An adversarial machine learning framework and biomechanical model‐guided approach for computing 3D lung tissue elasticity from end‐expiration 3DCT-Reference-Cited by-同舟云学术

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An adversarial machine learning framework and biomechanical model‐guided approach for computing 3D lung tissue elasticity from end‐expiration 3DCT

Published:2020-12-22 Issue:2 Volume:48 Page:667-675
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Santhanam Anand P.¹,Stiehl Brad¹,Lauria Michael¹,Hasse Katelyn¹,Barjaktarevic Igor²,Goldin Jonathan³,Low Daniel A.¹

Affiliation:

1. Department of Radiation Oncology University of California, Los Angeles Los Angeles CA90095USA

2. Department of Pulmonary Critical Care University of California, Los Angeles Los Angeles CA90095USA

3. Department of Radiological Sciences University of California, Los Angeles Los Angeles CA90095USA

Funder

Tobacco-Related Disease Research Program

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14252

Reference35 articles.

1. Stereotactic Body Radiation Therapy for Inoperable Early Stage Lung Cancer

2. Precision Hypofractionated Radiation Therapy in Poor Performing Patients With Non-Small Cell Lung Cancer: Phase 1 Dose Escalation Trial

3. Quality of Life After Stereotactic Ablative Radiotherapy for Early-Stage Lung Cancer: A Systematic Review

4. An inverse hyper-spherical harmonics-based formulation for reconstructing 3D volumetric lung deformations

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