Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT

• URL: http://arxiv.org/abs/2401.11256v1
• Date: Sat, 20 Jan 2024 15:29:29 GMT
• Title: Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT
• Authors: Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
• Abstract summary: We propose LIRE+, a learned iterative scheme for fast and memory-efficient CBCT reconstruction. LIRE+ is a rotationally-equivariant multiscale learned invertible primal-dual iterative scheme for CBCT reconstruction. Our method surpasses classical and deep learning baselines, including LIRE, on the thorax test set.
• Score: 7.497397088625152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cone Beam CT (CBCT) is an essential imaging modality nowadays, but the image quality of CBCT still lags behind the high quality standards established by the conventional Computed Tomography. We propose LIRE+, a learned iterative scheme for fast and memory-efficient CBCT reconstruction, which is a substantially faster and more parameter-efficient alternative to the recently proposed LIRE method. LIRE+ is a rotationally-equivariant multiscale learned invertible primal-dual iterative scheme for CBCT reconstruction. Memory usage is optimized by relying on simple reversible residual networks in primal/dual cells and patch-wise computations inside the cells during forward and backward passes, while increased inference speed is achieved by making the primal-dual scheme multiscale so that the reconstruction process starts at low resolution and with low resolution primal/dual latent vectors. A LIRE+ model was trained and validated on a set of 260 + 22 thorax CT scans and tested using a set of 142 thorax CT scans with additional evaluation with and without finetuning on an out-of-distribution set of 79 Head and Neck (HN) CT scans. Our method surpasses classical and deep learning baselines, including LIRE, on the thorax test set. For a similar inference time and with only 37 % of the parameter budget, LIRE+ achieves a +0.2 dB PSNR improvement over LIRE, while being able to match the performance of LIRE in 45 % less inference time and with 28 % of the parameter budget. Rotational equivariance ensures robustness of LIRE+ to patient orientation, while LIRE and other deep learning baselines suffer from substantial performance degradation when patient orientation is unusual. On the HN dataset in the absence of finetuning, LIRE+ is generally comparable to LIRE in performance apart from a few outlier cases, whereas after identical finetuning LIRE+ demonstates a +1.02 dB PSNR improvement over LIRE.

Related papers

• Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT: From Simulated to Real Data [28.21267815130768]
  LIRE++ is an end-to-end rotationally-equivariant multiscale learned invertible primal-dual scheme for fast and memory-efficient CBCT reconstruction.<n>LIRE++ was trained on simulated projection data from a fast quasi-Monte Carlo CBCT projection simulator.<n>On real clinical data, LIRE++ improved the average Mean Absolute Error between the reconstruction and the corresponding planning CT by 10 Hounsfield Units.
  arXiv  Detail & Related papers  (2025-12-24T13:59:43Z)
• Resolution-Independent Neural Operators for Multi-Rate Sparse-View CT [67.14700058302016]
  Deep learning methods achieve high-fidelity reconstructions but often overfit to a fixed acquisition setup.<n>We propose Computed Tomography neural Operator (CTO), a unified CT reconstruction framework that extends to continuous function space.<n>CTO enables consistent multi-sampling-rate and cross-resolution performance, with on average >4dB PSNR gain over CNNs.
  arXiv  Detail & Related papers  (2025-12-13T08:31:46Z)
• Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation [64.42236775544579]
  Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating thoracic tumours.<n>DeepPriorCBCT is a three-stage deep learning framework that achieves diagnostic-grade reconstruction using only one-sixth of the conventional radiation dose.
  arXiv  Detail & Related papers  (2025-11-30T12:45:02Z)
• Stage-Specific Benchmarking of Deep Learning Models for Glioblastoma Follow-Up MRI [1.1458853556386799]
  We present the first stage-specific, cross-sectional benchmarking of deep learning models for follow-up MRI.<n>We analyze different post-RT scans independently to test whether architecture performance depends on time-point.<n>These results establish a stage-aware benchmark and motivate future work incorporating longitudinal modeling, multi-sequence MRI, and larger multi-center cohorts.
  arXiv  Detail & Related papers  (2025-11-23T19:38:03Z)
• NeRF-based CBCT Reconstruction needs Normalization and Initialization [53.58395475423445]
  NeRF-based methods suffer from a local-global training mismatch between their two key components: the hash encoder and the neural network.<n>We introduce a Normalized Hash, which enhances feature consistency and mitigates the mismatch.<n>The neural network exhibits improved stability during early training, enabling faster convergence and enhanced reconstruction performance.
  arXiv  Detail & Related papers  (2025-06-24T16:01:45Z)
• Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models [7.703600088651634]
  Cone-beam CT (CBCT) is widely used in clinical radiotherapy for image-guided treatment.<n>However, slow gantry rotation limits performance by introducing motion artifacts, blurring, and increased dose.<n>This work aims to develop a clinically feasible method for reconstructing high-quality CBCT volumes from consecutive limited-angle acquisitions.
  arXiv  Detail & Related papers  (2025-06-16T14:32:16Z)
• MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting [3.9576638237437183]
  This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details. We propose a novel diffusion-based SR framework called Res-SRDiff, which integrates residual error shifting into the forward diffusion process.
  arXiv  Detail & Related papers  (2025-03-03T14:15:08Z)
• Future frame prediction in chest cine MR imaging using the PCA respiratory motion model and dynamically trained recurrent neural networks [0.0]
  Lung radiotherapy treatment systems are subject to a latency that leads to uncertainty in the estimated tumor location and high irradiation of healthy tissue. This work addresses future frame prediction in chest dynamic MRI sequences to compensate for that delay using RNNs trained with online learning algorithms.
  arXiv  Detail & Related papers  (2024-10-08T10:21:43Z)
• Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction [1.167578793004766]
  We propose a self-Supervised deep learning compressed Diffusion sensing MRI (DL)" method. We used 1,376 and singlecoil brain axial post T1 dataset (T1-w) 50 patients. It was compared with ReconFormer Transformer and SS-MRI, assessing performance using normalized mean error (NMSE), peak signal-to-noise ratio (PSNR), and similarity index (SSIM)
  arXiv  Detail & Related papers  (2024-06-21T21:22:17Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization [13.01191568245715]
  Cone beam computed tomography (CBCT) is an emerging medical imaging technique to visualize the internal anatomical structures of patients. reducing the number of projections in a CBCT scan while preserving the quality of a reconstructed image is challenging. We propose a fast and accurate sparse-view CBCT reconstruction (FACT) method to provide better reconstruction quality and faster optimization speed.
  arXiv  Detail & Related papers  (2023-12-04T07:23:44Z)
• Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks [70.75043144299168]
  Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. We propose the Spatial Learning Through Time (SLTT) method that can achieve high performance while greatly improving training efficiency. Our method achieves state-of-the-art accuracy on ImageNet, while the memory cost and training time are reduced by more than 70% and 50%, respectively, compared with BPTT.
  arXiv  Detail & Related papers  (2023-02-28T05:01:01Z)
• REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition [75.64791080418162]
  REGAS proposes a self-supervised method to synthesize the undersampled tomographic views and mitigate aliasing artifacts in reconstructed images. To address the large memory cost of deep neural networks on high resolution 4D data, REGAS introduces a novel Ray Path Transformation (RPT) that allows for distributed, differentiable forward projections.
  arXiv  Detail & Related papers  (2022-08-17T03:42:19Z)
• Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans [2.3682456328966115]
  We propose a novel, effective, two-step-wise approach to tickle this issue for COVID-19 symptom classification thoroughly. First, the semantic feature embedding of each slice for a CT scan is extracted by conventional backbone networks. Then, we proposed a long short-term memory (LSTM) and Transformer-based sub-network to deal with temporal feature learning.
  arXiv  Detail & Related papers  (2022-07-04T16:59:05Z)
• Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction [0.4199844472131921]
  The Primal-Dual network is a deep learning based method for reconstructing sparsely-sampled CT data. This paper introduces Primal-Dual UNet, which improves the Primal-Dual network in terms of accuracy and reconstruction speed.
  arXiv  Detail & Related papers  (2021-12-26T19:31:34Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• Iterative Network for Image Super-Resolution [69.07361550998318]
  Single image super-resolution (SISR) has been greatly revitalized by the recent development of convolutional neural networks (CNN) This paper provides a new insight on conventional SISR algorithm, and proposes a substantially different approach relying on the iterative optimization. A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization.
  arXiv  Detail & Related papers  (2020-05-20T11:11:47Z)

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.