Three-dimensional in silico breast phantoms for multimodal image simulations - PubMed (original) (raw)
Three-dimensional in silico breast phantoms for multimodal image simulations
David M Mahr et al. IEEE Trans Med Imaging. 2012 Mar.
Abstract
Anatomic simulators have provided researchers with the realistic objects needed to develop and evaluate medical imaging approaches. Today we have new insights into the cellular biology of breast tissues that is driving many new targeted diagnostic and therapeutic approaches, including molecular imaging. We report on our initial efforts to build a scalable framework for the construction of realistic 3-D in silico breast phantoms (ISBP) capable of leveraging existing knowledge and yet adaptable to fully integrate future discoveries. The ISBP frames are developed with scalable anatomical shapes and morphologic features as adapted from a rich literature on this topic. Frames are populated with tissue subtypes essential for imaging and object contrast functions are assigned. These data can be resampled to match the intrinsics scales of various imaging modalities; we explore mammography, sonography and computed tomography. Initial comparisons between simulated and clinical images demonstrate reasonable agreement and provides guidance for future development of a more realistic ISBP. An end-to-end simulation of breast images is described to demonstrate techniques for including stochastic variability and deterministic physical principles on which image formation is based.
Figures
Fig. 1
Normal breast anatomy. (Illustrated by and adapted from Lynch. [21]).
Fig. 2
Ellipsoid fit to reference points.
Fig. 3
The outer boundary of the breast is defined by an ellipsoid in the lower part (dark) and by second degree polynomials in the upper part (light) where _y_upper = 160 mm and _y_lower = 20 mm.
Fig. 4
a) The placement of spheres used to simulate irregular subcutaneousfibrous surfaces is illustrated. b) Example simulation of a subcutaneous–fibrous surface for an ISBP.
Fig. 5
Four simulated volumes with different adipose percentages. Displayed images are from maximum intensity projection.
Fig. 6
Volume showing fibrous tissue of Cooper’s ligaments within the subcutaneous fat layer.
Fig. 7
Example of a ductal tree generated from the ramification matrix in (2).
Fig. 8
a) Ductal tree with lobules attached. b) Close-up of the sphere clusters that composes lobule structures.
Fig. 9
Structures that compose the complete breast phantom. Structures include the breast boundary, tissue regions (subcutaneous fat layer and fibroglandular region), subcutaneous–fibrous surface, intraglandular fat, Cooper’s ligaments, and ductal structure.
Fig. 10
a)–d) Simulated mammograms for different adipose compositions (40%, 50%, 60%, and 70%) of the breast. e) Clinical mammogram. (Clinical mammogram used with permission from Bliznakova et al. [1]).
Fig. 11
a)–d) Simulated sonograms for different adipose percentages: 40%, 50%, 60%, and 70%. e) Clinical sonogram where SK = Skin, SF = Subcutaneous Fat, G = Fibroglandular (Fibrous), IF = Intraglandular Fat, D = Ductal Tissue (Glandular), and RF = Retromammary Fat. (Clinical sonogram used with permission from Ramsay et al. [14]).
Fig. 12
a)–d) Simulated breast CT images for different adipose percentages: 40%, 50%, 60%, and 70%. e) Clinical breast CT image. (Clinical CT image used with permission from Boone [34].
Fig. 13
Examples show orientation of branching angles with respect to the reference lobe axis. _θ_1 and _θ_2 are calculated with respect to the _z_′-axis and φ is calculated with respect to the _x_′-axis.
Fig. 14
Relative locations of each ductal opening.
References
- Bliznakova K, Bliznakov Z, Bravou V, Kolitsi Z, Pallikarakis N. A three-dimensional breast software phantom for mammography simulation. Phys Med Biol. 2003;48(22):3699–3719. - PubMed
- Bakic PR, Albert M, Maidment ADA. Classification of galactograms with ramification matrices. Acad Radiol. 2003;10(2):198–204. - PubMed
- Bakic PR, Albert M, Brzakovic D, Maidment AD. Mammogram synthesis using a 3-D simulation. I. Breast tissue model and image acquisition simulation. Med Phys. 2002;29(9):2131–2139. - PubMed
- Bakic PR, Albert M, Brzakovic D, Maidment AD. Mammogram synthesis using a three-dimensional simulation. III. Modeling and evaluation of the breast ductal network. Med Phys. 2003;30(7):1914–1925. - PubMed