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:

Gustavo S. Montana Distinguished Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Professor in Radiation Oncology

Radiation Oncology
School of Medicine

Director of the Medical Physics Graduate Program at Duke Kunshan University

DKU Faculty
Duke Kunshan University

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

The 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:

Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN).

4D-CBCT is a powerful tool to provide respiration-resolved images for the moving target localization. However, projections in each respiratory phase are intrinsically under-sampled under the clinical scanning time and imaging dose constraints. Images reconstructed by compressed sensing (CS)-based methods suffer from blurred edges. Introducing the average-4D-image constraint to the CS-based reconstruction, such as prior-image-constrained CS (PICCS), can improve the edge sharpness of the stable structures. However, PICCS can lead to motion artifacts in the moving regions. In this study, we proposed a dual-encoder convolutional neural network (DeCNN) to realize the average-image-constrained 4D-CBCT reconstruction. The proposed DeCNN has two parallel encoders to extract features from both the under-sampled target phase images and the average images. The features are then concatenated and fed into the decoder for the high-quality target phase image reconstruction. The reconstructed 4D-CBCT using of the proposed DeCNN from the real lung cancer patient data showed (1) qualitatively, clear and accurate edges for both stable and moving structures; (2) quantitatively, low-intensity errors, high peak signal-to-noise ratio, and high structural similarity compared to the ground truth images; and (3) superior quality to those reconstructed by several other state-of-the-art methods including the back-projection, CS total-variation, PICCS, and the single-encoder CNN. Overall, the proposed DeCNN is effective in exploiting the average-image constraint to improve the 4D-CBCT image quality.
MLA Citation
Jiang, Zhuoran, et al. “Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN).Ieee Trans Radiat Plasma Med Sci, vol. 6, no. 2, Feb. 2022, pp. 222–30. Pubmed, doi:10.1109/trpms.2021.3133510.
URI
https://scholars.duke.edu/individual/pub1504701
PMID
35386935
Source
pubmed
Published In
Ieee Transactions on Radiation and Plasma Medical Sciences
Volume
6
Published Date
Start Page
222
End Page
230
DOI
10.1109/trpms.2021.3133510

Evaluation of two automated treatment planning techniques for multiple brain metastases using a single isocenter.

Two automated treatment planning techniques were evaluated for multiple brain metastases using a single isocenter. One technique is knowledge-based planning (KBP) using a stereotactic radiosurgery (SRS) model in Eclipse treatment planning system (TPS); and the other is the Multiple Brain Mets (MBM) SRS technique in Brainlab Elements TPS. Eighteen plans each with 3-10 lesions were used for the study. Plan evaluation metrics included the planning target volume (PTV) coverage, conformity index (CI), total monitor units (MUs), plan optimization time, brain V12 Gy, V8 Gy, and V5 Gy. Both the KBP and MBM planning techniques produced comparable plans to the manually generated clinical plans in terms of PTV coverage and CI. For irregularly shaped lesions, the KBP plans provided more conformal dose distribution to the PTV than the MBM plans. The KBP plans took significantly longer time to plan but have fewer MUs than the MBM plans. The MBM plans spared normal brain tissues better than the KBP plans in terms of V5 Gy.
Authors
Cui, G; Yang, Y; Yin, F-F; Yoo, D; Kim, G; Duan, J
MLA Citation
Cui, Guoqiang, et al. “Evaluation of two automated treatment planning techniques for multiple brain metastases using a single isocenter.J Radiosurg Sbrt, vol. 8, no. 1, 2022, pp. 47–54.
URI
https://scholars.duke.edu/individual/pub1515667
PMID
35387403
Source
pubmed
Published In
J Radiosurg Sbrt
Volume
8
Published Date
Start Page
47
End Page
54

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

Purpose: To develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. Methods: Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively. Results: The proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted. Conclusion: The developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.
Authors
Ji, H; Lafata, K; Mowery, Y; Brizel, D; Bertozzi, AL; Yin, F-F; Wang, C
MLA Citation
Ji, Hangjie, et al. “Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.Front Oncol, vol. 12, 2022, p. 895544. Pubmed, doi:10.3389/fonc.2022.895544.
URI
https://scholars.duke.edu/individual/pub1523882
PMID
35646643
Source
pubmed
Published In
Frontiers in Oncology
Volume
12
Published Date
Start Page
895544
DOI
10.3389/fonc.2022.895544

Motion robust 4D-MRI sorting based on anatomic feature matching: A digital phantom simulation study

Purpose: Motion artifacts induced by breathing variations are common in 4D-MRI images. This study aims to reduce the motion artifacts by developing a novel, robust 4D-MRI sorting method based on anatomic feature matching and applicable in both cine and sequential acquisition. Method: The proposed method uses the diaphragm as the anatomic feature to guide the sorting of 4D-MRI images. Initially, both abdominal 2D sagittal cine MRI images and axial MRI images were acquired. The sagittal cine MRI images were divided into 10 phases as ground truth. Next, the phase of each axial MRI image is determined by matching its diaphragm position in the intersection plane to the ground truth cine MRI. Then, those matched phases axial images were sorted into 10-phase bins which were identical to the ground truth cine images. Finally, 10-phase 4D-MRI were reconstructed from these sorted axial images. The accuracy of reconstructed 4D-MRI data was evaluated by comparing with the ground truth using the 4D eXtended Cardiac Torso (XCAT) digital phantom. The effects of breathing signal, including both regular (cosine function) and irregular (patient data) in both axial cine and sequential scanning modes, on reconstruction accuracy were investigated by calculating total relative error (TRE) of the 4D volumes, Volume-Percent-Difference (VPD) and Center-of-Mass-Shift (COMS) of the estimated tumor volume, compared with the ground truth XCAT images. Results: In both scanning modes, reconstructed 4D-MRI images matched well with ground truth with minimal motion artifacts. The averaged TRE of the 4D volume, VPD and COMS of the EOE phase in both scanning modes are 0.32%/1.20%/±0.05 ​mm for regular breathing, and 1.13%/4.26%/±0.21 ​mm for patient irregular breathing. Conclusion: The preliminary evaluation results illustrated the feasibility of the robust 4D-MRI sorting method based on anatomic feature matching. This method provides improved image quality with reduced motion artifacts for both cine and sequential scanning modes.
Authors
Yang, Z; Ren, L; Yin, FF; Liang, X; Cai, J
MLA Citation
Yang, Z., et al. “Motion robust 4D-MRI sorting based on anatomic feature matching: A digital phantom simulation study.” Radiation Medicine and Protection, vol. 1, no. 1, Mar. 2020, pp. 41–47. Scopus, doi:10.1016/j.radmp.2020.01.003.
URI
https://scholars.duke.edu/individual/pub1503996
Source
scopus
Published In
Radiation Medicine and Protection
Volume
1
Published Date
Start Page
41
End Page
47
DOI
10.1016/j.radmp.2020.01.003

Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients.

PURPOSE: To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. METHODS AND MATERIALS: The MC-4D-MRI technique integrates multi-parametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre-processing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of-inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume. RESULTS: MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1-w MRI, to -4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV inter-observer mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1-w 4D-MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior-inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5 mm in SI, AP, and left-right (LR) directions. CONCLUSION: We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC-4D-MRI. The technique might offer new perspectives on the image contrast of MRI and 4D-MRI in MR-guided radiotherapy.
Authors
Zhang, L; Yin, F-F; Li, T; Teng, X; Xiao, H; Harris, W; Ren, L; Kong, F-MS; Ge, H; Mao, R; Cai, J
MLA Citation
Zhang, Lei, et al. “Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients.Med Phys, vol. 48, no. 12, Dec. 2021, pp. 7984–97. Pubmed, doi:10.1002/mp.15314.
URI
https://scholars.duke.edu/individual/pub1500528
PMID
34706072
Source
pubmed
Published In
Med Phys
Volume
48
Published Date
Start Page
7984
End Page
7997
DOI
10.1002/mp.15314

Research Areas:

Bioinformatics
Medical physics