Diffusion Schrödinger Bridge Models for High-Quality MR-to-CT Synthesis for Head and Neck Proton Treatment Planning
- URL: http://arxiv.org/abs/2404.11741v2
- Date: Sun, 30 Jun 2024 08:39:12 GMT
- Title: Diffusion Schrödinger Bridge Models for High-Quality MR-to-CT Synthesis for Head and Neck Proton Treatment Planning
- Authors: Muheng Li, Xia Li, Sairos Safai, Damien Weber, Antony Lomax, Ye Zhang,
- Abstract summary: Diffusion Schr"odinger Bridge Models (DSBM) is an innovative approach for high-quality MR-to-CT synthesis.
Our research introduces DSBM, an innovative approach for high-quality MR-to-CT synthesis.
- Score: 9.599774878892665
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent advancements in proton therapy, MR-based treatment planning is gaining momentum to minimize additional radiation exposure compared to traditional CT-based methods. This transition highlights the critical need for accurate MR-to-CT image synthesis, which is essential for precise proton dose calculations. Our research introduces the Diffusion Schr\"odinger Bridge Models (DSBM), an innovative approach for high-quality MR-to-CT synthesis. DSBM learns the nonlinear diffusion processes between MR and CT data distributions. This method improves upon traditional diffusion models by initiating synthesis from the prior distribution rather than the Gaussian distribution, enhancing both generation quality and efficiency. We validated the effectiveness of DSBM on a head and neck cancer dataset, demonstrating its superiority over traditional image synthesis methods through both image-level and dosimetric-level evaluations. The effectiveness of DSBM in MR-based proton treatment planning highlights its potential as a valuable tool in various clinical scenarios.
Related papers
- Transforming Multimodal Models into Action Models for Radiotherapy [39.682133213072554]
Radiotherapy a crucial cancer treatment demands precise planning to balance tumor preservation and eradication of healthy tissue.
Traditional treatment planning (TP) is iterative, time-consuming, and reliant on human expertise.
We propose a novel framework to transform a multimodal foundation model (MLM) into an action model for using a few-shot reinforcement learning approach.
arXiv Detail & Related papers (2025-02-06T09:51:28Z) - HC$^3$L-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesis [10.699377597641137]
We propose a novel conditional latent diffusion model for efficient CBCT-to-CT synthesis.
We employ the Unified Feature (UFE) to compress images into a low-dimensional latent space.
Our method can efficiently achieve high-quality CBCT-to-CT synthesis in only over 2 mins per patient.
arXiv Detail & Related papers (2024-11-03T14:00:12Z) - Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings [6.157230849293829]
Daily cone-beam CT (CBCT) imaging, pivotal for therapy adjustment, falls short in tissue density accuracy.
We maximize CBCT data during therapy, complemented by sparse paired fan-beam CTs.
Our approach shows promise in generating high-quality CT images from CBCT scans in RT.
arXiv Detail & Related papers (2024-09-19T07:56:06Z) - Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction [10.158713017984345]
We propose a few-shot low-dose CT reconstruction method using Partitioned Hankel-based Diffusion (PHD) models.
In the iterative reconstruction stage, an iterative differential equation solver is employed along with data consistency constraints to update the acquired projection data.
The results approximate those of normaldose counterparts, validating PHD model as an effective and practical model for reducing artifacts and noise while preserving image quality.
arXiv Detail & Related papers (2024-05-27T13:44:53Z) - Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood [64.95663299945171]
Training energy-based models (EBMs) on high-dimensional data can be both challenging and time-consuming.
There exists a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models.
We propose cooperative diffusion recovery likelihood (CDRL), an effective approach to tractably learn and sample from a series of EBMs.
arXiv Detail & Related papers (2023-09-10T22:05:24Z) - Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion
Generative Models [75.52575380824051]
We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI.
We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process.
Our method requires as few as five training images to learn effective sampling patterns.
arXiv Detail & Related papers (2023-06-05T22:09:06Z) - 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) - Incremental Cross-view Mutual Distillation for Self-supervised Medical
CT Synthesis [88.39466012709205]
This paper builds a novel medical slice to increase the between-slice resolution.
Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy.
Our method outperforms state-of-the-art algorithms by clear margins.
arXiv Detail & Related papers (2021-12-20T03:38:37Z) - CT Image Harmonization for Enhancing Radiomics Studies [10.643230630935781]
RadiomicGAN is developed to mitigate the discrepancy caused by using non-standard reconstruction kernels.
A novel training approach, called Dynamic Window-based Training, has been developed to transform the pre-trained model to the medical imaging domain.
Model performance evaluated using 1401 radiomic features show that RadiomicGAN clearly outperforms the state-of-art image standardization models.
arXiv Detail & Related papers (2021-07-03T04:03:42Z) - Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic
and Molecular MR Images in Patients with Post-treatment Malignant Gliomas [65.64363834322333]
Confidence Guided SAMR (CG-SAMR) synthesizes data from lesion information to multi-modal anatomic sequences.
module guides the synthesis based on confidence measure about the intermediate results.
experiments on real clinical data demonstrate that the proposed model can perform better than the state-of-theart synthesis methods.
arXiv Detail & Related papers (2020-08-06T20:20:22Z) - Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR
Images using a GAN [59.60954255038335]
The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators.
Experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.
arXiv Detail & Related papers (2020-06-26T02:50:09Z)
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.