TH-AB-201-01: A Real-Time Skin-Dose Mapping System for Region-Of-Interest (ROI) Fluoroscopy

Purpose: Augmented reality overlay systems can be used to project a CT image directly onto a patient during procedures. They have been actively trialed for computer-guided procedures, however they have not become commonplace in practice due to restrictions of previous systems. Previous systems have not been handheld, and have had complicated calibration procedures. We put forward a handheld tablet-based system for assisting with needle interventions. Methods: The system consists of a tablet display and a 3-D printed reusable and customizable frame. A simple and accurate calibration method was designed to align the patient to the projected image. The entire system is tracked via camera, with respect to the patient, and the projected image is updated in real time as the system is moved around the region of interest. Results: The resulting system allowed for 0.99mm mean position error in the plane of the image, and a mean position error of 0.61mm out of the plane of the image. This accuracy was thought to be clinically acceptable for tool using computer-guidance in several procedures that involve musculoskeletal needle placements. Conclusion: Our calibration method was developed and tested using the designed handheld system. Our results illustrate the potential for the use of augmented reality handheld systems in computer-guided needle procedures.

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Real-Time Runway Detection for Infrared Aerial Image Using Synthetic Vision and an ROI Based Level Set Method

Remote Sensing ◽

10.3390/rs10101544 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1544 ◽

Cited By ~ 1

Author(s):

Changjiang Liu ◽

Irene Cheng ◽

Anup Basu

Keyword(s):

Real Time ◽

Level Set Method ◽

Level Set ◽

Infrared Image ◽

Region Of Interest ◽

Aerial Image ◽

Real Time Processing ◽

Synthetic Vision ◽

Level Set Function ◽

Rough Region

We present a new method for real-time runway detection embedded in synthetic vision and an ROI (Region of Interest) based level set method. A virtual runway from synthetic vision provides a rough region of an infrared runway. A three-thresholding segmentation is proposed following Otsu’s binarization method to extract a runway subset from this region, which is used to construct an initial level set function. The virtual runway also gives a reference area of the actual runway in an infrared image, which helps us design a stopping criterion for the level set method. In order to meet the needs of real-time processing, the ROI based level set evolution framework is implemented in this paper. Experimental results show that the proposed algorithm is efficient and accurate.

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Real-time rate distortion-optimized image compression with region of interest on the ARM architecture for underwater robotics applications

Journal of Real-Time Image Processing ◽

10.1007/s11554-018-0833-5 ◽

2018 ◽

Vol 16 (1) ◽

pp. 193-225 ◽

Cited By ~ 3

Author(s):

Eduardo M. Rubino ◽

Alberto J. Álvares ◽

Raúl Marín ◽

Pedro J. Sanz

Keyword(s):

Image Compression ◽

Real Time ◽

Region Of Interest ◽

Rate Distortion ◽

Underwater Robotics ◽

Time Rate

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Determinants of Real-Time fMRI Neurofeedback Performance and Improvement – a Machine Learning Mega-Analysis

10.1101/2020.10.21.349118 ◽

2020 ◽

Author(s):

Amelie Haugg ◽

Fabian M. Renz ◽

Andrew A. Nicholson ◽

Cindy Lor ◽

Sebastian J. Götzendorfer ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Clinical Symptoms ◽

Gain Control ◽

Region Of Interest ◽

Open Science ◽

Imagery Task ◽

Neurofeedback Training ◽

Brain Signals ◽

Healthy Participants

AbstractReal-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments.With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

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