Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI

• URL: http://arxiv.org/abs/2211.07966v1
• Date: Tue, 15 Nov 2022 08:02:54 GMT
• Title: Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI
• Authors: Yeqi Wang, Weijian Huang, Cheng Li, Xiawu Zheng, Yusong Lin, Shanshan Wang
• Abstract summary: Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI sequences (e.g., contrast-enhanced T1-weighted imaging) were utilized in most of the existing relevant studies. However, acquiring contrast-enhanced MRI data is sometimes not feasible due to the patients physiological limitations. It is more time-consuming and costly to collect contrast-enhanced MRI data in the clinic.
• Score: 11.231836756951655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI sequences (e.g., contrast-enhanced T1-weighted imaging) were utilized in most of the existing relevant studies, in which remarkable diagnosis results have been reported. Nevertheless, acquiring contrast-enhanced MRI data is sometimes not feasible due to the patients physiological limitations. Furthermore, it is more time-consuming and costly to collect contrast-enhanced MRI data in the clinic. In this paper, we propose an adaptive PromptNet to address these issues. Specifically, a PromptNet for glioma grading utilizing only non-enhanced MRI data has been constructed. PromptNet receives constraints from features of contrast-enhanced MR data during training through a designed prompt loss. To further boost the performance, an adaptive strategy is designed to dynamically weight the prompt loss in a sample-based manner. As a result, PromptNet is capable of dealing with more difficult samples. The effectiveness of our method is evaluated on a widely-used BraTS2020 dataset, and competitive glioma grading performance on NE-MRI data is achieved.

Related papers

• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation [51.28453192441364]
  Multimodal brain magnetic resonance (MR) imaging is indispensable in neuroscience and neurology. Current MR image synthesis approaches are typically trained on independent datasets for specific tasks. We present TUMSyn, a Text-guided Universal MR image Synthesis model, which can flexibly generate brain MR images.
  arXiv  Detail & Related papers  (2024-09-25T11:14:47Z)
• The MRI Scanner as a Diagnostic: Image-less Active Sampling [9.964204750574469]
  We propose an ML-based framework that learns an active sampling strategy, via reinforcement learning, at a patient-level to directly infer disease from undersampled k-space. We validate our approach by inferring Meniscus Tear in undersampled knee MRI data, where we achieve diagnostic performance comparable with ML-based diagnosis.
  arXiv  Detail & Related papers  (2024-06-24T16:00:20Z)
• Enhanced Synthetic MRI Generation from CT Scans Using CycleGAN with Feature Extraction [3.2088888904556123]
  We propose an approach for enhanced monomodal registration using synthetic MRI images from CT scans. Our methodology shows promising results, outperforming several state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-31T16:39:56Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs [102.47542231659521]
  Gadolinium-based contrast agents (GBCA) are indispensable in Magnetic Resonance Imaging (MRI) for diagnosing various diseases. GBCAs are expensive and may accumulate in patients with potential side effects. It is unclear to which extent the GBCA dose can be reduced while preserving the diagnostic value.
  arXiv  Detail & Related papers  (2023-06-26T13:19:37Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction [0.0]
  Data Consistency (DC) is crucial for generalization in multi-modal data and robustness in detecting pathology. This work proposes the Cascades of Independently Recurrent Inference Machines (CIRIM) to assess DC through unrolled optimization. We show that the CIRIM performs best when implicitly enforcing DC, while the E2EVN requires explicitly formulated DC.
  arXiv  Detail & Related papers  (2021-11-30T15:34:30Z)
• Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis [8.363448006582065]
  We propose a deep learning based multi-modal computing model for MRI synthesis with feature disentanglement strategy. The proposed approach decomposes each input modality into modality-invariant space with shared information and modality-specific space with specific information. To address the lack of specific information of the target modality in the test phase, a local adaptive fusion (LAF) module is adopted to generate a modality-like pseudo-target.
  arXiv  Detail & Related papers  (2021-05-06T17:22:22Z)
• Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisition [19.422926534305837]
  We propose a deep-learning approach, aiming at reconstructing high-quality images from accelerated MRI acquisition. Specifically, we use Convolutional Neural Network (CNN) to learn the differences between the aliased images and the original images. Considering the peculiarity of the down-sampled k-space data, we introduce a new term to the loss function in learning, which effectively employs the given k-space data.
  arXiv  Detail & Related papers  (2020-01-13T19:01:17Z)

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.