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.
Approximately 2.5% of the proton range uncertainty comes from computed tomography (CT) number to material characteristic conversion. We aim to conquer this CT-to-material conversion error by proposing a multimodal imaging framework to enable deep learning (DL)-based material mass density inference using dual-energy CT (DECT) and magnetic resonance imaging (MRI). To ensure the robustness of DL models, we integrated physics insights into the framework to regularize DL models and achieve DL using small datasets. Five MRI-compatible phantoms were created from tissue-mimicking materials that served as a ground true reference to validate the proposed framework. The reference mass densities for each phantom were measured by a 150 MeV proton beam. Multimodal images were acquired from T1- and T2-weighted images and DECT images as training and validation data for DL. Residual networks (ResNet) were implemented to evaluate the feasibility of the proposed framework. ResNet-DE-MR denotes that ResNet was trained with MRI and DECT images, while ResNet-DE presents that only DECT images were used to train ResNet. ResNet was also compared to an empirical DECT model. Meanwhile, a retrospective patient case was included in the study to demonstrate the proof of concept for the proposed framework. The phantom validation experiment showed that ResNet-DE-MR achieved mass density errors of -0.4%, 0.3%, 0.4%, 0.7%, and -0.2% for adipose, muscle, liver, skin, and bone. The proposed DL-based multimodal imaging framework was demonstrated to enable accurate material mass density inference using DECT and MR images. The framework can potentially improve the treatment quality for proton therapy by reducing proton range uncertainty.
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