Mr. Arash Javanmardi Profile

Arash Javanmardi

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Proceedings Article | 12 March 2024 Poster + Paper

The use of speckle decorrelation OCT to detect nanobubbles in cell pellet aggregates

Arash Javanmardi, Elizabeth Berndl, Dana Wegierak, Agata Exner, Michael Kolios

Proceedings Volume 12856, 1285605 (2024) https://doi.org/10.1117/12.3002019

KEYWORDS: Optical coherence tomography, Speckle, Signal to noise ratio, Ultrasonography, Autocorrelation, Nanobubbles, Confocal microscopy, Signal intensity

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Nanobubbles (NBs) have demonstrable potential for ultrasound imaging and therapeutic applications. Recent studies have even shown their capacity for cellular internalization, which has important implications for their in-vivo stability and bioaccumulation. Traditional methods for observing NBs often involve fluorescence labelling, which can influence NB behaviour. Moreover, these methods are unsuitable for detecting intact (acoustically active) NBs within a cellular environment. This study introduces a label-free approach employing optical coherence tomography (OCT) to investigate the temporal variations in speckle intensity of the OCT backscatter signal of cells interacting with NBs. The temporal variations in the signal intensity of cell aggregates result from the motion of subcellular scatterers within the cellular environment. In this work, we investigate whether internalized NBs modify the temporal variations in the signal intensity. For our experimental imaging set-up, we used a Thorlabs MEMS-VCSEL Swept Source OCT system with a central wavelength of 1300 nm to acquire M-Mode and B-Mode acquisitions. PC3 prostate cancer cells and in-house lipid-shelled NBs were used. The sensitivity of the speckle decorrelation technique was tested on our system using an intensity autocorrelation function on polystyrene microspheres and diluted NBs. Our study demonstrates that speckle decorrelation OCT can effectively detect NBs within a compact cell pellet under specific conditions and was verified using contrast-enhanced ultrasound. This approach provides an additional optical method for NB detection within cellular environments and holds the potential for broader applications in detecting NBs in in-vivo applications.

Proceedings Article | 4 April 2022 Poster + Paper

Multi-modality fusion coupled with deep learning for improved outcome prediction in head and neck cancer

Arash Javanmardi, Mahdi Hosseinzadeh, Ghasem Hajianfar, Amir Hossein Nabizadeh, Seyed Masoud Rezaeijo, Arman Rahmim, Mohammadreza Salmanpour

Proceedings Volume 12032, 120322I (2022) https://doi.org/10.1117/12.2612833

KEYWORDS: Image fusion, Positron emission tomography, Computed tomography, Cancer, Discrete wavelet transforms, Tumors, Neck, Head, Magnetic resonance imaging, Machine learning

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Multi-level multi-modality fusion radiomics is a promising technique with potential to improve the prognostication of cancer. We aim to use advanced fusion techniques on PET and CT images coupled with deep learning (DL) to improve outcome prediction in head and neck squamous cell carcinoma (HNSCC). In our study, 408 HNSCC patients were included from The Cancer Imaging Archive (TCIA) in a multi-center setting. Prognostic outcomes (binary classification) included overall survival (OS), distant metastasis (DM), locoregional recurrence (LR), and progression free survival (PFS). We utilized a DL algorithm with a 17-layer 3D convolutional neural network (CNN) architecture. Prior to training, each image underwent min-max-normalization, image-augmentation by using random rotations (0-20°) to improve the performance and generalizability of our model and followed by 5-fold-cross-validation. We employed 12 datasets, including CT, PET, and 10 image-level fused datasets. The best OS performance was achieved via Discrete-wavelet-transform (DWT) resulting in mean accuracy of 0.93±0.06. The best DM score was achieved via ratio of low-pass pyramid (RLPP), resulting in an accuracy of 0.95±0.02. Optimal LR and PFS scores were achieved using DWT and RLPP for LR, and Laplacian pyramid for PFS, resulting in accuracies of 0.90-0.92. Comparatively, when using a machine learning framework instead of deep learning, we obtained scores of 0.83, 0.90, and 0.87 for the prediction of OS, DM, and LR. Our study demonstrates that our multi-modality fusion techniques performed better than using standalone PET or CT in prognostication of HNSCC patients and that a high level of accuracy can be achieved for prognostication of HNSCC patients when combining multi modality fusion techniques with DL.

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