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

Professor of Medical Physics at Duke Kunshan University

DKU Faculty
Duke Kunshan University

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

University of Chicago

Certificate In Therapeutic Radiologic Physics, Radiation Physics

American Board of Radiology

Grants:

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

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

Accurate, High Resolution 3D Dosimetry

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

A Limited-angle Intra-fractional Verification (LIVE) System for SBRT Treatments

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

Publications:

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

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. 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. 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. 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.Journal of Applied Clinical Medical Physics, Apr. 2020. Epmc, doi:10.1002/acm2.12869.
URI
https://scholars.duke.edu/individual/pub1436642
PMID
32239746
Source
epmc
Published In
Journal of Applied Clinical Medical Physics
Published Date
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

Volumetric cine magnetic resonance imaging (VC-MRI) using motion modeling, free-form deformation and multi-slice undersampled 2D cine MRI reconstructed with spatio-temporal low-rank decomposition.

Background: The purpose of this study is to improve on-board volumetric cine magnetic resonance imaging (VC-MRI) using multi-slice undersampled cine images reconstructed using spatio-temporal k-space data, patient prior 4D-MRI, motion modeling (MM) and free-form deformation (FD) for real-time 3D target verification of liver and lung radiotherapy. Methods: A previous method was developed to generate on-board VC-MRI by deforming prior MRI images based on a MM and a single-slice on-board 2D-cine image. The two major improvements over the previous method are: (I) FD was introduced to estimate VC-MRI to correct for inaccuracies in the MM; (II) multi-slice undersampled 2D-cine images reconstructed by a k-t SLR reconstruction method were used for FD-based estimation to maintain the temporal resolution while improving the accuracy of VC-MRI. The method was evaluated using XCAT lung simulation and four liver patients' data. Results: For XCAT, VC-MRI estimated using ten undersampled sagittal 2D-cine MRIs resulted in volume percent difference/volume dice coefficient/center-of-mass shift of 9.77%±3.71%/0.95±0.02/0.75±0.26 mm among all scenarios based on estimation with MM and FD. Adding FD optimization improved VC-MRI accuracy substantially for scenarios with anatomical changes. For patient data, the mean tumor tracking errors were 0.64±0.51, 0.62±0.47 and 0.24±0.24 mm along the superior-inferior (SI), anterior-posterior (AP) and lateral directions, respectively, across all liver patients. Conclusions: It is feasible to improve VC-MRI accuracy while maintaining high temporal resolution using FD and multi-slice undersampled 2D cine images for real-time 3D target verification.
Authors
Harris, W; Yin, F-F; Cai, J; Ren, L
MLA Citation
Harris, Wendy, et al. “Volumetric cine magnetic resonance imaging (VC-MRI) using motion modeling, free-form deformation and multi-slice undersampled 2D cine MRI reconstructed with spatio-temporal low-rank decomposition.Quant Imaging Med Surg, vol. 10, no. 2, Feb. 2020, pp. 432–50. Pubmed, doi:10.21037/qims.2019.12.10.
URI
https://scholars.duke.edu/individual/pub1434872
PMID
32190569
Source
pubmed
Published In
Quantitative Imaging in Medicine and Surgery
Volume
10
Published Date
Start Page
432
End Page
450
DOI
10.21037/qims.2019.12.10

Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes

© 2020 IOP Publishing Ltd. Purpose: to develop digital phantoms for characterizing inconsistencies among radiomics extraction methods based on three radiomics toolboxes: CERR (Computational Environment for Radiological Research), IBEX (imaging biomarker explorer), and an in-house radiomics platform. Materials and Methods: we developed a series of digital bar phantoms for characterizing intensity and texture features and a series of heteromorphic sphere phantoms for characterizing shape features. The bar phantoms consisted of n equal-width bars (n = 2, 4, 8, or 64). The voxel values of the bars were evenly distributed between 1 and 64. Starting from a perfect sphere, the heteromorphic sphere phantoms were constructed by stochastically attaching smaller spheres to the phantom surface over 5500 iterations. We compared 61 features typically extracted from three radiomics toolboxes: (1) CERR (2) IBEX (3) in-house toolbox. The degree of inconsistency was quantified by concordance correlation coefficient (CCC) and Pearson correlation coefficient (PCC). Sources of discrepancies were characterized based on differences in mathematical definition, pre-processing, and calculation methods. Results: For the intensity and texture features, only 53%, 45%, 55% features demonstrated perfect reproducibility (CCC = 1) between in-house/CERR, in-house/IBEX, and CERR/IBEX comparisons, while 71%, 61%, 61% features reached CCC > 0.8 and 25%, 39%, 39% features were with CCC < 0.5, respectively. Meanwhile, most features demonstrated PCC > 0.95. For shape features, the toolboxes produced similar (CCC > 0.98) volume yet inconsistent surface area, leading to inconsistencies in other shape features. However, all toolboxes resulted in PCC > 0.8 for all shape features except for compactness 1, where inconsistent mathematical definitions were observed. Discrepancies were characterized in pre-processing and calculation implementations from both type of phantoms. Conclusions: Inconsistencies among radiomics extraction toolboxes can be accurately identified using the developed digital phantoms. The inconsistencies demonstrate the significance of implementing quality assurance (QA) of radiomics extraction for reproducible and generalizable radiomic studies. Digital phantoms are therefore very useful tools for QA.
Authors
Chang, Y; Lafata, K; Wang, C; Duan, X; Geng, R; Yang, Z; Yin, FF
MLA Citation
Chang, Y., et al. “Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes.” Biomedical Physics and Engineering Express, vol. 7, no. 2, Jan. 2020. Scopus, doi:10.1088/2057-1976/ab779c.
URI
https://scholars.duke.edu/individual/pub1435901
Source
scopus
Published In
Biomedical Physics & Engineering Express
Volume
7
Published Date
DOI
10.1088/2057-1976/ab779c

Research Areas:

Bioinformatics
Medical physics