(3) washington university school of medicine, saint louis
TRANSCRIPT
A Generation Methodology for Numerical Phantoms with Statistically Relevant Variability
of Geometric and Physical Properties
Steven Dolly1, Eric Ehler1, Yang Lou2, Mark Anastasio2, Hua Li2
(1) University of Minnesota, Minneapolis, MN(2) Washington University in St. Louis, Saint Louis
(3) Washington University School of Medicine, Saint Louis, MO
Introduction
Numerical (i.e. digital) phantoms are useful for implementing computer-simulation studies by providing a known, ground-truth object
● Enables assessment of image quality, segmentation, registration, and radiotherapy efficacy
Useful studies require realistic phantoms
● Realistic in terms of depth of detail and breadth of variability
Introduction
Depth of detail can be accomplished by thorough segmentation of high-quality medical images
Breadth of variability is accomplished by adequately modeling the statistics of organ variability
● Geometric (e.g. shape) and physical (e.g. photon attenuation) properties
Purpose
1) To develop a methodology of generating numerical phantoms that have both geometric and physical property variability, which is learned from actual patient data using attribute distribution models
2) The methodology was demonstrated by generating an ensemble of head-and-neck CT phantoms, with corresponding images
Attribute Distribution Models
Quantify the statistical distribution of an attribute in terms of its principal components using principal component analysis (PCA):
Three attribute models considered for this numerical phantom:
1) Shape attribute distribution model2) Centroid attribute distribution model3) Physical attribute distribution model
Covariance
Eigenvectors (direction)
Eigenvalues (magnitude)
Workflow
Training Data Acquisition
Attribute Distribution Model
Construction
Mesh Creation & Post-processing
Phase 1: Model Training Phase 2: Phantom Generation
Numerical Phantom(s)
Apply Models
Workflow
Training Data Acquisition
Attribute Distribution Model
Construction
Mesh Creation & Post-processing
Phase 1: Model Training Phase 2: Phantom Generation
Numerical Phantom(s)
Apply Models
Training Data Acquisition
Extracted and anonymized planning CT images and RT structure files from previously treated head-and-neck cancer patients
Process produced 20 patient data sets, with 23 organs per patient
CT ImageRT Structure(Left Parotid)
Attribute Distribution Model Construction
Model: Mean + Randomly-weighted variation
Shape:
Centroid:
Physical (CT):
RT Structures
CT Images + RT Structures
Trained Using:
Sample Shape Attribute Distribution ModelMean Shapea(p)s = -2σ
1st Component
2nd Component
3rd Component
Left ParotidShape Components
a(p)s = +2σ
Centroid Attribute Distribution Model
Anterior
Left
Superior
Physical Attribute Distribution Model
Left Parotid
Brain
Organ CT histograms
Workflow
Training Data Acquisition
Attribute Distribution Model
Construction
Mesh Creation & Post-processing
Phase 1: Model Training Phase 2: Phantom Generation
Numerical Phantom(s)
Apply Models
Numerical Phantom Generation
**Repeat for all n organs
HU
**Repeat for all n organs
Numerical Phantom Geometries
Mean Sample
Left
Superior
CT Image Simulation
Helical projection data was simulated by calculating photon exponential attenuation through the phantoms
CT Images
Mean Sample
ConclusionIn this study:
1) The statistical variability of physical and geometric properties for patient organs was learned from training data using PCA
2) The generated phantoms encapsulate the variability of the training data set, removing the bias of single-phantom studies
3) CT images of the phantoms were simulated as a demonstrated use
Future Work
Incorporation of a priori knowledge as constraints in the post-processing step
● Example: spinal cord must smoothly connect to brain stem
More realistic representation of background tissues (e.g. muscle)
Multi-modality imaging simulation (e.g. MRI)
Extra Slides
Shape Attribute Distribution Model
Organ shapes were defined using implicit surface functions:
One model per organ: intra-structural model
Shape Attribute Distribution Model
1) Preprocessing: Translate organ surfaces (polygons) so centroid → origin2) Calculate implicit surfaces3) Calculate mean and covariance
4) Perform PCA to produce components and construct model
Centroid Attribute Distribution Model
1) Pre-processing: Procrustes analysis2) Calculate organ centroids (mean of each organ’s polygon points)3) Calculate mean and covariance
4) Perform PCA and construct model (inter-structural)
Physical Attribute Distribution Model
1) Determine which CT voxels belong to each organ using contours
2) Calculate HU histograms for each organ
3) Use most probable HU as the mean value
Numerical Phantom CT Numbers
Organ Mean CT Number (HU) Sample CT Number (HU)
Left Parotid -13.3 4.6
Right Parotid -11.7 -15.6
Brain Stem 24.1 21.4
Spinal Cord 33.2 35.1
Left Eye 9.4 7.4
Left Lacrimal Gland 26.9 30.9
Mesh Creation & Post-processing
Normal calculation and Poisson surface reconstruction utilized to convert to triangular meshes; quadric edge decimation used to simplify
Organ Overlap Challenge
Two main approaches to handle organ overlap issues via post-processing:
1) Heuristic iterative shift approach
2) Crop out intersection of deformable organs
In the future, the shape/centroid models could be constrained to limit organ
overlap