scholarly journals Optimal virtual monoenergetic image in “TwinBeam” dual-energy CT for organs-at-risk delineation based on contrast-noise-ratio in head-and-neck radiotherapy

2019 ◽  
Vol 20 (2) ◽  
pp. 121-128 ◽  
Author(s):  
Tonghe Wang ◽  
Beth Bradshaw Ghavidel ◽  
Jonathan J. Beitler ◽  
Xiangyang Tang ◽  
Yang Lei ◽  
...  
Keyword(s):  
At Risk ◽  
Head And Neck ◽  
Organs At Risk ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Noise Ratio ◽  
Head And Neck Radiotherapy ◽  
Monoenergetic Image

scholarly journals Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Brent van der Heyden ◽  
Patrick Wohlfahrt ◽  
Daniëlle B. P. Eekers ◽  
Christian Richter ◽  
Karin Terhaag ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Brain Tumor ◽  
Organs At Risk ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Learning Approach ◽  
Tumor Patients

scholarly journals OC-0423: An evaluation of variabilities in organs-at-risk delineation for MR-only head and neck radiotherapy

2018 ◽  
Vol 127 ◽  
pp. S221-S222
Author(s):  
K.Y. Chui ◽  
W.W.K. Fung ◽  
J. Yuan ◽  
A.W.L. Mui ◽  
G. Chiu
Keyword(s):  
At Risk ◽  
Head And Neck ◽  
Organs At Risk ◽  
Head And Neck Radiotherapy

scholarly journals Doses in organs at risk during head and neck radiotherapy using IMRT and 3D-CRT

2012 ◽  
Vol 46 (4) ◽  
Author(s):  
Magdalena Peszynska-Piorun ◽  
Julian Malicki ◽  
Wojciech Golusinski
Keyword(s):  
At Risk ◽  
Head And Neck ◽  
Organs At Risk ◽  
Head And Neck Radiotherapy ◽  
3D Crt

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

2020 ◽  
Vol 47 (9) ◽  
Author(s):  
Tomaž Vrtovec ◽  
Domen Močnik ◽  
Primož Strojan ◽  
Franjo Pernuš ◽  
Bulat Ibragimov
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Head And Neck ◽  
Organs At Risk ◽  
Radiotherapy Planning ◽  
Learning Methods ◽  
Head And Neck Radiotherapy

scholarly journals Threshold-dependent iodine imaging and spectral separation in a whole-body photon-counting CT system

2021 ◽  
Author(s):  
S. Sawall ◽  
L. Klein ◽  
E. Wehrse ◽  
L. T. Rotkopf ◽  
C. Amato ◽  
...  
Keyword(s):  
Image Quality ◽  
Photon Counting ◽  
Dual Energy ◽  
Whole Body ◽  
Dual Energy Ct ◽  
Post Mortem ◽  
Tube Voltage ◽  
Spectral Separation ◽  
Dual Source ◽  
Noise Ratio

Abstract Objective To evaluate the dual-energy (DE) performance and spectral separation with respect to iodine imaging in a photon-counting CT (PCCT) and compare it to dual-source CT (DSCT) DE imaging. Methods A semi-anthropomorphic phantom extendable with fat rings equipped with iodine vials is measured in an experimental PCCT. The system comprises a PC detector with two energy bins (20 keV, T) and (T, eU) with threshold T and tube voltage U. Measurements using the PCCT are performed at all available tube voltages (80 to 140 kV) and threshold settings (50–90 keV). Further measurements are performed using a conventional energy-integrating DSCT. Spectral separation is quantified as the relative contrast media ratio R between the energy bins and low/high images. Image noise and dose-normalized contrast-to-noise ratio (CNRD) are evaluated in resulting iodine images. All results are validated in a post-mortem angiography study. Results R of the PC detector varies between 1.2 and 2.6 and increases with higher thresholds and higher tube voltage. Reference R of the EI DSCT is found as 2.20 on average overall phantoms. Maximum CNRD in iodine images is found for T = 60/65/70/70 keV for 80/100/120/140 kV. The highest CNRD of the PCCT is obtained using 140 kV and is decreasing with decreasing tube voltage. All results could be confirmed in the post-mortem angiography study. Conclusion Intrinsically acquired DE data are able to provide iodine images similar to conventional DSCT. However, PCCT thresholds should be chosen with respect to tube voltage to maximize image quality in retrospectively derived image sets. Key Points • Photon-counting CT allows for the computation of iodine images with similar quality compared to conventional dual-source dual-energy CT. • Thresholds should be chosen as a function of the tube voltage to maximize iodine contrast-to-noise ratio in derived image sets. • Image quality of retrospectively computed image sets can be maximized using optimized threshold settings.

Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation

2019 ◽  
Vol 104 (3) ◽  
pp. 677-684 ◽  
Author(s):  
Ward van Rooij ◽  
Max Dahele ◽  
Hugo Ribeiro Brandao ◽  
Alexander R. Delaney ◽  
Berend J. Slotman ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Head And Neck ◽  
Organs At Risk

scholarly journals Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

10.2196/26151 ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. e26151
Author(s):  
Stanislav Nikolov ◽  
Sam Blackwell ◽  
Alexei Zverovitch ◽  
Ruheena Mendes ◽  
Michelle Livne ◽  
...  
Keyword(s):  
Computed Tomography ◽  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Data Set ◽  
Computed Tomography Scans ◽  
Neck Anatomy ◽  
Organ At Risk

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

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