SU-C-204-04: Patient Specific Proton Stopping Powers Estimation by Combining Proton Radiography and Prior-Knowledge X-Ray CT Information

2015 ◽  
Vol 42 (6Part2) ◽  
pp. 3199-3199 ◽  
Author(s):  
CA Collins-Fekete ◽  
S Brousmiche ◽  
D Hansen ◽  
L Beaulieu ◽  
J Seco
Keyword(s):  
Prior Knowledge ◽  
Patient Specific ◽  
Proton Radiography ◽  
X Ray

Contrast-Enhanced Proton Radiography for Patient Set-up by Using X-Ray CT Prior Knowledge

2014 ◽  
Vol 90 (3) ◽  
pp. 628-636 ◽  
Author(s):  
Maria Francesca Spadea ◽  
Aurora Fassi ◽  
Paolo Zaffino ◽  
Marco Riboldi ◽  
Guido Baroni ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Proton Radiography ◽  
X Ray ◽  
Contrast Enhanced ◽  
Set Up

Pre-treatment patient-specific stopping power by combining list-mode proton radiography and x-ray CT

2017 ◽  
Vol 62 (17) ◽  
pp. 6836-6852 ◽  
Author(s):  
Charles-Antoine Collins-Fekete ◽  
Sébastien Brousmiche ◽  
David C Hansen ◽  
Luc Beaulieu ◽  
Joao Seco
Keyword(s):  
Stopping Power ◽  
Patient Specific ◽  
List Mode ◽  
Proton Radiography ◽  
X Ray ◽  
Treatment Patient ◽  
Pre Treatment

Development and Pre‐Clinical Evaluation of a Patient‐Specific High Energy X‐ray Phase Sensitive Breast Tomosynthesis System

2021 ◽  
Author(s):  
Muhammad U. Ghani ◽  
Xizeng Wu ◽  
Laurie L. Fajardo ◽  
Zhengxue Jing ◽  
Molly D. Wong ◽  
...  
Keyword(s):  
Clinical Evaluation ◽  
High Energy ◽  
Patient Specific ◽  
Breast Tomosynthesis ◽  
X Ray ◽  
Phase Sensitive

scholarly journals Hybrid Spine Simulator Prototype for X-ray Free Pedicle Screws Fixation Training

2021 ◽  
Vol 11 (3) ◽  
pp. 1038
Author(s):  
Sara Condino ◽  
Giuseppe Turini ◽  
Virginia Mamone ◽  
Paolo Domenico Parchi ◽  
Vincenzo Ferrari
Keyword(s):  
Augmented Reality ◽  
Low Cost ◽  
Lumbar Vertebrae ◽  
Pedicle Screws ◽  
Simulation Software ◽  
Patient Specific ◽  
Innovative Strategy ◽  
X Ray ◽  
3D Printed ◽  
Harmful Radiation

Simulation for surgical training is increasingly being considered a valuable addition to traditional teaching methods. 3D-printed physical simulators can be used for preoperative planning and rehearsal in spine surgery to improve surgical workflows and postoperative patient outcomes. This paper proposes an innovative strategy to build a hybrid simulation platform for training of pedicle screws fixation: the proposed method combines 3D-printed patient-specific spine models with augmented reality functionalities and virtual X-ray visualization, thus avoiding any exposure to harmful radiation during the simulation. Software functionalities are implemented by using a low-cost tracking strategy based on fiducial marker detection. Quantitative tests demonstrate the accuracy of the method to track the vertebral model and surgical tools, and to coherently visualize them in either the augmented reality or virtual fluoroscopic modalities. The obtained results encourage further research and clinical validation towards the use of the simulator as an effective tool for training in pedicle screws insertion in lumbar vertebrae.

On-device Prior Knowledge Incorporated Learning for Personalized Atrial Fibrillation Detection

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Zhenge Jia ◽  
Yiyu Shi ◽  
Samir Saba ◽  
Jingtong Hu
Keyword(s):  
Atrial Fibrillation ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Fine Tuning ◽  
Cardiac Monitoring ◽  
Patient Specific ◽  
Detection Accuracy ◽  
Specific Patient ◽  
Deep Model ◽  
Tuning Strategy

Atrial Fibrillation (AF), one of the most prevalent arrhythmias, is an irregular heart-rate rhythm causing serious health problems such as stroke and heart failure. Deep learning based methods have been exploited to provide an end-to-end AF detection by automatically extracting features from Electrocardiogram (ECG) signal and achieve state-of-the-art results. However, the pre-trained models cannot adapt to each patient’s rhythm due to the high variability of rhythm characteristics among different patients. Furthermore, the deep models are prone to overfitting when fine-tuned on the limited ECG of the specific patient for personalization. In this work, we propose a prior knowledge incorporated learning method to effectively personalize the model for patient-specific AF detection and alleviate the overfitting problems. To be more specific, a prior-incorporated portion importance mechanism is proposed to enforce the network to learn to focus on the targeted portion of the ECG, following the cardiologists’ domain knowledge in recognizing AF. A prior-incorporated regularization mechanism is further devised to alleviate model overfitting during personalization by regularizing the fine-tuning process with feature priors on typical AF rhythms of the general population. The proposed personalization method embeds the well-defined prior knowledge in diagnosing AF rhythm into the personalization procedure, which improves the personalized deep model and eliminates the workload of manually adjusting parameters in conventional AF detection method. The prior knowledge incorporated personalization is feasibly and semi-automatically conducted on the edge, device of the cardiac monitoring system. We report an average AF detection accuracy of 95.3% of three deep models over patients, surpassing the pre-trained model by a large margin of 11.5% and the fine-tuning strategy by 8.6%.

Investigation of time-resolved proton radiography using x-ray flat-panel imaging system

2017 ◽  
Vol 62 (5) ◽  
pp. 1905-1919 ◽  
Author(s):  
K-W Jee ◽  
R Zhang ◽  
E H Bentefour ◽  
P J Doolan ◽  
E Cascio ◽  
...  
Keyword(s):  
Imaging System ◽  
Flat Panel ◽  
Proton Radiography ◽  
Time Resolved ◽  
X Ray ◽  
Flat Panel Imaging
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