Prof. Jihong Wang Profile

Prof. Jihong Wang

Assistant Professor at Univ of Texas MD Anderson Cancer Ctr

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Publications (7)

SPIE Journal Paper | 6 November 2023 Open Access

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform for head and neck cancer

Joint Head and Neck Radiotherapy-MRI Development Cooperative, Travis Salzillo, M. Alex Dresner, Ashley Way, Kareem Wahid, Brigid McDonald, Sam Mulder, Mohamed Naser, Renjie He, Yao Ding, Alison Yoder, Sara Ahmed, Kelsey Corrigan, Gohar Manzar, Lauren Andring, Chelsea Pinnix, R. Jason Stafford, Abdallah S. Mohamed, John Christodouleas, Jihong Wang, Clifton David Fuller

JMI, Vol. 10, Issue 06, 065501, (November 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.065501

KEYWORDS: Image segmentation, Image quality, Signal to noise ratio, Radiotherapy, Muscles, Lymph nodes, Neck, Magnetic resonance imaging, Head, Image analysis

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PurposeTo improve segmentation accuracy in head and neck cancer (HNC) radiotherapy treatment planning for the 1.5T hybrid magnetic resonance imaging/linear accelerator (MR-Linac), three-dimensional (3D), T2-weighted, fat-suppressed magnetic resonance imaging sequences were developed and optimized.ApproachAfter initial testing, spectral attenuated inversion recovery (SPAIR) was chosen as the fat suppression technique. Five candidate SPAIR sequences and a nonsuppressed, T2-weighted sequence were acquired for five HNC patients using a 1.5T MR-Linac. MR physicists identified persistent artifacts in two of the SPAIR sequences, so the remaining three SPAIR sequences were further analyzed. The gross primary tumor volume, metastatic lymph nodes, parotid glands, and pterygoid muscles were delineated using five segmentors. A robust image quality analysis platform was developed to objectively score the SPAIR sequences on the basis of qualitative and quantitative metrics.ResultsSequences were analyzed for the signal-to-noise ratio and the contrast-to-noise ratio and compared with fat and muscle, conspicuity, pairwise distance metrics, and segmentor assessments. In this analysis, the nonsuppressed sequence was inferior to each of the SPAIR sequences for the primary tumor, lymph nodes, and parotid glands, but it was superior for the pterygoid muscles. The SPAIR sequence that received the highest combined score among the analysis categories was recommended to Unity MR-Linac users for HNC radiotherapy treatment planning.ConclusionsOur study led to two developments: an optimized, 3D, T2-weighted, fat-suppressed sequence that can be disseminated to Unity MR-Linac users and a robust image quality analysis pathway that can be used to objectively score SPAIR sequences and can be customized and generalized to any image quality optimization protocol. Improved segmentation accuracy with the proposed SPAIR sequence will potentially lead to improved treatment outcomes and reduced toxicity for patients by maximizing the target coverage and minimizing the radiation exposure of organs at risk.

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Proceedings Article | 16 March 2015 Paper

A comparative study of two prediction models for brain tumor progression

Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li

Proceedings Volume 9399, 93990W (2015) https://doi.org/10.1117/12.2082645

KEYWORDS: Tumors, Brain, Magnetic resonance imaging, Data modeling, Neuroimaging, Diffusion tensor imaging, Machine learning, Performance modeling, Neural networks, Principal component analysis

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MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.

We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named "Dropout" can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012).

We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region.

Proceedings Article | 17 March 2011 Paper

Histogram analysis of ADC in brain tumor patients

Debrup Banerjee, Jihong Wang, Jiang Li

Proceedings Volume 7961, 79613V (2011) https://doi.org/10.1117/12.878259

KEYWORDS: Tumors, Brain, Expectation maximization algorithms, Magnetic resonance imaging, Diffusion tensor imaging, Magnesium, Neuroimaging, Cancer, Medical imaging, Image registration

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At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, post-contrast T1) were registered to the corresponding DTI scan at patient's first visit. Conventionally, hyper-intensity regions on T1-post contrast images are believed to represent the core tumor region while regions highlighted by FLAIR may overestimate tumor size. Thus we annotated tumor regions on the T1-post contrast scans and ADC intensity values for pixels were extracted inside tumor regions as defined on T1-post scans. We fit a mixture Gaussian (MG) model for the extracted pixels using the Expectation-Maximization (EM) algorithm, which produced a set of parameters (mean, various and mixture coefficients) for the MG model. This procedure was performed for each visits resulting in a series of GM parameters. We studied the parameters fitted for ADC and see if they can be used as indicators for tumor progression. Additionally, we studied the ADC characteristics in the peri-tumoral region as identified by hyper-intensity on FLAIR scans. The results show that ADC histogram analysis of the tumor region supports the two compartment model that suggests the low ADC value subregion corresponding to densely packed cancer cell while the higher ADC value region corresponding to a mixture of viable and necrotic cells with superimposed edema. Careful studies of the composition and relative volume of the two compartments in tumor region may provide some insights in the early assessment of tumor response to therapy for recurrence brain cancer patients.

Proceedings Article | 9 March 2011 Paper

Prediction of brain tumor progression using multiple histogram matched MRI scans

Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic McKenzie, Jihong Wang

Proceedings Volume 7963, 79632U (2011) https://doi.org/10.1117/12.878208

KEYWORDS: Tumors, Magnetic resonance imaging, Brain, Neuroimaging, Diffusion tensor imaging, Data modeling, Scanners, Machine learning, Tissues, Image registration

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Proceedings Article | 9 March 2010 Paper

Prediction of brain tumor progression using a machine learning technique

Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic McKenzie, Jihong Wang

Proceedings Volume 7624, 762425 (2010) https://doi.org/10.1117/12.844035

KEYWORDS: Tumors, Brain, Magnetic resonance imaging, Machine learning, Diffusion tensor imaging, Neuroimaging, Tissues, Data modeling, Feature extraction, Diffusion

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Proceedings Article | 27 February 2009 Paper

Using CSF as an internal quality assurance tool in diffusion tensor imaging studies of brain tumor

Jihong Wang, Yufei Shen, John DeGroot, Yuzhong Shen, Jiang Li

Proceedings Volume 7262, 726203 (2009) https://doi.org/10.1117/12.812128

KEYWORDS: Diffusion tensor imaging, Tumors, Brain, Scanners, Neuroimaging, Magnetic resonance imaging, Data acquisition, Molecules, Tissues, Diffusion

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Proceedings Article | 12 April 2002 Paper

Interactive method of assessing the characteristics of softcopy display using observer performance tests

Jihong Wang, Qi Peng

Proceedings Volume 4686, (2002) https://doi.org/10.1117/12.462677

KEYWORDS: Spatial resolution, Picture Archiving and Communication System, Displays, Software, Visualization, Image quality, Medical imaging, Target detection, Image processing, Contrast sensitivity

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