capable of effectively learning the data distribution of a standard imaging protocol with only a few rounds of generator training, and 2) an automatic DICOM reconstruction pipeline with systematic image quality control.
  arXiv  Detail & Related papers  (2020-04-02T23:43:06Z)

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.