Fang-Fang Yin

Overview:

Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy, image-guided radiation therapy, oncological imaging and informatics

Positions:

Professor in Radiation Oncology

Radiation Oncology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

B.S. 1982

Zhejiang University (China)

M.S. 1987

Bowling Green State University

Ph.D. 1992

The University of Chicago

Certificate In Therapeutic Radiologic Physics, Radiation Physics

American Board of Radiology

Grants:

Cross-disciplinary Training in Medical Physics

Administered By
Duke University Medical Physics Graduate Program
Awarded By
National Institutes of Health
Role
Associate Director
Start Date
End Date

Motion Management Using 4D-MRI for Liver Cancer in Radiation Therapy

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Co-Principal Investigator
Start Date
End Date

Digital tomosynthesis: a new paradigm for radiation treatment verification

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Image-guided Dosimetry for Injectable Brachytherapy based on Elastin-like Polypeptide Nanoparticles

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Robotic SPECT for Biological Imaging Onboard Radiation Therapy Machines

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Co-Principal Investigator
Start Date
End Date

Publications:

Liver 4D-MRI: An Image Mutual Information based Retrospective Self-sorting Method

© 2019 IEEE. Four-dimensional MRI (4D-MRI) is an emerging technique for soft-tissue motion management in radiotherapy treatment planning. The purpose of this study was to develop a novel image mutual information (MI) based retrospective 4DMRI sorting method that is fully automatic and free of external surrogates. Two novel components of the image-based 4D-MRI method were developed: automatic EOE/EOI phase sorting and inter-slice phase propagation. Image MI was first used to find EOE/EOI pair for each slice and subsequently used to form EOE/EOI chain across slices. After EOE and EOI phase determination, the MI values between intra-slice frames and EOE phase were employed as surrogates for phase sorting. In addition, MI-based inter-slice phase propagation was utilized to maximize the similarity between matching phases of neighboring slices so that the issue of image discontinuity can be mitigated. The first component was examined on a liver cancer patient and the second component on a 4D-XCAT digital human phantom infused with twelve real patient data. Our results showed that the fully automatic EOE/EOI phase sorting matched well with the manual sorting method. The inter-slice phase propagation method worked successfully on the XCAT digital phantom with less than 1% of voxels being mismatched. In conclusion, unlike some existing image-based 4D-MRI methods, the proposed MI-based 4D-MRI sorting method is fully automatic and potentially less sensitive to anatomy discontinuity caused by breathing irregularity. However, a future cohort study with a larger pool of human subjects is warranted to further assess the robustness of the proposed method.
Authors
Zhang, L; Yin, FF; Cai, J
MLA Citation
Zhang, L., et al. “Liver 4D-MRI: An Image Mutual Information based Retrospective Self-sorting Method.” 2019 International Conference on Medical Imaging Physics and Engineering, Icmipe 2019, 2019. Scopus, doi:10.1109/ICMIPE47306.2019.9098165.
URI
https://scholars.duke.edu/individual/pub1448070
Source
scopus
Published In
2019 International Conference on Medical Imaging Physics and Engineering, Icmipe 2019
Published Date
DOI
10.1109/ICMIPE47306.2019.9098165

909 INDUCTION OF RADIATION-INDUCED ERECTILE DYSFUNCTION OF RAT AFTER PROSTATE-CONFINED MODERN RADIOTHERAPY

Authors
Kimura, M; Yan, H; Rabbani, Z; Satoh, T; Baba, S; Yin, F-F; Polascik, T; Donatucci, C; Vujaskovic, Z; Koontz, B
MLA Citation
Kimura, Masaki, et al. “909 INDUCTION OF RADIATION-INDUCED ERECTILE DYSFUNCTION OF RAT AFTER PROSTATE-CONFINED MODERN RADIOTHERAPY.” Journal of Urology, vol. 185, no. 4S, Ovid Technologies (Wolters Kluwer Health), 2011. Crossref, doi:10.1016/j.juro.2011.02.800.
URI
https://scholars.duke.edu/individual/pub1444877
Source
crossref
Published In
The Journal of Urology
Volume
185
Published Date
DOI
10.1016/j.juro.2011.02.800

Retrospective quality metrics review of stereotactic radiosurgery plans treating multiple targets using single-isocenter volumetric modulated arc therapy.

PURPOSE: To characterize key plan quality metrics in multi-target stereotactic radiosurgery (SRS) plans treated using single-isocenter volumetric modulated arc therapy (VMAT) in comparison to dynamic conformal arc (DCA) plans treating single target. To investigate the feasibility of quality improvement in VMAT planning based on previous planning knowledge. MATERIALS AND METHODS: 97 VMAT plans of multi-target and 156 DCA plans of single-target treated in 2017 at a single institution were reviewed. A total of 605 targets were treated with these SRS plans. The prescription dose was normalized to 20 Gy in all plans for this analysis. Two plan quality metrics, target conformity index (CI) and normal tissue volume receiving more than 12 Gy (V12Gy), were calculated for each target. The distribution of V12Gy per target was plotted as a function of the target volume. For multi-target VMAT plans, the number of targets being treated in the same plan and the distance between targets were calculated to evaluate their impact on V12Gy. VMAT plans that had a large deviation of V12Gy from the average level were re-optimized to determine the possibility of reducing the variation of V12Gy in VMAT planning. RESULTS: Conformity index of multi-target VMAT plans were lower than that of DCA plans while the mean values of 12 Gy were comparable. The V12Gy for a target in VMAT plan did not show apparent dependence on the total number of targets or the distance between targets. The distribution of V12Gy exhibited a larger variation in VMAT plans compared to DCA plans. Re-optimization of outlier plans reduced V12 Gy by 33.9% and resulted in the V12Gy distribution in VMAT plans more closely resembling that of DCA plans. CONCLUSION: The benchmark data on key plan quality metrics were established for single-isocenter multi-target SRS planning. It is feasible to use this knowledge to guide VMAT planning and reduce high V12Gy outliers.
Authors
MLA Citation
Cui, Yunfeng, et al. “Retrospective quality metrics review of stereotactic radiosurgery plans treating multiple targets using single-isocenter volumetric modulated arc therapy.J Appl Clin Med Phys, vol. 21, no. 6, June 2020, pp. 93–99. Pubmed, doi:10.1002/acm2.12869.
URI
https://scholars.duke.edu/individual/pub1436642
PMID
32239746
Source
pubmed
Published In
Journal of Applied Clinical Medical Physics
Volume
21
Published Date
Start Page
93
End Page
99
DOI
10.1002/acm2.12869

Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.

PURPOSE: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning. METHODS AND MATERIALS: We propose a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically viable space of plans with best achievable dose-volume parameters before planning. For a given case, a case reference set (CRS) is selected using a novel anatomic similarity metric from a large reference plan pool. Then, a regression model is built on the CRS to estimate the expected dose-volume histograms (DVHs) for the current case. This model also predicts the DVHs for all CRS cases and captures the variation from the corresponding DVHs in the clinical plans. Finally, these DVH variations are analyzed using the principal component analysis to determine the tradeoff hyperplane for the current case. To evaluate the effectiveness of the proposed approach, 244 head and neck cases were randomly partitioned into reference (214) and validation (30) sets. A tradeoff hyperplane was built for each validation case and evenly sampled for 12 tradeoff predictions. Each prediction yielded a tradeoff plan. The root-mean-square errors of the predicted and the realized plan DVHs were computed for prediction achievability evaluation. RESULTS: The tradeoff hyperplane with 3 principal directions accounts for 57.8% ± 3.6% of variations in the validation cases, suggesting the hyperplanes capture a significant portion of the clinical tradeoff space. The average root-mean-square errors in 3 tradeoff directions are 5.23 ± 2.46, 5.20 ± 2.52, and 5.19 ± 2.49, compared with 4.96 ± 2.48 of the knowledge-based planning predictions, indicating that the tradeoff predictions are comparably achievable. CONCLUSIONS: Clinically relevant tradeoffs can be effectively extracted from existing plans and characterized by a tradeoff hyperplane model. The hyperplane allows physicians and planners to explore the best clinically achievable plans with different organ-at-risk sparing goals before inverse planning and is a natural extension of the current knowledge-based planning framework.
Authors
MLA Citation
Zhang, Jiahan, et al. “Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.Int J Radiat Oncol Biol Phys, vol. 106, no. 5, Apr. 2020, pp. 1095–103. Pubmed, doi:10.1016/j.ijrobp.2019.12.034.
URI
https://scholars.duke.edu/individual/pub1428932
PMID
31982497
Source
pubmed
Published In
Int J Radiat Oncol Biol Phys
Volume
106
Published Date
Start Page
1095
End Page
1103
DOI
10.1016/j.ijrobp.2019.12.034

Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).

Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For training purposes, major organs and gross tumor volumes (GTVs) in chest region were segmented from real patient images and assigned to different HU values to generate organ maps, which resemble the XCAT images. A dual-discriminator conditional-generative adversarial network (D-CGAN) was developed to synthesize anatomical textures in the corresponding organ maps. The D-CGAN was uniquely designed with two discriminators, one trained for the body and the other for the tumor. Various XCAT phantoms were input to the D-CGAN to generate textured XCAT phantoms. The D-CGAN model was trained separately using 62 CT and 63 CBCT images from lung SBRT patients to generate multi-contrast textured XCAT (MT-XCAT). The MT-XCAT phantoms were evaluated by comparing the intensity histograms and radiomic features with those from real patient images using Wilcoxon rank-sum test. The visual examination demonstrated that the MT-XCAT phantoms presented similar general contrast and anatomical textures as CT and CBCT images. The mean HU of the MT-XCAT-CT and MT-XCAT-CBCT were [Formula: see text] and [Formula: see text], compared with that of real CT ([Formula: see text]) and CBCT ([Formula: see text]). The majority of radiomic features from the MT-XCAT phantoms followed the same distribution as the real images according to the Wilcoxon rank-sum test, except for limited second-order features. The study demonstrated the feasibility of generating realistic MT-XCAT phantoms using D-CGAN. The MT-XCAT phantoms can be further expanded to include other modalities (MRI, PET, ultrasound, etc) under the same scheme. This crucial development greatly enhances the value of the phantom for various clinical applications, including testing and optimizing novel imaging techniques, validation of radiomics analysis methods, and virtual clinical trials.
Authors
Chang, Y; Lafata, K; Segars, WP; Yin, F-F; Ren, L
MLA Citation
Chang, Yushi, et al. “Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).Phys Med Biol, vol. 65, no. 6, Mar. 2020, p. 065009. Pubmed, doi:10.1088/1361-6560/ab7309.
URI
https://scholars.duke.edu/individual/pub1431200
PMID
32023555
Source
pubmed
Published In
Phys Med Biol
Volume
65
Published Date
Start Page
065009
DOI
10.1088/1361-6560/ab7309

Research Areas:

Bioinformatics
Medical physics