EP-2049 Patient specific pixel-based weighting factor bone-only dual-energy x-ray imaging

Darvish-Molla, Sahar; Reno, Michael C.; Sattarivand, Mike

doi:10.1016/s0167-8140(19)32469-7

Cited by 2 publications

(2 citation statements)

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“…The thin regions that saturated the detector readings at higher kVps were marked (these ROIs were used later to identify the upper limit for dynamic range of detector). Small circular ROIs were created under each pin to calculate the average scatter signal as per figure 5(c), similar to the technique described in previous studies (Li et al 2012, Darvish-Molla et al 2019. The average signal calculated for the small circular regions were only the scatter signal (as the length of the pins was enough to effectively block all the primary photons).…”

Section: Dynamic Range Of Flat Panel Detectormentioning

confidence: 99%

“…One of the interesting motivations for this work is the need for optimization of an ExacTrac stereotactic dual-energy x-ray imaging system for the enhancement of image quality for SBRT applications (Bowman et al 2017, Darvish-Molla et al 2019. Despite their advantages, conventional planar x-ray images show the presence of both soft tissue and bony anatomy, which may cause difficulty and inaccuracy in target localization and alignment, particularly in the overlapping regions of soft tissue and bone.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive characterization of ExacTrac stereoscopic image guidance system using Monte Carlo and Spektr simulations

Darvish-Molla¹,

Spurway²,

Sattarivand³

2020

Phys. Med. Biol.

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The purpose of this work is to develop accurate computational methods to comprehensively characterize and model the clinical ExacTrac imaging system, which is used as an image guidance system for stereotactic treatment applications. The Spektr toolkit was utilized to simulate the spectral and imaging characterization of the system. Since Spektr only simulates the primary beam (ignoring scatter), a full model of ExacTrac was also developed in Monte Carlo (MC) to characterize the imaging system. To ensure proper performance of both simulation models, Spektr and MC data were compared to the measured spectral and half value layers (HVLs) values. To validate the simulation results, x-ray spectra of the ExacTrac system were measured for various tube potentials using a CdTe spectrometer with multiple added narrow collimators. The raw spectra were calibrated using a 57Co source and corrected for the escape peaks and detector efficiency. HVLs in mm of Al for various energies were measured using a calibrated RaySafe detector. Spektr and MC HVLs were calculated and compared to the measured values. The patient surface dose was calculated for different clinical imaging protocols from the measured air kerma and HVL values following the TG-61 methodology. The x-ray focal spot was measured by slanted edge technique using gafchromic films. ExacTrac imaging system beam profiles were simulated for various energies by MC simulation and the results were benchmarked by experimentally acquired beam profiles using gafchromic films. The effect of 6D IGRT treatment couch on beam hardening, dynamic range of the flat panel detector and scatter effect were determined using both Spektr simulation and experimental measurements. The measured and simulated spectra (of both MC and Spektr) for various kVps were compared and agreed within acceptable error. As another validation, the measured HVLs agreed with the Spektr and MC simulated HVLs on average within 1.0% for all kVps. The maximum and minimum patient surface doses were found to be 1.06 mGy for shoulder (high) and 0.051 mGy for cranial (low) imaging protocols, respectively. The MC simulated beam profiles were well matched with experimental results and replicated the penumbral slopes, the heel effect, and out-of-field regions. Dynamic range of detector (in terms of air kerma at detector surface) was found to be in the range of [6.1 × 10−6, 5.3 × 10−3] mGy. Accurate MC and Spektr models of the ExacTrac image guidance system were successfully developed and benchmarked via experimental validation. While patient surface dose for available imaging protocols were reported in this study, the established MC model may be used to obtain 3D imaging dose distribution for real patient geometries.

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Section: Dynamic Range Of Flat Panel Detectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comprehensive characterization of ExacTrac stereoscopic image guidance system using Monte Carlo and Spektr simulations

Darvish-Molla¹,

Spurway²,

Sattarivand³

2020

Phys. Med. Biol.

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Adaptive noise reduction for dual-energy x-ray imaging based on spatial variations in beam attenuation

Romadanov¹,

Sattarivand²

2020

Phys. Med. Biol.

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Purpose. The main goal of this work is to improve the previously proposed patient-specific pixel-based dual-energy (PP-DE) algorithm by developing an adaptive anti-correlated noise reduction (ACNR) method, resulting in reduced image noise. Methods. Theoretical models of contrast-to-noise (CNR) and signal-to-noise (SNR) ratio were developed as functions of weighting factors for DE bone ω Bn or soft tissue ω ST cancellation. These analytical expressions describe CNR and SNR properties of dual-energy (DE) images, obtained with both simple log subtraction (SLS) and ACNR algorithms, and allow for a direct comparison between experimental and theoretical results. The theoretical models demonstrate the importance of ACNR weighting factor (ω A ) optimization leading to the maximization of the SNR of the final image. A step phantom was constructed, which consisted of overlapping slabs of solid water (0-30 cm) and bone-mimicking material (0-6 cm), resulting in a total of 7 × 7 regions. High-energy (HE) and low-energy (LE) images were acquired at 140 kVp and 60 kVp with a clinical ExacTrac imaging system. The CNR and SNR were obtained for the DE images as functions of ω Bn,ST and noise reduction weighting factor ω A for different combinations of thicknesses. Weighting factors for bone cancellation were optimized for each region of interest (ROI) by finding zeros of CNR function for DE images between soft tissue only and soft tissue plus bone regions (and vice versa for soft tissue cancellation). The weighting factor for the ACNR algorithm ω A was then optimized by maximizing the SNR function for each ROI. HE and LE images for an anthropomorphic Rando phantom were obtained with the same acquisition parameters as for the step phantom. DE images for bone only and soft tissue only were obtained with three algorithms: SLS and PP-DE with conventional ACNR (uniform ω A ), and PP-DE with adaptive ACNR (region-varying ω A ). Weighting factor maps for PP-DE and adaptive ACNR methods were obtained for Rando phantom geometry (which was determined from its CT scans) by interpolation (or extrapolation) of weighting factors for the step phantom. CNR values were calculated for different regions. Results. The CNR and SNR characteristics as functions of material cancellation and noise reduction weighting factors were obtained from theoretical models and experimental data from the step phantom. This showed a good qualitative validation of the models. For the ANCR algorithm, both the theory and experiment demonstrated that the material cancellation weighting factors (ω Bn,ST ) can be optimized independently of the noise cancellation weighting factors (ω A ), which can be optimized by maximizing SNR. For each ROI (with different overlapping bone and soft tissue thicknesses) the weighting factors ω Bn,ST were determined as well as corresponding optimal weighting factors ω A for noise reduction. For the Rando phantom, CNR values for regions representing different anatomical structures (ribs, spine, and tumor) were evaluated. It was shown that the proposed adaptive ACNR further improves image quality, compared to the conventional ACNR algorithm. The improvement is maximized for regions with bones (ribs or spine), where the largest attenuation is observed. Conclusion. The ACNR weighting factors are dependent on the material thicknesses due to varying beam attenuation leading to different levels of quantum noise. This was shown with the derived theoretical expressions of the CNR and SNR functions and was validated by experimental data. The adaptive ANCR DE algorithm was developed, which allows for an increase in image quality by spatially varying weighting factors for noise reduction. This algorithm complements the previously developed PP-DE algorithm to obtain better quality DE images.

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EP-2049 Patient specific pixel-based weighting factor bone-only dual-energy x-ray imaging

Cited by 2 publications

References 0 publications

Comprehensive characterization of ExacTrac stereoscopic image guidance system using Monte Carlo and Spektr simulations

Comprehensive characterization of ExacTrac stereoscopic image guidance system using Monte Carlo and Spektr simulations

Adaptive noise reduction for dual-energy x-ray imaging based on spatial variations in beam attenuation

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