Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis

• URL: http://arxiv.org/abs/2303.10202v1
• Date: Fri, 17 Mar 2023 18:45:05 GMT
• Title: Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis
• Authors: Lotte Nijskens, Cornelis (Nico) AT van den Berg, Joost JC Verhoeff, Matteo Maspero
• Abstract summary: MRI protocols may change over time or differ between centres resulting in low-quality sCT. domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation. Purpose: investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation. Methods: CT and corresponding T1-weighted MRI with/without contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation. A ``Baseline'' generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline. Results: The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE)=106$\pm$20.7 HU (mean$\pm\sigma$). Performance on FLAIR significantly improved for the DR model with MAE=99.0$\pm$14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE=72.6$\pm$10.1 HU). Similarly, an improvement in $\gamma$-pass rate was obtained for DR vs Baseline. Conclusions: DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Deep Learning Based Apparent Diffusion Coefficient Map Generation from Multi-parametric MR Images for Patients with Diffuse Gliomas [1.5267759610392577]
  Apparent diffusion coefficient (ADC) maps derived from diffusion weighted (DWI) MRI provide functional measurements about the water molecules in tissues. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images.
  arXiv  Detail & Related papers  (2024-07-02T19:08:40Z)
• Evaluating the Impact of Sequence Combinations on Breast Tumor Segmentation in Multiparametric MRI [0.0]
  The effect of sequence combinations in mpMRI remains under-investigated. The nnU-Net model using DCE sequences achieved a Dice similarity coefficient (DSC) of 0.69 $pm$ 0.18 for functional tumor volume (FTV) segmentation.
  arXiv  Detail & Related papers  (2024-06-12T02:09:05Z)
• Cycle-consistent Generative Adversarial Network Synthetic CT for MR-only Adaptive Radiation Therapy on MR-Linac [0.0]
  Cycle-GAN model was trained with MRI and CT scan slices from MR-LINAC treatments, generating sCT volumes. Dosimetric evaluations indicated minimal differences between sCTs and dCTs, with sCTs showing better air-bubble reconstruction.
  arXiv  Detail & Related papers  (2023-12-03T04:38:17Z)
• 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)
• DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding [7.6849475214826315]
  We propose a novel framework called DDMM- Synth for medical image synthesis. It combines an MRI-guided diffusion model with a new CT measurement embedding reverse sampling scheme. It can adjust the projection number of CT a posteriori for a particular clinical application and its modified version can even improve the results significantly for noisy cases.
  arXiv  Detail & Related papers  (2023-03-28T07:13:11Z)
• 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)
• Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it? [0.0]
  We evaluate strategies to integrate information from perfusion imaging in deep neural architectures. Perfusion maps from dynamic contrast enhanced MR exams are shown to positively impact segmentation and grading performance of PCa lesions.
  arXiv  Detail & Related papers  (2022-07-06T07:55:46Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• 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)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.