Modeling the irradiation pattern of LEDs at short distances

Enhancing endoscopic scene reconstruction with color-aware inverse rendering through neural SDF and radiance fields

Virtual surgical training is crucial for enhancing minimally invasive surgical skills. Traditional geometric reconstruction methods based on medical CT/MRI images often fall short in providing color information, which is typically generated through pseudo-coloring or artistic rendering. To simultaneously reconstruct both the geometric shape and appearance information of organs, we propose a novel organ model reconstruction network called Endoscope-NeSRF. This network jointly leverages neural radiance fields and Signed Distance Function (SDF) to reconstruct a textured geometric model of the organ of interest from multi-view photometric images acquired by an endoscope. The prior knowledge of the inverse correlation between the distance from the light source to the object and the radiance improves the real physical properties of the organ. The dilated mask further refines the appearance and geometry at the organ's edges. We also proposed a highlight adaptive optimization strategy to remove highlights caused by the light source during the acquisition process, thereby preventing the reconstruction results in areas previously affected by highlights from turning white. Finally, the real-time realistic rendering of the organ model is achieved by combining the inverse rendering and Bidirectional Reflectance Distribution Function (BRDF) rendering methods. Experimental results show that our method closely matches the Instant-NGP method in appearance reconstruction, outperforming other state-of-the-art methods, and stands as the superior method in terms of geometric reconstruction. Our method obtained a detailed geometric model and realistic appearance, providing a realistic visual sense for virtual surgical simulation, which is important for medical training.

Neural radiance fields-based multi-view endoscopic scene reconstruction for surgical simulation

PURPOSE

In virtual surgery, the appearance of 3D models constructed from CT images lacks realism, leading to potential misunderstandings among residents. Therefore, it is crucial to reconstruct realistic endoscopic scene using multi-view images captured by an endoscope.

METHODS

We propose an Endoscope-NeRF network for implicit radiance fields reconstruction of endoscopic scene under non-fixed light source, and synthesize novel views using volume rendering. Endoscope-NeRF network with multiple MLP networks and a ray transformer network represents endoscopic scene as implicit field function with color and volume density at continuous 5D vectors (3D position and 2D direction). The final synthesized image is obtained by aggregating all sampling points on each ray of the target camera using volume rendering. Our method considers the effect of distance from the light source to the sampling point on the scene radiance.

RESULTS

Our network is validated on the lung, liver, kidney and heart of pig collected by our device. The results show that the novel views of endoscopic scene synthesized by our method outperform existing methods (NeRF and IBRNet) in terms of PSNR, SSIM, and LPIPS metrics.

CONCLUSION

Our network can effectively learn a radiance field function with generalization ability. Fine-tuning the pre-trained model on a new endoscopic scene to further optimize the neural radiance fields of the scene, which can provide more realistic, high-resolution rendered images for surgical simulation.

General framework for calculating irradiance distributions of symmetric surface sources

Symmetries in system modeling can be exploited to obtain analytical results on the system behavior and to speed up computations using the symmetric model. This work explores the use of symmetries in radiant surfaces for calculating the induced irradiance distributions by developing a general mathematical expression. The obtained model is applied to flat, cylindrical, and spherical sources to obtain explicit expressions. An experimental evaluation of the flat source is carried out and compared with a traditional point source, and the obtained procedure for the flat scenario is compared with the direct integration approach, which shows an improvement in the computation time of at least two orders of magnitude with a relative root mean square error of less than 10%. The results show that the proposed approach enhances short-range predictions for extended sources. To demonstrate the impact of this in optical wireless communications we have outlined a few applications.

Comparison between Single and Multi-LED Emitters for Photodynamic Therapy: An In Vitro Study on Enterococcus faecalis and Human Gingival Fibroblasts

Aim of the study: The aim was to evaluate the effects of two LED devices, TL-01 and TL-03 in photodynamic therapy (PDT), on Enterococcus faecalis and on human gingival fibroblasts (HGFs). TL-01, characterized by a single emitter, irradiates one periodontal site at a time, whereas the multi-led device (TL-03) irradiates all vestibular sites of a single arch at a time. Methods: E. faecalis bacterial suspensions and HGFs were incubated for 45 min with Aladent gel (ALAD) containing 5-aminolevulinic acid and then exposed to LED devices (ALAD-PDT), having different distance and timing of irradiation (TL-01 N (0.5 mm, for 7 min), TL-03 N (0.5 mm, 15 min) and TL-03 F (30.0 mm, 15 min)). For bacterial suspension, the colony forming units and the live/dead staining were evaluated after 24 h, while the protoporphyrin IX (PpIX) content was monitored in all phases of the experimentation. For HGFs, the cell viability, proliferation, cell morphology, and adhesion were evaluated at 24 h. Results: Both TL-01 and TL-03 showed a significant reduction of bacterial load. The photoinactivation was inversely proportional to the PpIX accumulation. TL-01 and TL-03 promoted proliferation and adhesion of HGFs. Conclusions: Both tested devices for ALAD-PDT were equally effective in significantly reducing Enterococcus faecalis growth and in promoting HGFs proliferation and adhesion, in vitro.