835 research outputs found
GPUMLib: Deep Learning SOM Library for Surface Reconstruction
The evolution of 3D scanning devices and innovation in computer
processing power and storage capacity has sparked the revolution of
producing big point-cloud datasets. This phenomenon has becoming
an integral part of the sophisticated building design process
especially in the era of 4th Industrial Revolution. The big point-cloud
datasets have caused complexity in handling surface reconstruction
and visualization since existing algorithms are not so readily
available. In this context, the surface reconstruction intelligent
algorithms need to be revolutionized to deal with big point-cloud
datasets in tandem with the advancement of hardware processing
power and storage capacity. In this study, we propose GPUMLib –
deep learning library for self-organizing map (SOM-DLLib) to solve
problems involving big point-cloud datasets from 3D scanning
devices. The SOM-DLLib consists of multiple layers for reducing
and optimizing those big point cloud datasets. The findings show the
final objects are successfully reconstructed with optimized
neighborhood representation and the performance becomes better as
the size of point clouds increases
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography
At X-ray beamlines of synchrotron light sources, the achievable
time-resolution for 3D tomographic imaging of the interior of an object has
been reduced to a fraction of a second, enabling rapidly changing structures to
be examined. The associated data acquisition rates require sizable
computational resources for reconstruction. Therefore, full 3D reconstruction
of the object is usually performed after the scan has completed. Quasi-3D
reconstruction -- where several interactive 2D slices are computed instead of a
3D volume -- has been shown to be significantly more efficient, and can enable
the real-time reconstruction and visualization of the interior. However,
quasi-3D reconstruction relies on filtered backprojection type algorithms,
which are typically sensitive to measurement noise. To overcome this issue, we
propose Noise2Filter, a learned filter method that can be trained using only
the measured data, and does not require any additional training data. This
method combines quasi-3D reconstruction, learned filters, and self-supervised
learning to derive a tomographic reconstruction method that can be trained in
under a minute and evaluated in real-time. We show limited loss of accuracy
compared to training with additional training data, and improved accuracy
compared to standard filter-based methods
Real-Time Magnetic Resonance Imaging
Real‐time magnetic resonance imaging (RT‐MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast‐switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady‐state free precession, and single‐shot rapid acquisition with relaxation enhancement. RT‐MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft‐tissue contrast, as well as flow information. In this review, we discuss the history of RT‐MRI, fundamental tradeoffs, enabling technology, established applications, and current trends
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