I am an assistant professor in the Department of Human Oncology with clinical, research and teaching duties. In the clinic, I am involved in brachytherapy treatments, TSET and TBI treatments, improvements to CT simulation imaging for treatment planning, commissioning and validation of new beam models for our treatment planning system, and Tomotherapy quality assurance.
My research focuses on the role of dual-energy CT in radiation oncology imaging, including the use of dual-energy CT to improve tumor delineation for pancreatic and liver tumors.
I am the mentor of the medical physics residency Special Procedures rotation, which is focused on TSET and TBI, and teach a graduate level brachytherapy course in the Department of Medical physics.
Resident, University of Wisconsin–Madison, Radiation Oncology Physics (2017)
PhD, University of Texas, MD Anderson Cancer Center, Medical Physics (2015)
BS, Rice University, Physics (2010)
Assistant Professor, Human Oncology (2017)
Selected Honors and Awards
AAPM Peter R. Almond Award of Excellence for an Outstanding Radiation Measurements Article (2020)
PTW Poster Award at the Southwest AAPM Chapter Meeting (2014)
MD Anderson Cancer Center Trainee Excellence Award (2012)
Faculty & Alumni Merit Fellowship from the University of Texas Health Science Center Graduate School of Biomedical Sciences (2011–2014)
Boards, Advisory Committees and Professional Organizations
Simulation improvement committee in DHO (2017-pres.)
American Brachytherapy Society (2016-pres.)
AAPM Students and Trainees Subcommittee (2013-2016)
American Association of Physicists in Medicine (2011-pres.)
Treatment Planning, Dual-Energy CT, Monte Carlo Electron Dose Calculation, Brachytherapy
Evaluation of a commercial deformable image registration algorithm for dual-energy CT processing Journal of applied clinical medical physics
Huang JY, Lawless MJ, Matrosic CK, Maso DD, Miller JR
2020 Sep;21(9):227-234. doi: 10.1002/acm2.12987. Epub 2020 Jul 25.
PURPOSE: Several dual-energy computed tomography (DECT) techniques require a deformable image registration to correct for motion between the acquisition of low and high energy data. However, current DECT software does not provide tools to assess registration accuracy or allow the user to export deformed images, presenting a unique challenge for image registration quality assurance (QA). This work presents a methodology to evaluate the accuracy of DECT deformable registration and to quantify the impact of registration errors on end-product images.
METHODS: The deformable algorithm implemented in Siemen Healthineers's Syngo was evaluated using a deformable abdomen phantom and a rigid phantom to mimic sliding motion in the thorax. Both phantoms were imaged using sequential 80 and 140 kVp scans with motion applied between the two scans. Since Syngo does not allow the export of the deformed images, this study focused on quantifying the accuracy of various end-product, dual-energy images resulting from processing of deformed images.
RESULTS: The Syngo algorithm performed well for the abdomen phantom with a mean registration error of 0.4 mm for landmark analysis, Dice similarity coefficients (DSCs) > 0.90 for five organs contoured, and mean iodine concentrations within 0.2 mg/mL of values measured on static images. For rigid sliding motion, the algorithm performed poorer and resulted in noticeable registration errors toward the superior and inferior scan extents and DSCs as low as 0.41 for iodine rods imaged in the phantom. Additionally, local iodine concentration errors in areas of misregistration exceeded 3 mg/mL.
CONCLUSIONS: This work represents the first methodology for DECT image registration QA using commercial software. Our data support the clinical use of the Syngo algorithm for abdominal sites with limited motion (i.e., pancreas and liver). However, dual-energy images generated with this algorithm should be used with caution for quantitative measurements in areas with sliding motion.
PMID:32710502 | PMC:PMC7497912 | DOI:10.1002/acm2.12987
View details for PubMedID 32710502
Evaluation of a commercial Monte Carlo dose calculation algorithm for electron treatment planning Journal of applied clinical medical physics
Huang JY, Dunkerley D, Smilowitz JB
2019 Jun;20(6):184-193. doi: 10.1002/acm2.12622. Epub 2019 May 23.
The RayStation treatment planning system implements a Monte Carlo (MC) algorithm for electron dose calculations. For a TrueBeam accelerator, beam modeling was performed for four electron energies (6, 9, 12, and 15 MeV), and the dose calculation accuracy was tested for a range of geometries. The suite of validation tests included those tests recommended by AAPM's Medical Physics Practice Guideline 5.a, but extended beyond these tests in order to validate the MC algorithm in more challenging geometries. For MPPG 5.a testing, calculation accuracy was evaluated for square cutouts of various sizes, two custom cutout shapes, oblique incidence, and heterogenous media (cork). In general, agreement between ion chamber measurements and RayStation dose calculations was excellent and well within suggested tolerance limits. However, this testing did reveal calculation errors for the output of small cutouts. Of the 312 output factors evaluated for square cutouts, 20 (6.4%) were outside of 3% and 5 (1.6%) were outside of 5%, with these larger errors generally being for the smallest cutout sizes within a given applicator. Adjustment of beam modeling parameters did not fix these calculation errors, nor does the planning software allow the user to input correction factors as a function of field size. Additional validation tests included several complex phantom geometries (triangular nose phantom, lung phantom, curved breast phantom, and cortical bone phantom), designed to test the ability of the algorithm to handle high density heterogeneities and irregular surface contours. In comparison to measurements with radiochromic film, RayStation showed good agreement, with an average of 89.3% pixels passing for gamma analysis (3%/3mm) across four phantom geometries. The MC algorithm was able to accurately handle the presence of irregular surface contours (curved cylindrical phantom and a triangular nose phantom), as well as heterogeneities (cork and cortical bone).
PMID:31120615 | PMC:PMC6560228 | DOI:10.1002/acm2.12622
View details for PubMedID 31120615
Novel use of ViewRay MRI guidance for high-dose-rate brachytherapy in the treatment of cervical cancer Brachytherapy
Ko HC, Huang JY, Miller JR, Das RK, Wallace CR, Costa AD, Francis DM, Straub MR, Anderson BM, Bradley KA
2018 Jul-Aug;17(4):680-688. doi: 10.1016/j.brachy.2018.04.005. Epub 2018 Jun 7.
PURPOSE: To characterize image quality and feasibility of using ViewRay MRI (VR)-guided brachytherapy planning for cervical cancer.
METHODS AND MATERIALS: Cervical cancer patients receiving intracavitary brachytherapy with tandem and ovoids, planned using 0.35T VR MRI at our institution, were included in this series. The high-risk clinical target volume (HR-CTV), visible gross tumor volume, bladder, sigmoid, bowel, and rectum contours for each fraction of brachytherapy were evaluated for dosimetric parameters. Typically, five brachytherapy treatments were planned using the T2 sequence on diagnostic MRI for the first and third fractions, and a noncontrast true fast imaging with steady-state precession sequence on VR or CT scan for the remaining fractions. Most patients received 5.5 Gy × 5 fractions using high-dose-rate Ir-192 following 45 Gy of whole-pelvis radiotherapy. The plan was initiated at 5.5 Gy to point A and subsequently optimized and prescribed to the HR-CTV. The goal equivalent dose in 2 Gy fractions for the combined external beam and brachytherapy dose was 85 Gy. Soft-tissue visualization using contrast-to-noise ratios to distinguish normal tissues from tumor at their interface was compared between diagnostic MRI, CT, and VR.
RESULTS: One hundred and forty-two fractions of intracavitary brachytherapy were performed from April 2015 to January 2017 on 29 cervical cancer patients, ranging from stages IB1 to IVA. The median HR-CTV was 27.78 cc, with median D90 HR-CTV of 6.1 Gy. The median time from instrument placement to start of treatment using VR was 65 min (scan time 2 min), compared to 105 min using diagnostic MRI (scan time 11 min) (t-test, p < 0.01). The contrast-to-noise ratio of tumor to cervix in both diagnostic MRI and VR had significantly higher values compared to CT (ANOVA and t-tests, p < 0.01).
CONCLUSIONS: We report the first clinical use of VR-guided brachytherapy. Time to treatment using this approach was shorter compared to diagnostic MRI. VR also provided significant advantage in visualizing the tumor and cervix compared to CT. This presents a feasible and reliable manner to image and plan gynecologic brachytherapy.
PMID:29773331 | DOI:10.1016/j.brachy.2018.04.005
View details for PubMedID 29773331
Approaches to reducing photon dose calculation errors near metal implants Medical physics
Huang JY, Followill DS, Howell RM, Liu X, Mirkovic D, Stingo FC, Kry SF
2016 Sep;43(9):5117. doi: 10.1118/1.4960632.
PURPOSE: Dose calculation errors near metal implants are caused by limitations of the dose calculation algorithm in modeling tissue/metal interface effects as well as density assignment errors caused by imaging artifacts. The purpose of this study was to investigate two strategies for reducing dose calculation errors near metal implants: implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) dose calculation method and use of metal artifact reduction methods for computed tomography (CT) imaging.
METHODS: Both error reduction strategies were investigated using a simple geometric slab phantom with a rectangular metal insert (composed of titanium or Cerrobend), as well as two anthropomorphic phantoms (one with spinal hardware and one with dental fillings), designed to mimic relevant clinical scenarios. To assess the dosimetric impact of metal kernels, the authors implemented titanium and silver kernels in a commercial collapsed cone C/S algorithm. To assess the impact of CT metal artifact reduction methods, the authors performed dose calculations using baseline imaging techniques (uncorrected 120 kVp imaging) and three commercial metal artifact reduction methods: Philips Healthcare's o-mar, GE Healthcare's monochromatic gemstone spectral imaging (gsi) using dual-energy CT, and gsi with metal artifact reduction software (mars) applied. For the simple geometric phantom, radiochromic film was used to measure dose upstream and downstream of metal inserts. For the anthropomorphic phantoms, ion chambers and radiochromic film were used to quantify the benefit of the error reduction strategies.
RESULTS: Metal kernels did not universally improve accuracy but rather resulted in better accuracy upstream of metal implants and decreased accuracy directly downstream. For the clinical cases (spinal hardware and dental fillings), metal kernels had very little impact on the dose calculation accuracy (<1.0%). Of the commercial CT artifact reduction methods investigated, the authors found that o-mar was the most consistent method, resulting in either improved dose calculation accuracy (dental case) or little impact on calculation accuracy (spine case). gsi was unsuccessful at reducing the severe artifacts caused by dental fillings and had very little impact on calculation accuracy. gsi with mars on the other hand gave mixed results, sometimes introducing metal distortion and increasing calculation errors (titanium rectangular implant and titanium spinal hardware) but other times very successfully reducing artifacts (Cerrobend rectangular implant and dental fillings).
CONCLUSIONS: Though successful at improving dose calculation accuracy upstream of metal implants, metal kernels were not found to substantially improve accuracy for clinical cases. Of the commercial artifact reduction methods investigated, o-mar was found to be the most consistent candidate for all-purpose CT simulation imaging. The mars algorithm for gsi should be used with caution for titanium implants, larger implants, and implants located near heterogeneities as it can distort the size and shape of implants and increase calculation errors.
PMID:27587042 | PMC:PMC4991994 | DOI:10.1118/1.4960632
View details for PubMedID 27587042
An evaluation of three commercially available metal artifact reduction methods for CT imaging Physics in medicine and biology
Huang JY, Kerns JR, Nute JL, Liu X, Balter PA, Stingo FC, Followill DS, Mirkovic D, Howell RM, Kry SF
2015 Feb 7;60(3):1047-67. doi: 10.1088/0031-9155/60/3/1047. Epub 2015 Jan 14.
Three commercial metal artifact reduction methods were evaluated for use in computed tomography (CT) imaging in the presence of clinically realistic metal implants: Philips O-MAR, GE's monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI monochromatic imaging with metal artifact reduction software applied (MARs). Each method was evaluated according to CT number accuracy, metal size accuracy, and streak artifact severity reduction by using several phantoms, including three anthropomorphic phantoms containing metal implants (hip prosthesis, dental fillings and spinal fixation rods). All three methods showed varying degrees of success for the hip prosthesis and spinal fixation rod cases, while none were particularly beneficial for dental artifacts. Limitations of the methods were also observed. MARs underestimated the size of metal implants and introduced new artifacts in imaging planes beyond the metal implant when applied to dental artifacts, and both the O-MAR and MARs algorithms induced artifacts for spinal fixation rods in a thoracic phantom. Our findings suggest that all three artifact mitigation methods may benefit patients with metal implants, though they should be used with caution in certain scenarios.
PMID:25585685 | PMC:PMC4311882 | DOI:10.1088/0031-9155/60/3/1047
View details for PubMedID 25585685
Institutional patient-specific IMRT QA does not predict unacceptable plan delivery International journal of radiation oncology, biology, physics
Kry SF, Molineu A, Kerns JR, Faught AM, Huang JY, Pulliam KB, Tonigan J, Alvarez P, Stingo F, Followill DS
2014 Dec 1;90(5):1195-201. doi: 10.1016/j.ijrobp.2014.08.334. Epub 2014 Oct 21.
PURPOSE: To determine whether in-house patient-specific intensity modulated radiation therapy quality assurance (IMRT QA) results predict Imaging and Radiation Oncology Core (IROC)-Houston phantom results.
METHODS AND MATERIALS: IROC Houston's IMRT head and neck phantoms have been irradiated by numerous institutions as part of clinical trial credentialing. We retrospectively compared these phantom results with those of in-house IMRT QA (following the institution's clinical process) for 855 irradiations performed between 2003 and 2013. The sensitivity and specificity of IMRT QA to detect unacceptable or acceptable plans were determined relative to the IROC Houston phantom results. Additional analyses evaluated specific IMRT QA dosimeters and analysis methods.
RESULTS: IMRT QA universally showed poor sensitivity relative to the head and neck phantom, that is, poor ability to predict a failing IROC Houston phantom result. Depending on how the IMRT QA results were interpreted, overall sensitivity ranged from 2% to 18%. For different IMRT QA methods, sensitivity ranged from 3% to 54%. Although the observed sensitivity was particularly poor at clinical thresholds (eg 3% dose difference or 90% of pixels passing gamma), receiver operator characteristic analysis indicated that no threshold showed good sensitivity and specificity for the devices evaluated.
CONCLUSIONS: IMRT QA is not a reasonable replacement for a credentialing phantom. Moreover, the particularly poor agreement between IMRT QA and the IROC Houston phantoms highlights surprising inconsistency in the QA process.
PMID:25442044 | PMC:PMC4276500 | DOI:10.1016/j.ijrobp.2014.08.334
View details for PubMedID 25442044
Effects of spatial resolution and noise on gamma analysis for IMRT QA Journal of applied clinical medical physics
Huang JY, Pulliam KB, McKenzie EM, Followill DS, Kry SF
2014 Jul 8;15(4):4690. doi: 10.1120/jacmp.v15i4.4690.
We investigated the sensitivity of the gamma index to two factors: the spatial resolution and the noise level in the measured dose distribution. We also examined how the choice of reference distribution and analysis software affect the sensitivity of gamma analysis to these two factors for quality assurance (QA) of intensity-modulated radiation therapy (IMRT) treatment plans. For ten clinical IMRT plans, the dose delivered to a transverse dose plane was measured with EDR2 radiographic film. To evaluate the effects of spatial resolution, each irradiated film was digitized using three different resolutions (71, 142, and 285 dpi). To evaluate the effects of image noise, 1% and 2% local Gaussian noise was added to the film images. Gamma analysis was performed using 2%/2 mm and 3%/3 mm acceptance criteria and two commercial software packages, OmniPro I'mRT and DoseLab Pro. Dose comparisons were performed with the treatment planning system (TPS)-calculated dose as the reference, and then repeated with the film as the reference to evaluate how the choice of reference distribution affects the results of dose comparisons. When the TPS-calculated dose was designated as the reference distribution, the percentage of pixels with passing gamma values increased with both increasing resolution and noise. For 3%/3 mm acceptance criteria, increasing the film image resolution by a factor of two and by a factor of four caused a median increase of 0.9% and 2.6%, respectively, in the percentage of pixels passing. Increasing the noise level in the film image resulted in a median increase in percentage of pixels passing of 5.5% for 1% added local Gaussian noise and 5.8% for 2% added noise. In contrast, when the film was designated as the reference distribution, the percentage of pixels passing decreased with increased film noise, while increased resolution had no significant effect on passing rates. Furthermore, the sensitivity of gamma analysis to noise and resolution differed between OmniPro I'mRT and DoseLab Pro, with DoseLab Pro being less sensitive to the effects of noise and resolution. Noise and high scanning resolution can artificially increase the percentage of pixels with passing gamma values in IMRT QA. Thus, these factors, if not properly taken into account, can potentially affect the results of IMRT QA by causing a plan that should be classified as failing to be falsely classified as passing. In designing IMRT QA protocols, it is important to be aware that gamma analysis is sensitive to these parameters.
PMID:25207399 | DOI:10.1120/jacmp.v15i4.4690
View details for PubMedID 25207399
Comparison of 2D and 3D gamma analyses Medical physics
Pulliam KB, Huang JY, Howell RM, Followill D, Bosca R, O'Daniel J, Kry SF
2014 Feb;41(2):021710. doi: 10.1118/1.4860195.
PURPOSE: As clinics begin to use 3D metrics for intensity-modulated radiation therapy (IMRT) quality assurance, it must be noted that these metrics will often produce results different from those produced by their 2D counterparts. 3D and 2D gamma analyses would be expected to produce different values, in part because of the different search space available. In the present investigation, the authors compared the results of 2D and 3D gamma analysis (where both datasets were generated in the same manner) for clinical treatment plans.
METHODS: Fifty IMRT plans were selected from the authors' clinical database, and recalculated using Monte Carlo. Treatment planning system-calculated ("evaluated dose distributions") and Monte Carlo-recalculated ("reference dose distributions") dose distributions were compared using 2D and 3D gamma analysis. This analysis was performed using a variety of dose-difference (5%, 3%, 2%, and 1%) and distance-to-agreement (5, 3, 2, and 1 mm) acceptance criteria, low-dose thresholds (5%, 10%, and 15% of the prescription dose), and data grid sizes (1.0, 1.5, and 3.0 mm). Each comparison was evaluated to determine the average 2D and 3D gamma, lower 95th percentile gamma value, and percentage of pixels passing gamma.
RESULTS: The average gamma, lower 95th percentile gamma value, and percentage of passing pixels for each acceptance criterion demonstrated better agreement for 3D than for 2D analysis for every plan comparison. The average difference in the percentage of passing pixels between the 2D and 3D analyses with no low-dose threshold ranged from 0.9% to 2.1%. Similarly, using a low-dose threshold resulted in a difference between the mean 2D and 3D results, ranging from 0.8% to 1.5%. The authors observed no appreciable differences in gamma with changes in the data density (constant difference: 0.8% for 2D vs 3D).
CONCLUSIONS: The authors found that 3D gamma analysis resulted in up to 2.9% more pixels passing than 2D analysis. It must be noted that clinical 2D versus 3D datasets may have additional differences--for example, if 2D measurements are made with a different dosimeter than 3D measurements. Factors such as inherent dosimeter differences may be an important additional consideration to the extra dimension of available data that was evaluated in this study.
PMID:24506601 | PMC:PMC3977814 | DOI:10.1118/1.4860195
View details for PubMedID 24506601
Investigation of various energy deposition kernel refinements for the convolution∕superposition method Medical physics
Huang JY, Eklund D, Childress NL, Howell RM, Mirkovic D, Followill DS, Kry SF
2013 Dec;40(12):121721. doi: 10.1118/1.4831758.
PURPOSE: Several simplifications used in clinical implementations of the convolution∕superposition (C∕S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C∕S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C∕S algorithm.
METHODS: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C∕S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C∕S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels.
RESULTS: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found that depth was the most dominant factor affecting the pattern of energy deposition; however, the effects of field size and off-axis distance were not negligible. For the material-specific kernels, we found that as the density of the material increased, more energy was deposited laterally by charged particles, as opposed to in the forward direction. Thus, density scaling of water kernels becomes a worse approximation as the density and the effective atomic number of the material differ more from water. Implementation of spatially variant, polyenergetic kernels increased the percent depth dose value at 25 cm depth by 2.1%-5.8% depending on the field size, while implementation of titanium kernels gave 4.9% higher dose upstream of the metal cavity (i.e., higher backscatter dose) and 8.2% lower dose downstream of the cavity.
CONCLUSIONS: Of the various kernel refinements investigated, inclusion of depth-dependent and metal-specific kernels into the C∕S method has the greatest potential to improve dose calculation accuracy. Implementation of spatially variant polyenergetic kernels resulted in a harder depth dose curve and thus has the potential to affect beam modeling parameters obtained in the commissioning process. For metal implants, the C∕S algorithms generally underestimate the dose upstream and overestimate the dose downstream of the implant. Implementation of a metal-specific kernel mitigated both of these errors.
PMID:24320507 | PMC:PMC3856653 | DOI:10.1118/1.4831758
View details for PubMedID 24320507
Accuracy and sources of error of out-of field dose calculations by a commercial treatment planning system for intensity-modulated radiation therapy treatments Journal of applied clinical medical physics
Huang JY, Followill DS, Wang XA, Kry SF
2013 Mar 4;14(2):4139. doi: 10.1120/jacmp.v14i2.4139.
Although treatment planning systems are generally thought to have poor accuracy for out-of-field dose calculations, little work has been done to quantify this dose calculation inaccuracy for modern treatment techniques, such as intensity-modulated radiation therapy (IMRT), or to understand the sources of this inaccuracy. The aim of this work is to evaluate the accuracy of out-of-field dose calculations by a commercial treatment planning system (TPS), Pinnacle3 v.9.0, for IMRT treatment plans. Three IMRT plans were delivered to anthropomorphic phantoms, and out-of-field doses were measured using thermoluminescent detectors (TLDs). The TLD-measured dose was then compared to the TPS-calculated dose to quantify the accuracy of TPS calculations at various distances from the field edge and out-of-field anatomical locations of interest (i.e., radiosensitive organs). The individual components of out-of-field dose (patient scatter, collimator scatter, and head leakage) were also calculated in Pinnacle and compared to Monte Carlo simulations for a 10 × 10 cm2 field. Our results show that the treatment planning system generally underestimated the out-of-field dose and that this underestimation worsened (accuracy decreased) for increasing distances from the field edge. For the three IMRT treatment plans investigated, the TPS underestimated the dose by an average of 50%. Our results also showed that collimator scatter was underestimated by the TPS near the treatment field, while all components of out-of-field dose were severely underestimated at greater distances from the field edge. This study highlights the limitations of commercial treatment planning systems in calculating out-of-field dose and provides data about the level of accuracy, or rather inaccuracy, that can be expected for modern IMRT treatments. Based on our results, use of the TPS-reported dose could lead to an underestimation of secondary cancer induction risk, as well as poor clinical decision-making for pregnant patients or patients with implantable cardiac pacemakers and defibrillators.
PMID:23470942 | PMC:PMC5714363 | DOI:10.1120/jacmp.v14i2.4139
View details for PubMedID 23470942
Investigation of dose perturbations and the radiographic visibility of potential fiducials for proton radiation therapy of the prostate Physics in medicine and biology
Huang JY, Newhauser WD, Zhu XR, Lee AK, Kudchadker RJ
2011 Aug 21;56(16):5287-302. doi: 10.1088/0031-9155/56/16/014. Epub 2011 Jul 28.
Image guidance using implanted fiducial markers is commonly used to ensure accurate and reproducible target positioning in radiation therapy for prostate cancer. The ideal fiducial marker is clearly visible in kV imaging, does not perturb the therapeutic dose in the target volume and does not cause any artifacts on the CT images used for treatment planning. As yet, ideal markers that fully meet all three of these criteria have not been reported. In this study, 12 fiducial markers were evaluated for their potential clinical utility in proton radiation therapy for prostate cancer. In order to identify the good candidates, each fiducial was imaged using a CT scanner as well as a kV imaging system. Additionally, the dose perturbation caused by each fiducial was quantified using radiochromic film and a clinical proton beam. Based on the results, three fiducials were identified as good candidates for use in proton radiotherapy of prostate cancer.
PMID:21799236 | PMC:PMC3171138 | DOI:10.1088/0031-9155/56/16/014
View details for PubMedID 21799236
Jessie Huang-Vredevoogd, PhDK4/B100 CSC,
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