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TH‐A‐18C‐09: Ultra‐Fast Monte Carlo Simulation for Cone Beam CT Imaging of Brain Trauma
Author(s) -
Sisniega A,
Zbijewski W,
Stayman J,
Yorkston J,
Aygun N,
Koliatsos V,
Siewerdsen J
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4889568
Subject(s) - monte carlo method , cone beam computed tomography , imaging phantom , projection (relational algebra) , physics , optics , detector , nuclear medicine , computer science , mathematics , algorithm , medicine , computed tomography , radiology , statistics
Purpose: Application of cone‐beam CT (CBCT) to low‐contrast soft tissue imaging, such as in detection of traumatic brain injury, is challenged by high levels of scatter. A fast, accurate scatter correction method based on Monte Carlo (MC) estimation is developed for application in high‐quality CBCT imaging of acute brain injury. Methods: The correction involves MC scatter estimation executed on an NVIDIA GTX 780 GPU (MC‐GPU), with baseline simulation speed of ˜1e7 photons/sec. MC‐GPU is accelerated by a novel, GPU‐optimized implementation of variance reduction (VR) techniques (forced detection and photon splitting). The number of simulated tracks and projections is reduced for additional speed‐up. Residual noise is removed and the missing scatter projections are estimated via kernel smoothing (KS) in projection plane and across gantry angles. The method is assessed using CBCT images of a head phantom presenting a realistic simulation of fresh intracranial hemorrhage (100 kVp, 180 mAs, 720 projections, source‐detector distance 700 mm, source‐axis distance 480 mm). Results: For a fixed run‐time of ˜1 sec/projection, GPU‐optimized VR reduces the noise in MC‐GPU scatter estimates by a factor of 4. For scatter correction, MC‐GPU with VR is executed with 4‐fold angular downsampling and 1e5 photons/projection, yielding 3.5 minute run‐time per scan, and de‐noised with optimized KS. Corrected CBCT images demonstrate uniformity improvement of 18 HU and contrast improvement of 26 HU compared to no correction, and a 52% increase in contrast‐tonoise ratio in simulated hemorrhage compared to “oracle” constant fraction correction. Conclusion: Acceleration of MC‐GPU achieved through GPU‐optimized variance reduction and kernel smoothing yields an efficient (<5 min/scan) and accurate scatter correction that does not rely on additional hardware or simplifying assumptions about the scatter distribution. The method is undergoing implementation in a novel CBCT dedicated to brain trauma imaging at the point of care in sports and military applications. Research grant from Carestream Health. JY is an employee of Carestream Health.