Fang-Cheng (Frank) Yeh, MD, PhD

Assistant Professor
Director, Fiber Tractography Lab


Fang-Cheng Yeh

Contact

412-383-0871

Biography

Fang-Cheng (Frank) Yeh, MD, PhD, joined the Department of Neurological Surgery in 2016 as a tenure-track assistant professor.

Prior to joining the faculty at the University of Pittsburgh, Dr. Yeh received his MD degree from National Taiwan University and completed his PhD study in biomedical engineering at Carnegie Mellon University in 2014.

Dr. Yeh is currently working on diffusion MRI and its role as image biomarkers for neurological and psychiatric disorders. His research focuses on novel applications of computational methods to brain connectome research, a challenging field with a lot of known unknowns and unsolved questions that require extensive technological development. He has developed several diffusion MRI methods and applied them to both clinical and translational studies.

Dr. Yeh is known for his development of DSI Studio, an integrated platform for diffusion MRI analysis, fiber tracking, and 3D tractography visualization. In 2018 alone, DSI Studio facilitated more than 100 peer-reviewed publications. DSI Studio provides the core technique for “high accuracy fiber tracking,” which has been widely used by many research groups to investigate how major fiber pathways are affected by neurological and psychiatric diseases. In an open compettition sponsored by the International Society for Magnetic Resonance in Medicine (ISMRM) in 2015, Dr. Yeh’s method achieved the highest valid connection score (92.49%, ID:03) among 96 different approaches submitted by a total of 20 groups from around the world.

Dr. Yeh also developed WS-Recognizer, an open-source quantitative pathology tool that analyzes whole slide image and automatically recognizes targets. WS-Recognizer has been used to correlate pathology finding with MRI and visualize tissue characteristics in a panoramic view across the entire tissue section.

Specialized Areas of Interest

Diffusion MRI, tractography, network analysis, medical image analysis, pathology informatics.

Professional Organization Membership

International Society for Magnetic Resonance in Medicine 

Education & Training

MD, National Taiwan University, 2006
PhD, Biomedical Engineering, Carnegie Mellon University, 2014

Honors & Awards

Chancellor’s Commercialization Fund Award, Pitt Ventures First Gear Program, University of Pittsburgh, 2019

Selected Publications

Yeh FC, Vettel JM, Singh A, Poczos B, Grafton ST, Erickson KI, Tseng WI, Verstynen TD. Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome FingerprintsPLoS Comput Biol 12(11):e1005203, 2016.

Yeh FC, Badre D, Verstynen T. Connectometry: A statistical approach harnessing the analytical potential of the local connectome. Neuroimage 125:162-71, 2016.

Fernández-Miranda JC, Wang Y, Pathak S, Stefaneau L, Verstynen T, Yeh FC. Asymmetry, connectivity, and segmentation of the arcuate fascicle in the human brain. Brain Struct Funct 220(3):1665-80, 2014.

Wang Y, Fernández-Miranda JC, Verstynen T, Pathak S, Schneider W, Yeh FC. Rethinking the role of the middle longitudinal fascicle in language and auditory pathways. Cereb Cortex 23(10):2347-56, 2013.

Yeh FC, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng WY. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One 8(11):e80713, 2013.

Yeh FC, Tseng WY. NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage 58(1):91-9, 2011.

Yeh FC, Wedeen VJ, Tseng WY. (2010). Generalized q-sampling imaging. IEEE Trans Med Imaging 29(9):1626-35, 2010.

Yeh FC, Parwani AV, Pantanowitz L, Ho C. Automated grading of renal cell carcinoma using whole slide imaging. Journal of Pathology Informatics 5:23, 2014.

Yeh FC, Ye Q, Hitchens TK, Wu YL, Parwani AV, Ho C. Mapping stain distribution in pathology slides using whole slide imaging. Journal of Pathology Informatics 5:1, 2014.

A complete list of Dr. Yeh's publications can be reviewed through the National Library of Medicine's publication database.

Research Activities

1) Differential Tractography as a Track-Based Biomarker for Neuronal Injury

Diffusion MRI tractography has been used to map the axonal structure of human brain, but its ability to detect neuronal injury is yet to be explored. Here we report differential tractography, a new type of tractography that utilizes repeat MRI scans and a novel tracking strategy to map the exact segment of fiber pathways with neuronal injury. We examined differential tractography on multiple sclerosis, Huntington disease, amyotrophic lateral sclerosis, and epileptic patients. The results showed that the affected pathways shown by differential tractography matched well with the unique clinical symptoms of the patients, and the false discovery rate of the findings could be estimated using a sham setting to provide a reliability measurement. This novel approach enables a quantitative and objective method to monitor neuronal injury in individuals, allowing for diagnostic and prognostic evaluation of brain diseases.

2) Connectometry as a Surrogate Imaging Biomarker for in Huntington’s Disease

Huntington’s disease (HD) is a devastating neurodegenerative disorder characterized by adult-onset of motor, psychiatric, and cognitive anomaly. Due to its diverse disease manifestation, the HD community has long recognized the need for a more sensitive and objective disease assessment. Here we examined the feasibility of connectometry findings, as an imaging biomarker for evaluating the severity and progression of HD. Connectometry is a novel analysis that maps pathways with substantial change of diffusion pattern by comparing diffusion MRI data in a standard space. A total of 18 patients were recruited in this study and received repeated diffusion MRI scans for connectometry assessment, which was then correlated with their Unified Huntington’s Disease Rating Scale (UHDRS). Our results show that the volume of the affected pathways mapped by connectometry significantly correlated with UHDRS scores. Changes in connectometry also predicted changes in UHDRS with a moderate correlation power (r=0.5~0.6). Our study suggests that connectometry can be a surrogate imaging biomarker to complement the role of UHDRS and provide a robust and objective clinical assessment of disease progression in HD.

3) Automatic Removal of False Connections in Diffusion MRI Tractography Using Topology-Informed Pruning (TIP)

Diffusion MRI fiber tracking provides a non-invasive method for mapping the trajectories of human brain connections, but its false connection problem has been a major challenge. This study introduces topology-informed pruning (TIP), a method that automatically identifies singular tracts and eliminates them to improve the tracking accuracy. The accuracy of the tractography with and without TIP was evaluated by a team of 6 neuroanatomists in a blinded setting to examine whether TIP could improve the accuracy. The results showed that TIP improved the tracking accuracy by 11.93% in the single-shell scheme and by 3.47% in the grid scheme. The improvement is significantly different from a random pruning (p-value < 0.001). The diagnostic agreement between TIP and neuroanatomists was comparable to the agreement between neuroanatomists. The proposed TIP algorithm can be used to automatically clean up noisy fibers in deterministic tractography, with a potential to confirm the existence of a fiber connection in basic neuroanatomical studies or clinical neurosurgical planning.

Media Appearances