Prof. Ali Fatemi Profile

Prof. Ali Fatemi

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Proceedings Article | 2 April 2024 Presentation + Paper

MRI data consistency guided conditional diffusion probabilistic model for MR imaging acceleration

Mojtaba Safari, Xiaofeng Yang, Ali Fatemi

Proceedings Volume 12930, 129300R (2024) https://doi.org/10.1117/12.3002863

KEYWORDS: Magnetic resonance imaging, Data modeling, Diffusion, Sampling rates, Tumors, Medical image reconstruction, Image restoration, Education and training, Visualization, Motion models

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The long acquisition time required for high-resolution Magnetic Resonance Imaging (MRI) leads to patient discomfort, increased likelihood of voluntary and involuntary movements, and reduced throughput in imaging centers. This study proposed a novel method that leverages MRI physics to incorporate data consistency during the training of a conditional diffusion probabilistic model, which we refer to as the data consistency-guided conditional diffusion probabilistic model (DC-CDPM). This model aimed to reconstruct high-resolution contrast enhanced T1W MRI from partially sampled data. The DC-CDPM utilized the conjugate gradient optimization method to minimize data consistency loss between reconstructed MRI images and fully sampled unknown MRI images. Further, a diffusion probabilistic model conditioned on the optimization’s output was trained to reconstruct the fully sampled MRI. The publicly available dataset of 230 post-surgery patients with different brain tumors was used in this study to train the model. The equidistant under-sampling method was implemented to simulate four different under-sampling levels. The qualitative and quantitative comparisons were done between DC-CDPM and an exactly similar CDPM model except not conditioned on the optimization output. Qualitatively, the DC-CDPM could reconstruct fully sampled images compared with CDPM. Furthermore, the image profile along a tumor indicated better performance of DC-CDPM. Quantitatively, the DC-CDPM outperformed CDPM in four out of six quantitative metrics and had a consistent performance throughout the different under-sampling levels. Our method could allow us to perform brain imaging with substantially lower acquisition time while achieving similar image quality of fully sampled MRI images with a long acquisition time.

Proceedings Article | 2 April 2024 Poster + Paper

Information maximized U-Nets for vestibular schwannoma segmentation using MRI with missing modality

Mojtaba Safari, Xiaofeng Yang, Ali Fatemi

Proceedings Volume 12930, 129301H (2024) https://doi.org/10.1117/12.3000647

KEYWORDS: Education and training, Magnetic resonance imaging, Cochlea, Tumors, Image segmentation, Data modeling, Deep learning, Cancer, Performance modeling, Tumor growth modeling

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Several Magnetic Resonance Imaging (MRI) sequences are acquired for diagnosis and treatment. MRI with excellent soft-tissue contrast is desired for post-processing algorithms such as tumor segmentation. However, their performance markedly dropped due to the variation in medical imaging protocols or missing information. This study proposed a co-training deep learning algorithm for segmenting the vestibular schwannoma (VS) cancer. Our model was trained on both contrast-enhanced T1W (ceT1W) and high-resolution T2W (hrT2W) MRI sequences to segment Vestibular Schwannoma (VS) cancer and cochlea. Our model utilized content and style matching mechanisms to infuse the informative features from the network trained using full modality into the network trained using missing modality. Our model was trained using the publicly available Vestibular-Schwannoma-SEG dataset, which consists of 242 patients with ceT1W and hrT2W MRI sequences. The dataset was split into two non-overlapping groups: training (n=210) and testing (n=32). Three metrics were reported, including Dice Score (DCS), Relative Volume Error (RVE), and area under the receiver operating characteristic (AUC-ROC) curve. Our method had a superior performance to segment tumor compared with the baseline with (DCS, RVE, AUC-ROC) of (0.89, 3.57, 0.96) and (0.94, 3.10, 0.97) when ceT1W and hrT2W were missed, respectively. Similar performance was observed for segmenting the cochlea when hrT2W was missed with (DCS, RVE, AUC-ROC) of (0.68, 14.06, 0.80). Our model is robust against missing sequences, which is common in clinical settings. It could benefit clinical centers with missing data or different imaging protocols.

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