A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation

• URL: http://arxiv.org/abs/2411.07503v1
• Date: Tue, 12 Nov 2024 03:01:39 GMT
• Title: A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation
• Authors: Shengqi Chen, Zilin Wang, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Jiayun Chen, Guohua Wu, Yuan Tang,
• Abstract summary: This study refined the motion tracking accuracy in MRIgRT through the innovation of an automatic real-time tracking method. We developed a novel MRIgRT motion tracking method by integrating two primary methods: theD framework and an improved Chan-Vese model (ICV)
• Score: 9.332679162161428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: Ensuring the precision in motion tracking for MRI-guided Radiotherapy (MRIgRT) is crucial for the delivery of effective treatments. This study refined the motion tracking accuracy in MRIgRT through the innovation of an automatic real-time tracking method, leveraging an enhanced Tracking-Learning-Detection (ETLD) framework coupled with automatic segmentation. Methods: We developed a novel MRIgRT motion tracking method by integrating two primary methods: the ETLD framework and an improved Chan-Vese model (ICV), named ETLD+ICV. The TLD framework was upgraded to suit real-time cine MRI, including advanced image preprocessing, no-reference image quality assessment, an enhanced median-flow tracker, and a refined detector with dynamic search region adjustments. Additionally, ICV was combined for precise coverage of the target volume, which refined the segmented region frame by frame using tracking results, with key parameters optimized. Tested on 3.5D MRI scans from 10 patients with liver metastases, our method ensures precise tracking and accurate segmentation vital for MRIgRT. Results: An evaluation of 106,000 frames across 77 treatment fractions revealed sub-millimeter tracking errors of less than 0.8mm, with over 99% precision and 98% recall for all subjects, underscoring the robustness and efficacy of the ETLD. Moreover, the ETLD+ICV yielded a dice global score of more than 82% for all subjects, demonstrating the proposed method's extensibility and precise target volume coverage. Conclusions: This study successfully developed an automatic real-time motion tracking method for MRIgRT that markedly surpasses current methods. The novel method not only delivers exceptional precision in tracking and segmentation but also demonstrates enhanced adaptability to clinical demands, positioning it as an indispensable asset in the quest to augment the efficacy of radiotherapy treatments.

Related papers

• IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare [1.5236380958983642]
  The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Human Motion Recognition (HMR) challenges such as high computational demands, low accuracy, and limited adaptability persist. This study proposes a novel HMR method for MRHA detection, leveraging multi-stage deep learning techniques integrated with IoT.
  arXiv  Detail & Related papers  (2025-01-13T03:41:57Z)
• Data-Centric Learning Framework for Real-Time Detection of Aiming Beam in Fluorescence Lifetime Imaging Guided Surgery [3.8261910636994925]
  This study introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm) The primary challenge arises from the complex and variable conditions encountered in the surgical environment, particularly in Transoral Robotic Surgery (TORS) An instance segmentation model was developed using a data-centric training strategy that improves accuracy by minimizing label noise and enhancing detection robustness.
  arXiv  Detail & Related papers  (2024-11-11T22:04:32Z)
• Unsupervised dMRI Artifact Detection via Angular Resolution Enhancement and Cycle Consistency Learning [45.3610312584439]
  Diffusion magnetic resonance imaging (dMRI) is a crucial technique in neuroimaging studies, allowing for the non-invasive probing of the underlying structures of brain tissues. Clinical dMRI data is susceptible to various artifacts during acquisition, which can lead to unreliable subsequent analyses. We propose a novel unsupervised deep learning framework called $textbfU$n $textbfd$MRI $textbfA$rtifact $textbfD$etection via $textbfA$ngular Resolution Enhancement and $textbfC$ycle
  arXiv  Detail & Related papers  (2024-09-24T08:56:10Z)
• CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI [40.11088079783521]
  The CMRxRecon2024 dataset is the largest and most protocal-diverse publicly available cardiac k-space dataset. It is acquired from 330 healthy volunteers, covering commonly used modalities, anatomical views, and acquisition trajectories in clinical cardiac MRI.
  arXiv  Detail & Related papers  (2024-06-27T09:50:20Z)
• Improve Myocardial Strain Estimation based on Deformable Groupwise Registration with a Locally Low-Rank Dissimilarity Metric [15.682983716154403]
  The proposed method, Groupwise-LLR, performs feature tracking by iteratively updating the entire displacement field across all cardiac phases. Groupwise-LLR reduces the drift effect and provides more accurate myocardial tracking and strain estimation.
  arXiv  Detail & Related papers  (2023-11-13T14:06:44Z)
• Preserved Edge Convolutional Neural Network for Sensitivity Enhancement of Deuterium Metabolic Imaging (DMI) [10.884358837187243]
  This work presents a deep learning method for sensitivity enhancement of Deuterium Metabolic Imaging (DMI) A convolutional neural network (CNN) was designed to estimate the 2H-labeled metabolite concentrations from low SNR. The estimation precision was further improved by fine-tuning the CNN with MRI-based edge-preserving regularization for each DMI dataset.
  arXiv  Detail & Related papers  (2023-09-08T03:41:54Z)
• WarpEM: Dynamic Time Warping for Accurate Catheter Registration in EM-guided Procedures [42.72633281833923]
  This paper introduces a novel automated catheter registration method for EM-guided MIEP. The method utilizes 3D signal temporal analysis, such as Dynamic Time Warping (DTW) algorithms, to improve registration accuracy and reliability. The results indicate that the DTW method yields accurate and reliable registration outcomes, with a mean error of $2.22$mm.
  arXiv  Detail & Related papers  (2023-08-07T15:07:21Z)
• Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet [0.5106162890866905]
  We propose FocalSegNet, a novel 3D focal modulation UNet, to detect an aneurysm and offer an initial, coarse segmentation of it from time-of-flight MRA image patches. We trained and evaluated our model on a public dataset, and in terms of UIA detection, our model showed a low false-positive rate of 0.21 and a high sensitivity of 0.80.
  arXiv  Detail & Related papers  (2023-08-06T03:28:08Z)
• Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification [42.75911994044675]
  We present a novel approach for unpaired image-to-image translation of prostate MRIs and an uncertainty-aware training approach for classifying clinically significant PCa. Our approach involves a novel pipeline for translating unpaired 3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available training data. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work.
  arXiv  Detail & Related papers  (2023-07-02T05:26:54Z)
• Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions [0.0]
  Manual segmentation of stroke lesions from 3D magnetic resonance (MR) imaging volumes, the current gold standard, is not only very time-consuming, but its accuracy highly depends on the operator's experience. We have implemented and tested a fully automatic method for stroke lesion segmentation using eight different 2D-model architectures trained via transfer learning (TL) and mixed data approaches. Cross-validation results indicate that our new method can efficiently and automatically segment lesions fast and with high accuracy compared to ground truth.
  arXiv  Detail & Related papers  (2023-06-20T17:42:30Z)
• A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis [4.780348242743022]
  We present a quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework enables active acquisition of radial k-space data, in which acquisition can be stopped as soon as acquired data are sufficient to produce high quality reconstructions and segmentations.
  arXiv  Detail & Related papers  (2022-05-02T18:02:22Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Occlusion-robust Visual Markerless Bone Tracking for Computer-Assisted Orthopaedic Surgery [41.681134859412246]
  We propose a RGB-D sensing-based markerless tracking method that is robust against occlusion. By using a high-quality commercial RGB-D camera, our proposed visual tracking method achieves an accuracy of 1-2 degress and 2-4 mm on a model knee.
  arXiv  Detail & Related papers  (2021-08-24T09:49:08Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

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.