Dr. Jiahan Zhang Profile

Dr. Jiahan Zhang

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Publications (5)

Proceedings Article | 4 April 2022 Presentation + Paper

Deep-learning-based modulated radiotherapy dose plan prediction with integration of non-modulated dose distribution

Shadab Momin, Yang Lei, Jiahan Zhang, Tonghe Wang, Justin Roper, Jeffrey Bradley, Pretesh Patel, Tian Liu, Xiaofeng Yang

Proceedings Volume 12034, 1203418 (2022) https://doi.org/10.1117/12.2612230

KEYWORDS: Modulation, Radiotherapy, Pancreatic cancer, Computed tomography, Statistical analysis, Pancreas, Liver, Kidney, Feature extraction, Convolutional neural networks

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Recently, various deep learning (DL)-based frameworks have been proposed for predicting dose distributions for radiotherapy treatment, which serve as initial dose maps and provide treatment planners with optimization starting points. This can be advantageous for dynamic treatment sites such as pancreatic cancer due to large patient to patient anatomic variations. DL-based methods thus far have used CT and contour maps to train DL networks for predictions of dose distributions. While these inputs provide important information such as density variations within a CT slice as well as spatial locations of various structures within the CT slice, these inputs do not provide radiation dose deposition information, which is typically used in inverse optimization algorithms during treatment planning to reach the desired dose distribution. In this work, we propose a new deep learning-based correction method for generating modulated dose map from non-modulated dose map, which is the dose distribution achieved from uniform beamlet intensities. Our method is consisted of two subnetworks: fusion module and correction module. Fusion module extracts features from CT and contours, including GTV contour and multi-OAR contours. Through hierarchical layers, the extracted feature map is represented as a target activation map, which can well represent the distribution of modulated dose map. To include the geometric information, the target activation map together with non-modulated dose map are then transferred into the correction module to obtain the estimated modulated dose map. Histogram matching loss with traditional losses are used. The goal of histogram matching loss is used to match the distribution of estimated modulated dose to that of ground truth modulated dose map. We performed 3-fold cross-validation on a dataset consisted of 30 patients. Our proposed method generated comparable predictions, compared to the ground truth, for 20/23 clinically relevant dose volume parameters. Overall results demonstrate the feasibility and efficacy of our proposed DL-based method for pancreas SBRT dose prediction.

SPIE Journal Paper | 22 November 2016

Initial performance studies of a wearable brain positron emission tomography camera based on autonomous thin-film digital Geiger avalanche photodiode arrays

Charles Schmidtlein, James Turner, Michael Thompson, Krishna Mandal, Ida Häggström, Jiahan Zhang, John Humm, David Feiglin, Andrzej Krol

JMI, Vol. 4, Issue 01, 011003, (November 2016) https://doi.org/10.1117/12.10.1117/1.JMI.4.1.011003

KEYWORDS: Positron emission tomography, Sensors, Scintillators, Brain, Imaging systems, Photons, Cameras, Neuroimaging, Crystals, Signal attenuation

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Proceedings Article | 29 March 2016 Paper

Performance modeling of a wearable brain PET (BET) camera

A. Krol, C. Schmidtlein, J. Turner, M. Thompson, K. Mandal, J. Zhang, J. Humm, D. Feiglin, I. Häggström

Proceedings Volume 9788, 978806 (2016) https://doi.org/10.1117/12.2217020

KEYWORDS: Positron emission tomography, Monte Carlo methods, Scintillators, Sensors, Brain, Photons, Statistical analysis, Neuroimaging, Cameras, Spherical lenses

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Purpose: To explore, by means of analytical and Monte Carlo modeling, performance of a novel lightweight and low-cost wearable helmet-shaped Brain PET (BET) camera based on thin-film digital Geiger Avalanche Photo Diode (dGAPD) with LSO and LaBr₃ scintillators for imaging in vivo human brain processes for freely moving and acting subjects responding to various stimuli in any environment.

Methods: We performed analytical and Monte Carlo modeling PET performance of a spherical cap BET device and cylindrical brain PET (CYL) device, both with 25 cm diameter and the same total mass of LSO scintillator. Total mass of LSO in both the BET and CYL systems is about 32 kg for a 25 mm thick scintillator, and 13 kg for 10 mm thick scintillator (assuming an LSO density of 7.3 g/ml). We also investigated a similar system using an LaBr3 scintillator corresponding to 22 kg and 9 kg for the 25 mm and 10 mm thick systems (assuming an LaBr₃ density of 5.08 g/ml). In addition, we considered a clinical whole body (WB) LSO PET/CT scanner with 82 cm ring diameter and 15.8 cm axial length to represent a reference system. BET consisted of distributed Autonomous Detector Arrays (ADAs) integrated into Intelligent Autonomous Detector Blocks (IADBs). The ADA comprised of an array of small LYSO scintillator volumes (voxels with base a×a: 1.0 ≤ a ≤ 2.0 mm and length c: 3.0 ≤ c ≤ 6.0 mm) with 5–65 μm thick reflective layers on its five sides and sixth side optically coupled to the matching array of dGAPDs and processing electronics with total thickness of 50 μm. Simulated energy resolution was 10.8% and 3.3% for LSO and LaBr₃ respectively and the coincidence window was set at 2 ns. The brain was simulated as a sphere of uniform F-18 activity with diameter of 10 cm embedded in a center of water sphere with diameter of 10 cm.

Results: Analytical and Monte Carlo models showed similar results for lower energy window values (458 keV versus 445 keV for LSO, and 492 keV versus 485 keV for LaBr₃), and for the relative performance of system sensitivity. Monte Carlo results further showed that the BET geometry had >50% better noise equivalent count (NEC) performance relative to the CYL geometry, and >1100% better performance than a WB geometry for 25 mm thick LSO and LaBr₃. For 10 mm thick LaBr₃ equivalent mass systems LSO (7 mm thick) performed ~40% higher NEC than LaBr₃. Analytic and Monte Carlo simulations also showed that 1×1×3 mm scintillator crystals can achieve ~1.2 mm FWHM spatial resolution.

Conclusions: This study shows that a spherical cap brain PET system can provide improved NEC while preserving spatial resolution when compared to an equivalent dedicated cylindrical PET brain camera and shows greatly improved PET performance relative to a conventional whole body PET/CT. In addition, our simulations show that LSO will generally outperform LaBr₃ for NEC unless the timing resolution for LaBr₃ is considerably smaller than presently used for LSO, i.e. well below 300 ps.

Proceedings Article | 18 March 2015 Paper

SPECT reconstruction using DCT-induced tight framelet regularization

Andrzej Krol, Jiahan Zhang, Si Li, C. Schmidtlein, Yuesheng Xu, Edward Lipson, David Feiglin

Proceedings Volume 9412, 94123H (2015) https://doi.org/10.1117/12.2082118

KEYWORDS: Expectation maximization algorithms, Reconstruction algorithms, Wavelets, Single photon emission computed tomography, Gaussian filters, Image restoration, Image filtering, Spatial frequencies, CT reconstruction, Monte Carlo methods

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Proceedings Article | 19 March 2013 Paper

Semi-dynamic preconditioned alternating projection MAP ECT reconstruction from low-dose ECT

Andrzej Krol, Si Li, Lixin Shen, Yuesheng Xu, Jiahan Zhang, Levon Vogelsang, David Feiglin

Proceedings Volume 8668, 86685F (2013) https://doi.org/10.1117/12.2008153

KEYWORDS: Reconstruction algorithms, Expectation maximization algorithms, Optical spheres, Algorithm development, Monte Carlo methods, Data modeling, Spatial resolution, Gaussian filters, Convex optimization, Single photon emission computed tomography

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