FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction

Deng, Bin; Gu, Hanxue; Zhu, Hongtao; Chang, Ken; Hoebel, Katharina; Patel, Jay; Kalpathy–Cramer, Jayashree; Carp, Stefan A.

doi:10.1109/tmi.2023.3252576

Cited by 10 publications

(21 citation statements)

References 49 publications

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“…In the diagnosis of prostate cancer using multiparametric magnetic resonance imaging (mpMRI), Gassenmaier et al [476] proposed an accelerated deep learning image reconstruction T2-weighted turbo spin echo (TSE) sequence, which reduced the acquisition time by more than 60%. In order to solve the problem that traditional optical image reconstruction methods based on the finite element method (FEM) are time-consuming and cannot fully restore the lesion contrast, Deng et al [477] proposed FDU-Net, which consists of a fully connected subnetwork, a convolutional encoder-decoder subnetwork, and a U-Net. Among them, the U-Net is used for fast and end-to-end reconstruction of 3D diffuse optical tomography (DOT) images.…”

Section: Image Reconstructionmentioning

confidence: 99%

Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang

Wang

et al. 2023

Cancers

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(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

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Section: Image Reconstructionmentioning

confidence: 99%

Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang

Wang

et al. 2023

Cancers

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“…Due to the successful application of deep convolutional neural networks (DCNNs) in various fields, deep-based image reconstruction has received considerable attention in both industry and academia [6]. Deep intrinsic features can be adaptively extracted via DCNN-based methods, and hierarchical representation can be performed.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous High-Resolution Blind Image Reconstruction and Perturbation Defense

Xue,

Zheng,

et al. 2024

IEEE Access

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Image reconstruction plays an important role in modern electronic information systems and equipment and has attracted widespread intense attention in academic and industrial fields. Restoring delicate image particulars using traditional handcrafted prior image reconstruction methods is often difficult because of the inferior prior characterization abilities of these methods. In this article, an efficient image reconstruction model, named DGMR, is proposed based on deep Gaussian mixture learning. In particular, a channel attention mechanism is designed for image spatial correlation exploitation. Both the external and internal information are explored using deep Gaussian mixture. A residual Swin transformer module is constructed to learn Gaussian mixture priors, including the images ' means and variances; in contrast, existing methods calculate only the mean and ignore the variance. Moreover, sparse regularized united learning is developed to improve invariant representation learning ability, and the model is customized by internal learning with spatial constraints and regularization. Extensive qualitative and quantitative experiments are performed, confirming that DGMR is superior to the current advanced comparison approaches.INDEX TERMS Deep learning, Gaussian mixture, image reconstruction, prior learning.

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“… 17 , 18 Researchers have extensively investigated the use of DOT in the diagnosis of breast cancer and the estimation of tissue optical properties. 19 – 24 By incorporating DOT into breast cancer diagnosis, we can potentially improve the accuracy of breast cancer detection and reduce the need for unnecessary biopsies, ultimately improving patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model

Xue,

Li,

Zhu

2024

J. Biomed. Opt.

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Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis.Aim: We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy.Approach: We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data.Results: Transitioning from simulation and phantom data to clinical patients' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis. Conclusions:The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile.

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FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction

Cited by 10 publications

References 49 publications

Deep Learning for Medical Image-Based Cancer Diagnosis

Deep Learning for Medical Image-Based Cancer Diagnosis

Simultaneous High-Resolution Blind Image Reconstruction and Perturbation Defense

Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model

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