A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field. This setup aggregates 2D scores at multiple camera viewpoints into a 3D score, and repurposes a pretrained 2D model for 3D data generation. We identify a technical challenge of distribution mismatch that arises in this application, and propose a novel estimation mechanism to resolve it. We run our algorithm on several off-the-shelf diffusion image generative models, including the recently released Stable Diffusion trained on the large-scale LAION dataset.
Background CT-routine MRI fusion imaging has recently become available to evaluate spinal anatomy before surgery. Due to the 3-5 mm slice thickness and non-isotropic of routine MRI sequence, the CT-routine MRI fusion imaging is not good. The MRI multiple recalled gradient echo (MERGE) sequence is potentially useful in diagnosis of lumbar degeneration disease due to the better nerve roots visualization, 1 mm slice thickness and its isotropy. Purpose The present study aimed to evaluate the image quality of CT-3D MERGE fusion images compared with CT and 3D MERGE images in patients with lumbar disc herniation. Methods Fifty-nine patients with lumbar disc herniation who underwent both lumbar CT and MRI including 3D-MERGE and routine lumbar MRI sequences were evaluated. All CT, 3D MERGE and CT-3D MERGE fusion images were separately assessed by two radiologists using five-point Likert scoring method based on five aspects: display of bony structure, intervertebral discs, nerve roots, overall anatomical details and image artifacts. Furthermore, two observers documented the sacral slope (SS), L4/5 intervertebral space heights (ISH), width and height of L4/5 intervertebral foramen (FW and FH) on CT and CT-MERGE fusion images. Results There was insufficient evidence to show a difference in bony structure score between CT and CT-3D MERGE fusion images ( p = 0.22), but it was significantly higher than that of MERGE ( p < 0.001). The scores of intervertebral discs and nerve roots between MERGE and fusion images were not statistically different ( p = 0.19 and 0.88), which were higher than CT (all p < 0.001). The overall anatomical detail score of fusion imaging was higher than CT and MERGE ( p < 0.001). No significant difference of image artifacts score was found among CT, MERGE and fusion images ( p = 0.47). There was no significant difference in SS, ISH, FW, FH values between CT and fusion images (all p > 0.05). Conclusion CT-3D MERGE fusion images exhibit superior image quality to both CT and 3D MERGE for the simultaneous observation of bony structures, intervertebral discs, and nerve roots.
AIDS, or acquired immunodeficiency syndrome, is caused by the human immunodeficiency virus (HIV), which was first reported in the United States in 1981. HIV infection has spread almost globally and has become a worldwide problem that poses a serious threat to human health.
Solar power has become one of the most important renewable resources due to the energy crisis recently.An intelligent solar LED lighting driver system based on embedded technology was designed to make full use of the green energy.Electricity which was detected real-timely was generated by solar cells.Boost-Buck DC/DC converter was controlled by PWM to realize the control of Maximum Power Point Tracking Charge and the intelligent management to the batteries.Besides, LED was adopted to improve the energy efficiency, working reliability, practicality and environment protection.The new system is efficient, stable and practical.It can be widely applied in all kinds of embedded devices to supply the effective and environment protective energy.
A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field. This setup aggregates 2D scores at multiple camera viewpoints into a 3D score, and re-purposes a pretrained 2D model for 3D data generation. We identify a technical challenge of distribution mismatch that arises in this application, and propose a novel estimation mechanism to resolve it. We run our algorithm on several off-the-shelf diffusion image generative models, including the recently released Stable Diffusion trained on the large-scale LAION 5B dataset.
This paper introduces a computer and camera-based intelligent monitoring system, achieving real-time site monitoring, intelligence. In the case of low-cost, relying on image recognition, the system begin to record when an intruder, and the record is to stop after the invasion, you can save a lot of storage space. And the system does not depend on the camera driver, suitable for any camera.
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