Organ-based estimation and minimization of clinician's X-ray dose.

PURPOSE: Hybrid surgeries, allowing real-time visualization of patient inner anatomy, are possible through the use of intraoperative X-ray imaging. However, the intensive use of X-rays can have undesired consequences for the clinicians or the patient in the operating room (OR). METHODS: In this paper, we provide a tool to visualize the X-rays and to optimally place protective shields in the hybrid operating room to reduce the clinician's dose according to their most sensitive body parts. We first acquire measurements in a hybrid operating room with dosimeters placed at different locations on a mannequin simulating a clinician. We demonstrate that a small displacement of a protective shield has significant consequences on the dose received by a clinician. Then, we reproduce the scene virtually and use Monte Carlo simulations to estimate the dose received by the clinician. Finally, we optimally place protective shields with a Nelder-Mead-based numerical optimization algorithm. RESULTS: The results show a high sensitivity of the clinician's dose to protective shield placement. Numerical optimization of the shields' placement can help to reduce the dose and show a decrease between 79 and 89% of the exposition when comparing no external shield protection and our optimal external shield position. CONCLUSION: Our work can help to raise awareness of the risks induced by X-rays during intraoperative surgery and reduce the dose received by the clinicians. In future work, our approach can be linked with human pose estimation algorithms to trace surgeons' moves, estimate dynamically the dose and summarize it in a surgical report, giving the dose for important organs.

Multimodal imaging as optical biopsy system for gastritis diagnosis in humans, and input of the mouse model.

BACKGROUND: Gastric inflammation is a major risk factor for gastric cancer. Current endoscopic methods are not able to efficiently detect and characterize gastric inflammation, leading to a sub-optimal patients' care. New non-invasive methods are needed. Reflectance mucosal light analysis is of particular interest in this context. The aim of our study was to analyze reflectance light and specific autofluorescence signals, both in humans and in a mouse model of gastritis. METHODS: We recruited patients undergoing gastroendoscopic procedure during which reflectance was analysed with a multispectral camera. In parallel, the gastritis mouse model of Helicobacter pylori infection was used to investigate reflectance from ex vivo gastric samples using a spectrometer. In both cases, autofluorescence signals were measured using a confocal microscope. FINDINGS: In gastritis patients, reflectance modifications were significant in near-infrared spectrum, with a decrease between 610 and 725 nm and an increase between 750 and 840 nm. Autofluorescence was also modified, showing variations around 550 nm of emission. In H. pylori infected mice developing gastric inflammatory lesions, we observed significant reflectance modifications 18 months after infection, with increased intensity between 617 and 672 nm. Autofluorescence was significantly modified after 1, 3 and 6 months around 550 and 630 nm. Both in human and in mouse, these reflectance data can be considered as biomarkers and accurately predicted inflammatory state. INTERPRETATION: In this pilot study, using a practical measuring device, we identified in humans, modification of reflectance spectra in the visible spectrum and for the first time in near-infrared, associated with inflammatory gastric states. Furthermore, both in the mouse model and humans, we also observed modifications of autofluorescence associated with gastric inflammation. These innovative data pave the way to deeper validation studies on larger cohorts, for further development of an optical biopsy system to detect gastritis and finally to better surveil this important gastric cancer risk factor. FUNDING: The project was funded by the ANR EMMIE (ANR-15-CE17-0015) and the French Gastroenterology Society (SNFGE).

Intrinsic RGB and multispectral images recovery by independent quadratic programming.

This work introduces a method to estimate reflectance, shading, and specularity from a single image. Reflectance, shading, and specularity are intrinsic images derived from the dichromatic model. Estimation of these intrinsic images has many applications in computer vision such as shape recovery, specularity removal, segmentation, or classification. The proposed method allows for recovering the dichromatic model parameters thanks to two independent quadratic programming steps. Compared to the state of the art in this domain, our approach has the advantage to address a complex inverse problem into two parallelizable optimization steps that are easy to solve and do not require learning. The proposed method is an extension of a previous algorithm that is rewritten to be numerically more stable, has better quantitative and qualitative results, and applies to multispectral images. The proposed method is assessed qualitatively and quantitatively on standard RGB and multispectral datasets.