Chen et al., 2023 - Google Patents
Patient-Specific Auto-segmentation on Daily kVCT Images for Adaptive Radiation TherapyChen et al., 2023
View PDF- Document ID
- 6415546393733315691
- Author
- Chen Y
- Gensheimer M
- Bagshaw H
- Butler S
- Yu L
- Zhou Y
- Shen L
- Kovalchuk N
- Surucu M
- Chang D
- Xing L
- Han B
- Publication year
- Publication venue
- International Journal of Radiation Oncology* Biology* Physics
External Links
Snippet
Purpose This study explored deep-learning-based patient-specific auto-segmentation using transfer learning on daily RefleXion kilovoltage computed tomography (kVCT) images to facilitate adaptive radiation therapy, based on data from the first group of patients treated …
- 238000001959 radiotherapy 0 title abstract description 48
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1031—Treatment planning systems using a specific method of dose optimization
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/1048—Monitoring, verifying, controlling systems and methods
- A61N5/1064—Monitoring, verifying, controlling systems and methods for adjusting radiation treatment in response to monitoring
- A61N5/1065—Beam adjustment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/50—Clinical applications
- A61B6/507—Clinical applications involving determination of haemodynamic parameters, e.g. perfusion CT
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Johnstone et al. | Systematic review of synthetic computed tomography generation methodologies for use in magnetic resonance imaging–only radiation therapy | |
Largent et al. | Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning | |
Cusumano et al. | Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives | |
Kurz et al. | Investigating deformable image registration and scatter correction for CBCT‐based dose calculation in adaptive IMPT | |
Tsuji et al. | Dosimetric evaluation of automatic segmentation for adaptive IMRT for head-and-neck cancer | |
JP5330992B2 (en) | Biologically guided adaptive treatment planning | |
Marchant et al. | Accuracy of radiotherapy dose calculations based on cone-beam CT: comparison of deformable registration and image correction based methods | |
Farjam et al. | Dosimetric evaluation of an atlas‐based synthetic CT generation approach for MR‐only radiotherapy of pelvis anatomy | |
Dai et al. | Synthetic CT‐aided multiorgan segmentation for CBCT‐guided adaptive pancreatic radiotherapy | |
Kadoya et al. | Dosimetric impact of 4-dimensional computed tomography ventilation imaging-based functional treatment planning for stereotactic body radiation therapy with 3-dimensional conformal radiation therapy | |
Olin et al. | Feasibility of multiparametric positron emission tomography/magnetic resonance imaging as a one-stop shop for radiation therapy planning for patients with head and neck cancer | |
Mancosu et al. | Applications of artificial intelligence in stereotactic body radiation therapy | |
Lavrova et al. | Adaptive radiation therapy: A review of CT-based techniques | |
Nachbar et al. | Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy | |
Dewalle-Vignion et al. | Is STAPLE algorithm confident to assess segmentation methods in PET imaging? | |
Li et al. | A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy | |
Zhang et al. | Reproducibility of tumor motion probability distribution function in stereotactic body radiation therapy of lung cancer | |
Kejda et al. | Evaluation of the clinical feasibility of cone-beam computed tomography guided online adaption for simulation-free palliative radiotherapy | |
Chen et al. | A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma | |
Chen et al. | Patient-Specific Auto-segmentation on Daily kVCT Images for Adaptive Radiation Therapy | |
Martin et al. | A multiphase validation of atlas-based automatic and semiautomatic segmentation strategies for prostate MRI | |
Costa et al. | Optimization of GATE simulations for whole-body planar scintigraphic acquisitions using the XCAT male phantom with 177Lu-DOTATATE biokinetics in a Siemens Symbia T2 | |
Hysing et al. | Statistical motion modelling for robust evaluation of clinically delivered accumulated dose distributions after curative radiotherapy of locally advanced prostate cancer | |
Largent et al. | Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning | |
Osman | Radiation oncology in the era of big data and machine learning for precision medicine |