scholarly journals Comparison of visual biofeedback system with a guiding waveform and abdomen-chest motion self-control system for respiratory motion management

2016 ◽  
Vol 57 (4) ◽  
pp. 387-392 ◽  
Author(s):  
Yujiro Nakajima ◽  
Noriyuki Kadoya ◽  
Takayuki Kanai ◽  
Kengo Ito ◽  
Kiyokazu Sato ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Wave Model ◽  
Free Breathing ◽  
Self Control ◽  
Patient Specific ◽  
Motion Management ◽  
Visual Biofeedback ◽  
Respiratory Management ◽  
Biofeedback System ◽  
Clinical Benefits

Abstract Irregular breathing can influence the outcome of 4D computed tomography imaging and cause artifacts. Visual biofeedback systems associated with a patient-specific guiding waveform are known to reduce respiratory irregularities. In Japan, abdomen and chest motion self-control devices (Abches) (representing simpler visual coaching techniques without a guiding waveform) are used instead; however, no studies have compared these two systems to date. Here, we evaluate the effectiveness of respiratory coaching in reducing respiratory irregularities by comparing two respiratory management systems. We collected data from 11 healthy volunteers. Bar and wave models were used as visual biofeedback systems. Abches consisted of a respiratory indicator indicating the end of each expiration and inspiration motion. Respiratory variations were quantified as root mean squared error (RMSE) of displacement and period of breathing cycles. All coaching techniques improved respiratory variation, compared with free-breathing. Displacement RMSEs were 1.43 ± 0.84, 1.22 ± 1.13, 1.21 ± 0.86 and 0.98 ± 0.47 mm for free-breathing, Abches, bar model and wave model, respectively. Period RMSEs were 0.48 ± 0.42, 0.33 ± 0.31, 0.23 ± 0.18 and 0.17 ± 0.05 s for free-breathing, Abches, bar model and wave model, respectively. The average reduction in displacement and period RMSE compared with the wave model were 27% and 47%, respectively. For variation in both displacement and period, wave model was superior to the other techniques. Our results showed that visual biofeedback combined with a wave model could potentially provide clinical benefits in respiratory management, although all techniques were able to reduce respiratory irregularities.

Cardiac Resynchronisation Therapy – Evolving Strategies to Enhance Response

2010 ◽  
Vol 6 (1) ◽  
pp. 83
Author(s):  
Jagmeet P Singh ◽  
Keyword(s):  
Heart Failure ◽  
Systolic Heart Failure ◽  
Cardiac Resynchronisation Therapy ◽  
Left Ventricular ◽  
Patient Specific ◽  
Cardiac Resynchronisation ◽  
Clinical Benefits ◽  
Pacing Lead ◽  
Resynchronisation Therapy ◽  
Physiological Impact

Cardiac resynchronisation therapy (CRT) has gained widespread acceptance as a safe and effective therapeutic strategy for congestive heart failure (CHF) refractory to optimal medical therapy. The use of implantable devices has substantially altered the natural history of systolic heart failure. These devices exert their physiological impact through ventricular remodelling, associated with a reduction in left ventricular (LV) volumes and an improvement in ejection fraction (EF). Several prospective randomised studies have shown that this in turn translates into long-term clinical benefits such as improved quality of life, increased functional capacity and reduction in hospitalisation for heart failure and overall mortality. Despite these obvious benefits, there remain more than a few unresolved concerns, the most important being that up to one-third of patients treated with CRT do not derive any detectable benefit. There are several determinants of successful delivery and response to CRT, including selecting the appropriate patient, patient-specific optimal LV pacing lead placement and appropriate post-implant device care and follow-up. This article highlights the importance of collectively working on all of these aspects of CRT to enhance and maximise response.

scholarly journals Audio-Visual Biofeedback for Respiratory Motion Management: Comparison of the Reproducibility of Breath-Holding between Visual and Audio Guidance

2018 ◽  
Vol 09 (13) ◽  
pp. 2286-2294
Author(s):  
Naoki Sano ◽  
Masahide Saito ◽  
Hiroshi Onishi ◽  
Kengo Kuriyama ◽  
Takafumi Komiyama ◽  
...  
Keyword(s):  
Respiratory Motion ◽  
Breath Holding ◽  
Motion Management ◽  
Visual Biofeedback

Development of real time abdominal compression force monitoring and visual biofeedback system

2018 ◽  
Vol 63 (5) ◽  
pp. 055014
Author(s):  
Tae-Ho Kim ◽  
Siyong Kim ◽  
Dong-Su Kim ◽  
Seong-Hee Kang ◽  
Min-Seok Cho ◽  
...  
Keyword(s):  
Real Time ◽  
Compression Force ◽  
Abdominal Compression ◽  
Visual Biofeedback ◽  
Biofeedback System ◽  
Force Monitoring

Quasi-breath-hold technique using personalized audio-visual biofeedback for respiratory motion management in radiotherapy

2011 ◽  
Vol 38 (6Part1) ◽  
pp. 3114-3124 ◽  
Author(s):  
Yang-Kyun Park ◽  
Siyong Kim ◽  
Hwiyoung Kim ◽  
II Han Kim ◽  
Kunwoo Lee ◽  
...  
Keyword(s):  
Respiratory Motion ◽  
Breath Hold ◽  
Motion Management ◽  
Visual Biofeedback

scholarly journals Patient-specific prediction of SEEG electrode bending for stereotactic neurosurgical planning

2021 ◽  
Author(s):  
Alejandro Granados ◽  
Yuxuan Han ◽  
Oeslle Lucena ◽  
Vejay Vakharia ◽  
Roman Rodionov ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Brain Tissue ◽  
Mean Squared Error ◽  
Brain Structures ◽  
Patient Specific ◽  
Target Point ◽  
Computer Assisted ◽  
Local Displacement ◽  
Neurosurgical Planning ◽  
Future Work

Abstract Purpose  Electrode bending observed after stereotactic interventions is typically not accounted for in either computer-assisted planning algorithms, where straight trajectories are assumed, or in quality assessment, where only metrics related to entry and target points are reported. Our aim is to provide a fully automated and validated pipeline for the prediction of stereo-electroencephalography (SEEG) electrode bending. Methods  We transform electrodes of 86 cases into a common space and compare features-based and image-based neural networks on their ability to regress local displacement ($$\mathbf{lu} $$ lu ) or electrode bending ($$\hat{\mathbf{eb }}$$ eb ^ ). Electrodes were stratified into six groups based on brain structures at the entry and target point. Models, both with and without Monte Carlo (MC) dropout, were trained and validated using tenfold cross-validation. Results  mage-based models outperformed features-based models for all groups, and models that predicted $$\mathbf{lu} $$ lu performed better than for $$\hat{\mathbf{eb }}$$ eb ^ . Image-based model prediction with MC dropout resulted in lower mean squared error (MSE) with improvements up to 12.9% ($$\mathbf{lu} $$ lu ) and 39.9% ($$\hat{\mathbf{eb }}$$ eb ^ ), compared to no dropout. Using an image of brain tissue types (cortex, white and deep grey matter) resulted in similar, and sometimes better performance, compared to using a T1-weighted MRI when predicting $$\mathbf{lu} $$ lu . When inferring trajectories of image-based models (brain tissue types), 86.9% of trajectories had an MSE$$\le 1$$ ≤ 1  mm. Conclusion  An image-based approach regressing local displacement with an image of brain tissue types resulted in more accurate electrode bending predictions compared to other approaches, inputs, and outputs. Future work will investigate the integration of electrode bending into planning and quality assessment algorithms.

scholarly journals Deep Learning Schätzung absorbierter Strahlungsdosis für die nuklearmedizinische Diagnostik

2020 ◽  
Author(s):  
Luciano Melodia
Keyword(s):  
Monte Carlo ◽  
Deep Learning ◽  
Mean Squared Error ◽  
Imaging Techniques ◽  
Whole Body ◽  
Patient Specific ◽  
Whole Body Ct ◽  
Squared Error ◽  
Future Work ◽  
First Time

The distribution of energy dose from Lu177 radiotherapy can be estimated by convolving an image of a time-integrated activity distribution with a dose voxel kernel (DVK) consisting of different types of tissues. This fast and inacurate approximation is inappropriate for personalized dosimetry as it neglects tissue heterogenity. The latter can be calculated using different imaging techniques such as CT and SPECT combined with a time consuming monte-carlo simulation. The aim of this study is, for the first time, an estimation of DVKs from CT-derived density kernels (DK) via deep learning in convolutional neural networks (CNNs). The proposed CNN achieved, on the test set, a mean intersection over union (IOU) of =0.86 after 308 epochs and a corresponding mean squared error (MSE) =1.24⋅10−4. This generalization ability shows that the trained CNN can indeed learn the difficult transfer function from DK to DVK. Future work will evaluate DVKs estimated by CNNs with full monte-carlo simulations of a whole body CT to predict patient specific voxel dose maps.

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