30 research outputs found
Oral-3Dv2: 3D Oral Reconstruction from Panoramic X-Ray Imaging with Implicit Neural Representation
3D reconstruction of medical imaging from 2D images has become an
increasingly interesting topic with the development of deep learning models in
recent years. Previous studies in 3D reconstruction from limited X-ray images
mainly rely on learning from paired 2D and 3D images, where the reconstruction
quality relies on the scale and variation of collected data. This has brought
significant challenges in the collection of training data, as only a tiny
fraction of patients take two types of radiation examinations in the same
period. Although simulation from higher-dimension images could solve this
problem, the variance between real and simulated data could bring great
uncertainty at the same time. In oral reconstruction, the situation becomes
more challenging as only a single panoramic X-ray image is available, where
models need to infer the curved shape by prior individual knowledge. To
overcome these limitations, we propose Oral-3Dv2 to solve this cross-dimension
translation problem in dental healthcare by learning solely on projection
information, i.e., the projection image and trajectory of the X-ray tube. Our
model learns to represent the 3D oral structure in an implicit way by mapping
2D coordinates into density values of voxels in the 3D space. To improve
efficiency and effectiveness, we utilize a multi-head model that predicts a
bunch of voxel values in 3D space simultaneously from a 2D coordinate in the
axial plane and the dynamic sampling strategy to refine details of the density
distribution in the reconstruction result. Extensive experiments in simulated
and real data show that our model significantly outperforms existing
state-of-the-art models without learning from paired images or prior individual
knowledge. To the best of our knowledge, this is the first work of a
non-adversarial-learning-based model in 3D radiology reconstruction from a
single panoramic X-ray image
PartDiff: Image Super-resolution with Partial Diffusion Models
Denoising diffusion probabilistic models (DDPMs) have achieved impressive
performance on various image generation tasks, including image
super-resolution. By learning to reverse the process of gradually diffusing the
data distribution into Gaussian noise, DDPMs generate new data by iteratively
denoising from random noise. Despite their impressive performance,
diffusion-based generative models suffer from high computational costs due to
the large number of denoising steps.In this paper, we first observed that the
intermediate latent states gradually converge and become indistinguishable when
diffusing a pair of low- and high-resolution images. This observation inspired
us to propose the Partial Diffusion Model (PartDiff), which diffuses the image
to an intermediate latent state instead of pure random noise, where the
intermediate latent state is approximated by the latent of diffusing the
low-resolution image. During generation, Partial Diffusion Models start
denoising from the intermediate distribution and perform only a part of the
denoising steps. Additionally, to mitigate the error caused by the
approximation, we introduce "latent alignment", which aligns the latent between
low- and high-resolution images during training. Experiments on both magnetic
resonance imaging (MRI) and natural images show that, compared to plain
diffusion-based super-resolution methods, Partial Diffusion Models
significantly reduce the number of denoising steps without sacrificing the
quality of generation
Design and analysis of actuator system of electromagnetic shell with high-overload resistances
The components of an electromagnetic shell system should be able to sustain the impact of high-strength instantaneous acceleration when the system is launched. The dynamic characteristics of high overload present significant challenges in the component (electronic and mechanical) design and part assembly of a steering gear system. This paper proposes a new design strategy for the servo system of a high-overload electromagnetic projectile. First, according to the special environment index parameters of a high-overload electromagnetic shell steering system, a new anti-overload deceleration mechanism that combines a triangular thread lead screw, a shift fork, and the entire anti-high-overload mechanical structure is proposed. The transient dynamic vibration characteristics of the entire high overload are analyzed. Based on the integrated module method for complex mechanical and electrical equipment, a mathematical model of the full closed-loop electromagnetic shell actuator system is established, and its dynamic characteristics are analyzed. Finally, a prototype of the high-overload electromagnetic projectile steering system is manufactured. By testing the maximum rudder deflection angle and the frequency and step responses of the system, the dynamic characteristics of the new high-overload electromagnetic shell actuator system are verified. This study provides a new method for designing high-overload electromagnetic shell steering gears
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI
Prostate cancer is the second leading cause of cancer death among men in the
United States. The diagnosis of prostate MRI often relies on the accurate
prostate zonal segmentation. However, state-of-the-art automatic segmentation
methods often fail to produce well-contained volumetric segmentation of the
prostate zones since certain slices of prostate MRI, such as base and apex
slices, are harder to segment than other slices. This difficulty can be
overcome by accounting for the cross-slice relationship of adjacent slices, but
current methods do not fully learn and exploit such relationships. In this
paper, we propose a novel cross-slice attention mechanism, which we use in a
Transformer module to systematically learn the cross-slice relationship at
different scales. The module can be utilized in any existing learning-based
segmentation framework with skip connections. Experiments show that our
cross-slice attention is able to capture the cross-slice information in
prostate zonal segmentation and improve the performance of current
state-of-the-art methods. Our method significantly improves segmentation
accuracy in the peripheral zone, such that the segmentation results are
consistent across all the prostate slices (apex, mid-gland, and base)