Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization

• URL: http://arxiv.org/abs/2402.01191v1
• Date: Fri, 2 Feb 2024 07:26:56 GMT
• Title: Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization
• Authors: Wentao Chen, Jiwei Li, Xichen Xu, Hui Huang, Siyu Yuan, Miao Zhang, Tianming Xu, Jie Luo, Weimin Zhou
• Abstract summary: 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.
• Score: 31.47772770612585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: [$^{18}$F]fluorodeoxyglucose (FDG) positron emission tomography (PET) has emerged as a crucial tool in identifying the epileptic focus, especially in cases where magnetic resonance imaging (MRI) diagnosis yields indeterminate results. FDG PET can provide the metabolic information of glucose and help identify abnormal areas that are not easily found through MRI. However, the effectiveness of FDG PET-based assessment and diagnosis depends on the selection of a healthy control group. The healthy control group typically consists of healthy individuals similar to epilepsy patients in terms of age, gender, and other aspects for providing normal FDG PET data, which will be used as a reference for enhancing the accuracy and reliability of the epilepsy diagnosis. However, significant challenges arise when a healthy PET control group is unattainable. Yaakub \emph{et al.} have previously introduced a Pix2PixGAN-based method for MRI to PET translation. This method used paired MRI and FDG PET scans from healthy individuals for training, and produced pseudo normal FDG PET images from patient MRIs that are subsequently used for lesion detection. However, this approach requires a large amount of high-quality, paired MRI and PET images from healthy control subjects, which may not always be available. In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization. Two deep learning methods, CycleGAN and SynDiff, were employed, and we found that diffusion-based method achieved improved performance in accurately localizing the epileptic focus.

Related papers

• Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRI [5.190302448685122]
  We propose a framework for 3D brain PET image synthesis with paired structural MRI as input condition, through a new constrained diffusion model (CDM) The FICD introduces noise to PET and then progressively removes it with CDM, ensuring high output fidelity throughout a stable training phase. The CDM learns to predict denoised PET with a functional imaging constraint introduced to ensure voxel-wise alignment between each denoised PET and its ground truth.
  arXiv  Detail & Related papers  (2024-05-03T22:33:46Z)
• 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)
• 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)
• 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)
• Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks [10.095428964324874]
  Positron emission tomography (PET) with radiolabeled water (15O-water) is considered the gold-standard for the measurement of cerebral blood flow (CBF) in humans. PET imaging is not widely available because of its prohibitive costs and use of short-lived radiopharmaceutical tracers that typically require onsite cyclotron production. This study presents a convolutional encoder-decoder network with attention mechanisms to predict gold-standard 15O-water PET CBF from multi-sequence MRI scans.
  arXiv  Detail & Related papers  (2022-11-22T08:25:44Z)
• A resource-efficient deep learning framework for low-dose brain PET image reconstruction and analysis [13.713286047709982]
  We propose a resource-efficient deep learning framework for L-PET reconstruction and analysis, referred to as transGAN-SDAM. The transGAN generates higher quality F-PET images, and then the SDAM integrates the spatial information of a sequence of generated F-PET slices to synthesize whole-brain F-PET images.
  arXiv  Detail & Related papers  (2022-02-14T08:40:19Z)
• Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images [58.85481248101611]
  We propose a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost.
  arXiv  Detail & Related papers  (2021-06-04T09:51:27Z)
• 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)
• Convolutional neural networks for automatic detection of Focal Cortical Dysplasia [59.034649152318224]
  Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations. Recent methods of Deep Learning-based FCD detection apply it for a dataset of 15 labeled FCD patients. The model results in the successful detection of FCD on 11 out of 15 subjects.
  arXiv  Detail & Related papers  (2020-10-20T15:30:37Z)
• 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.