U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images

• URL: http://arxiv.org/abs/2206.08078v1
• Date: Thu, 16 Jun 2022 10:47:15 GMT
• Title: U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images
• Authors: Marcel Kollovieh, Matthias Keicher, Stephan Wunderlich, Hendrik Burwinkel, Thomas Wendler and Nassir Navab
• Abstract summary: We propose a multi-task method that takes T1-weighted MR images as an input to generate synthetic FDG-PET images. The attention gates used in both task heads can visualize the most relevant parts of the brain, guiding the examiner and adding interpretability. Results show the successful generation of synthetic FDG-PET images and a performance increase in disease classification over the naive single-task baseline.
• Score: 37.14076185163271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is the best image-based tool for diagnosis, FDG-PET is not always available. The reliable detection of Alzheimer's disease with only MRI could be beneficial, especially in regions where FDG-PET might not be affordable for all patients. To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classifies the dementia progression of the patient into cognitive normal (CN), cognitive impairment (MCI), and AD. The attention gates used in both task heads can visualize the most relevant parts of the brain, guiding the examiner and adding interpretability. Results show the successful generation of synthetic FDG-PET images and a performance increase in disease classification over the naive single-task baseline.

Related papers

• PASTA: Pathology-Aware MRI to PET Cross-Modal Translation with Diffusion Models [7.6672160690646445]
  We introduce PASTA, a novel pathology-aware image translation framework based on conditional diffusion models. A cycle exchange consistency and volumetric generation strategy elevate PASTA's capability to produce high-quality 3D PET scans. For Alzheimer's classification, the performance of synthesized scans improves over MRI by 4%, almost reaching the performance of actual PET.
  arXiv  Detail & Related papers  (2024-05-27T08:33:24Z)
• Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with Conditional Generative Adversarial Networks [45.426889188365685]
  Alzheimer's Disease hallmarks include amyloid-beta deposits and brain atrophy. PET is expensive, invasive and exposes patients to ionizing radiation. MRI is cheaper, non-invasive, and free from ionizing radiation but limited to measuring brain atrophy.
  arXiv  Detail & Related papers  (2024-05-03T14:10:29Z)
• Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization [31.47772770612585]
  FDG positron emission tomography (PET) has emerged as a crucial tool in identifying the epileptic focus. The effectiveness of FDG PET-based assessment and diagnosis depends on the selection of a healthy control group. In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization.
  arXiv  Detail & Related papers  (2024-02-02T07:26:56Z)
• Amyloid-Beta Axial Plane PET Synthesis from Structural MRI: An Image Translation Approach for Screening Alzheimer's Disease [49.62561299282114]
  An image translation model is implemented to produce synthetic amyloid-beta PET images from structural MRI that are quantitatively accurate. We found that the synthetic PET images could be produced with a high degree of similarity to truth in terms of shape, contrast and overall high SSIM and PSNR.
  arXiv  Detail & Related papers  (2023-09-01T16:26:42Z)
• Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction [62.29541106695824]
  This paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM) By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process.
  arXiv  Detail & Related papers  (2023-08-20T04:10:36Z)
• Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model [18.106861006893524]
  PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Ultra-high-field imaging has proven valuable in both clinical and academic settings. We propose a method for synthetic PET from high-filed MRI and ultra-high-field MRI.
  arXiv  Detail & Related papers  (2023-05-06T02:41:03Z)
• Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs [3.678164468512092]
  Alzheimer's Disease (AD) is the most common form of dementia and often difficult to diagnose due to the multifactorial etiology of dementia. Recent works on neuroimaging-based computer-aided diagnosis with deep neural networks (DNNs) showed that fusing structural magnetic resonance images (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) leads to improved accuracy in a study population of healthy controls and subjects with AD. We argue that future work on multi-modal fusion should systematically assess the contribution of individual modalities following our proposed evaluation framework.
  arXiv  Detail & Related papers  (2022-07-05T14:55:56Z)
• Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging [77.32032399775152]
  Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability.
  arXiv  Detail & Related papers  (2022-01-27T16:37:19Z)
• Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques [111.165389441988]
  This work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI) Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.
  arXiv  Detail & Related papers  (2021-05-18T11:37:57Z)
• GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI [1.4838157426267355]
  Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD) MRI is more widely available and provides more flexibility when setting the desired image resolution. Many attempts have been made to synthesize PET images from MR images using generative adversarial networks (GANs) This paper proposes an alternative approach, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance.
  arXiv  Detail & Related papers  (2020-08-10T20:09:35Z)

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.