Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion
- URL: http://arxiv.org/abs/2507.01909v3
- Date: Wed, 09 Jul 2025 16:36:58 GMT
- Title: Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion
- Authors: Jorge Tapias Gomez, Nishant Nadkarni, Lando S. Bosma, Jue Jiang, Ergys D. Subashi, William P. Segars, James M. Balter, Mert R Sabuncu, Neelam Tyagi, Harini Veeraraghavan,
- Abstract summary: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics.<n>Patient-specific digital twins (DTs) modeling temporally varying motion were created to assess the accuracy of DIR methods.
- Score: 8.884066499888718
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DT) modeling temporally varying motion were created to assess the accuracy of DIR methods. Approach: 21 motion phases simulating digestive GI motion as 4D sequences were generated from static 3D patient scans using published analytical GI motion models through a semi-automated pipeline. Eleven datasets, including six T2w FSE MRI (T2w MRI), two T1w 4D golden-angle stack-of-stars, and three contrast-enhanced CT scans. The motion amplitudes of the DTs were assessed against real patient stomach motion amplitudes extracted from independent 4D MRI datasets. The generated DTs were then used to assess six different DIR methods using target registration error, Dice similarity coefficient, and the 95th percentile Hausdorff distance using summary metrics and voxel-level granular visualizations. Finally, for a subset of T2w MRI scans from patients treated with MR-guided radiation therapy, dose distributions were warped and accumulated to assess dose warping errors, including evaluations of DIR performance in both low- and high-dose regions for patient-specific error estimation. Main results: Our proposed pipeline synthesized DTs modeling realistic GI motion, achieving mean and maximum motion amplitudes and a mean log Jacobian determinant within 0.8 mm and 0.01, respectively, similar to published real-patient gastric motion data. It also enables the extraction of detailed quantitative DIR performance metrics and rigorous validation of dose mapping accuracy. Significance: The pipeline enables rigorously testing DIR tools for dynamic, anatomically complex regions enabling granular spatial and dosimetric accuracies.
Related papers
- DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images [4.117232425638352]
DIMA (DIffusing Motion Artifacts) is a novel framework that leverages diffusion models to enable unsupervised motion artifact correction in brain MRI.<n>Our two-phase approach first trains a diffusion model on unpaired motion-affected images to learn the distribution of motion artifacts.<n>This model then generates realistic motion artifacts on clean images, creating paired datasets suitable for supervised training of correction networks.
arXiv Detail & Related papers (2025-04-09T10:43:38Z) - Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging [70.66500060987312]
Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules.
This work proposes DirGeo-DTI, a deep learning-based method to estimate reliable DTI metrics even from a set of DWIs acquired with the minimum theoretical number (6) of gradient directions.
arXiv Detail & Related papers (2024-09-11T11:12:26Z) - SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data [1.980639720136382]
Traditional MRI scans often yield anisotropic data due to technical constraints.<n>Super-resolution techniques aim to address these limitations by reconstructing isotropic high-resolution images from anisotropic data.<n>We introduce SIMPLE,'' a Simultaneous Multi-Plane Self-Supervised Learning approach for isotropic MRI restoration from anisotropic data.
arXiv Detail & Related papers (2024-08-23T13:48:11Z) - Extraction of 3D trajectories of mandibular condyles from 2D real-time MRI [2.1001649486621137]
Real-time MRI enables the extraction of condylar trajectories with sufficient accuracy for evaluating clinically relevant parameters.
The segmentation of the sagittal slices required some fine-tuning.
The difference in the superior-inferior coordinate of the condyles in the closed jaw position was 1.7 mm on average.
arXiv Detail & Related papers (2024-06-21T07:35:40Z) - 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) - Towards Automatic Scoring of Spinal X-ray for Ankylosing Spondylitis [4.310687588548587]
manually grading structural changes with the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) on spinal X-ray imaging is costly and time-consuming.
We propose a 2-step auto-grading pipeline, called VertXGradeNet, to automatically predict mSASSS scores for the cervical and lumbar vertebral units (VUs) in X-ray spinal imaging.
arXiv Detail & Related papers (2023-08-08T19:59:23Z) - Validated respiratory drug deposition predictions from 2D and 3D medical
images with statistical shape models and convolutional neural networks [47.187609203210705]
We aim to develop and validate an automated computational framework for patient-specific deposition modelling.
An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
arXiv Detail & Related papers (2023-03-02T07:47:07Z) - Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of
Articulated Motion [48.52403516006036]
This paper proposes a vision-based approach allowing autonomous robotic US limb scanning.
To this end, an atlas MRI template of a human arm with annotated vascular structures is used to generate trajectories.
In all cases, the system can successfully acquire the planned vascular structure on volunteers' limbs.
arXiv Detail & Related papers (2022-08-10T15:39:20Z) - Automatic lesion detection, segmentation and characterization via 3D
multiscale morphological sifting in breast MRI [3.4400216692203998]
We present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention.
The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification.
Compared with previously proposed systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.
arXiv Detail & Related papers (2020-07-07T04:39:13Z) - 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) - A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions
Artefacts in MRI Scans [63.28835187934139]
We propose a novel method for removing motion artefacts using a deep neural network with two input branches.
The proposed method can be applied to artefacts generated by multiple movements of the patient.
arXiv Detail & Related papers (2020-06-24T15:25:11Z) - Appearance Learning for Image-based Motion Estimation in Tomography [60.980769164955454]
In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals.
Patient motion corrupts the geometry alignment in the reconstruction process resulting in motion artifacts.
We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object.
arXiv Detail & Related papers (2020-06-18T09:49:11Z)
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.