Dr. Nadja Kadom Profile

Dr. Nadja Kadom

at Emory Univ School of Medicine

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Publications (2)

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Proceedings Article | 10 April 2023 Poster + Paper

Quantification of radiation damage for proton craniospinal irradiation using magnetic resonance imaging

Chih-Wei Chang, Matt Goette, Nadja Kadom, Yinan Wang, Jacob Wynne, Tonghe Wang, Tian Liu, Natia Esiashvili, Jun Zhou, Bree Eaton, Xiaofeng Yang

Proceedings Volume 12468, 124681A (2023) https://doi.org/10.1117/12.2653665

KEYWORDS: Radiotherapy, Magnetic resonance imaging, Bone, Proton therapy, Computed tomography, Spine, Cancer, Oncology, In vivo imaging, Physics

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Radiation doses delivered to entire vertebral bodies are current standard practice for the growing pediatric proton craniospinal irradiation (CSI) patients who are growing children. This procedure prevents patients from developing radiation-induced growth impairment, but it will cause hematopoietic marrow suppression. We aim to develop a noninvasive method to verify radiation damage to the marrow in spine vertebrae during fractional treatment using multiple magnetic resonance imaging (MRI) scans. We identified five pediatric patients who received proton CSI treatment with prescription relative biological effectiveness doses of 36 Gy for the spine. Each patient underwent multiple MRI scans during the treatment using T1-weighted sequences. Sagittal MR images were analyzed and focused on lumbar spine regions. Multi-Gaussian models were used to fit histograms from different MR images to quantify the radiation-induced damage to the bone marrow. MR images acquired before the treatment served as the reference to ensure no radiation-induced damage was found. After the treatment started, radiation-induced fatty marrow filtration showed in the vertebral bodies. We defined the radiation-induced damage based on the ratio between fatty marrow imaging pixels and total pixels in spine marrow, L1-L5 level. Damage fractions increased rapidly when the vertebral bodies received doses between 14 Gy and 34 Gy. The maximum damage happened approximately 40 days from the treatment start. After that, bone marrow regeneration was observed, and the damage fractions decreased. The proposed method can potentially achieve adaptative proton plan modification on the fly.

Proceedings Article | 9 March 2018 Presentation + Paper

Content-oriented sparse representation (COSR) denoising in CT images

Huiqiao Xie, Nadja Kadom, Xiangyang Tang

Proceedings Volume 10573, 1057328 (2018) https://doi.org/10.1117/12.2293417

KEYWORDS: Denoising, Computed tomography, Tissues, Image segmentation, Image denoising, Head, X-ray computed tomography

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Denoising has been a challenging research subject in medical imaging in general and in CT imaging in particular, because the suppression of noise conflicts with the preservation of texture and edges. The purpose of this paper is to develop and evaluate a content-oriented sparse representation (COSR) denoising method in CT to effectively address this challenge. A CT image is firstly segmented by thresholding into several content-areas with similar materials, such as the air, soft tissues and bones. After being ex-painted smoothly outside it boundary, each content-area is sparsely coded by an atom from the dictionary that learnt from the image patches extracted from the corresponding content-area. The regenerated content-areas are finally aggregated to form the denoised CT image. The efficiency of image denoising and the ability of preserving texture and edges are demonstrated with a cylinder water phantom generated by simulation. The denoising performance of the proposed method is further tested with images of a pediatric head phantom and an anonymous pediatric patient that scanned by a state-of-the-art CT scanner, which shows that the proposed COSR denoising method can effectively preserve texture and edges while reducing noise. It is believed that this method would find its utility in extensive clinical and pre-clinical applications, such as dedicated and low dose CT, image segmentation and registration, and computer aided diagnosis (CAD) etc.

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