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Sahar Yousefi

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8 records found

Hierarchical Prediction of Registration Misalignment using a Convolutional LSTM

Application to Chest CT Scans

Journal article - Hessam Sokooti, Sahar Yousefi, Mohamed S. Elmahdy, B.P.F. Lelieveldt, B.P.F. Lelieveldt, M. Staring, M. Staring

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: 'correct' 0-3 mm, 'poor' 3-6 mm and 'wrong' over 6 mm. Rather than ...

Esophageal Tumor Segmentation in CT Images using a Dilated Dense Attention Unet (DDAUnet)

Journal article - Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Irene M. Lips, Mohammad T.Manzuri Shalmani, Roel T. Zinkstok, Frank J.W.M. Dankers, M. Staring, M. Staring

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feed ...

Personalized local SAR prediction for parallel transmit neuroimaging at 7T from a single T1-weighted dataset

Journal article - Wyger M. Brink, Sahar Yousefi, Prernna Bhatnagar, Prernna Bhatnagar, R.F. Remis, M. Staring, A. Webb

Purpose: Parallel RF transmission (PTx) is one of the key technologies enabling high quality imaging at ultra-high fields (≥7T). Compliance with regulatory limits on the local specific absorption rate (SAR) typically involves over-conservative safety margins to account for inters ...

A Novel Motion Detection Method Using 3D Discrete Wavelet Transform

Journal article - Sahar Yousefi, Sahar Yousefi, M.T. Manzuri Shalmani, Jeremy Lin, M. Staring, M. Staring

The problem of motion detection has received considerable attention due to the explosive growth of its applications in video analysis and surveillance systems. While the previous approaches can produce good results, the accurate detection of motion remains a challenging task due ...

An adaptive intelligence algorithm for undersampled knee MRI reconstruction

Journal article - Nicola Pezzotti, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Fransiscus van Gemert, Christophe Schuelke, Mariya Doneva, Tim Nielsen, B.P.F. Lelieveldt, B.P.F. Lelieveldt, M. Staring, M. Staring, None More Authors

Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based r ...

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

Journal article - Mohamed S. Elmahdy, Thyrza Jagt, M. Staring, M. Staring, Roel T. Zinkstok, Yuchuan Qiao, Rahil Shahzad, Hessam Sokooti, Sahar Yousefi, Luca Incrocci, C.A.M. Marijnen, Mischa Hoogeman

Purpose: To develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity-Modulated Proton Therapy (IMPT) of prostate cancer using elastix software and deep learning. Methods: A three-dimensional (3D) Convolutional Neural Network was trained for automatic bladder segmentation of the computed tomography (CT) scans. The automatic bladder segmentation alongside the computed tomography (CT) scan is jointly optimized to add explicit knowledge about the underlying anatomy to the registration algorithm. We included three datasets from different institutes and CT manufacturers. The first was used for training and testing the ConvNet, where the second and the third were used for evaluation of the proposed pipeline. The system performance was quantified geometrically using the dice similarity coefficient (DSC), the mean surface distance (MSD), and the 95% Hausdorff distance (HD). The propagated contours were validated clinically through generating the associated IMPT plans and compare it with the IMPT plans based on the manual delineations. Propagated contours were considered clinically acceptable if their treatment plans met the dosimetric coverage constraints on the manual contours. Results: The bladder segmentation network achieved a DSC of 88% and 82% on the test datasets. The proposed registration pipeline achieved a MSD of 1.29 ± 0.39, 1.48 ± 1.16, and 1.49 ± 0.44 mm for the prostate, seminal vesicles, and lymph nodes, respectively, on the second dataset and a MSD of 2.31 ± 1.92 and 1.76 ± 1.39 mm for the prostate and seminal vesicles on the third dataset. The automatically propagated contours met the dose coverage constraints in 86%, 91%, and 99% of the cases for the prostate, seminal vesicles, and lymph nodes, respectively. A Conservative Success Rate (CSR) of 80% was obtained, compared to 65% when only using intensity-based registration. Conclusion: The proposed registration pipeline obtained highly promising results for generating treatment plans adapted to the daily anatomy. With 80% of the automatically generated treatment plans directly usable without manual correction, a substantial improvement in system robustness was reached compared to a previous approach. The proposed method therefore facilitates more precise proton therapy of prostate cancer, potentially leading to fewer treatment-related adverse side effects.@en

Fast Dynamic Perfusion and Angiography Reconstruction Using an End-to-End 3D Convolutional Neural Network

Conference paper - Sahar Yousefi, Lydiane Hirschler, Merlijn van der Plas, Mohamed S. Elmahdy, Hessam Sokooti, Matthias J.P. van Osch, M. Staring

Hadamard time-encoded pseudo-continuous arterial spin labeling (te-pCASL) is a signal-to-noise ratio (SNR)-efficient MRI technique for acquiring dynamic pCASL signals that encodes the temporal information into the labeling according to a Hadamard matrix. In the decoding step, the ...

Fast Dynamic Perfusion and Angiography Reconstruction Using an End-to-End 3D Convolutional Neural Network

Conference paper - Sahar Yousefi, Lydiane Hirschler, Merlijn van der Plas, Mohamed S. Elmahdy, Hessam Sokooti, Matthias J.P. van Osch, M. Staring

Contributed

1 records found

Numerical Body Model Inference for Individualized RF exposure Prediction in Neuroimaging at 7T MRI

Master thesis - P. Bhatnagar, R.F. Remis, M. Staring, Wyger Brink, Sahar Yousefi

Magnetic Resonance Imaging is a popular modality for brain imaging in present times. The quality of the images depends on the strength of the magnetic field. An MRI scanner with a magnetic field strength of 3 Tesla(T) is pre-dominantly used for clinical purposes. However, with the advancement in technology, and the need to image finer image finer structures, we are gradually shifting to higher magnetic field strengths like 7T and above. One of the major bottlenecks in these systems is the bias induced in the images due to field inhomogeneities of higher field strengths. Yet another drawback of these systems is the increase in power dissipation in the tissues. This is measured by a quantity called Specific Absorption Rate(SAR). The goal of this thesis is to accurately predict SAR values for various volunteers as they may differ from subject to subject. Many homogeneous models have been created earlier to estimate the value of SAR, however, these estimates are often over-conservative and safe which compromises with image quality. A personalised numerical body model is created for all volunteers using relevant information derived from simulations performed on generalized models. Various tissues have to be segmented to create these 3D numerical body models. However, there has to be a trade-off between ease of segmentation and SAR accuracy. Keeping that in mind, it was found that the optimum number of tissues to get reliable SAR estimates is six. A deep learning method was then used for segmentation. A numerical body model was derived for all the volunteers using the deep learning segmentation. An adapted ForkNet, which is similar to U-net in architecture is used to segment these images. The SAR values derived from the predicted numerical body model and the original body model are similar, hence speeding up the process for SAR prediction. However, there are certain limitations of the thesis that can be addressed in the future. Inadequate data remains a major bottleneck for the project, increasing the data should result in improved segmentation, this can be addressed by acquiring more data. Another major drawback of the thesis is the segmentation accuracy, the ground truth segmentation is performed to the best of our knowledge, however, some errors are still present in the ground truth segmentation. The next steps for this project would be to acquire more data, train the data on multiple input sequences, use a 3D network and using localizer images that are acquired at the start of the MRI scan. Nonetheless, the principles established in this thesis confirm that a deep learning approach can be used to create numerical body models for SAR estimates. It also establishes the fact that these SAR estimates are comparable to the SAR estimates generated from the ground truth numerical body models.