Alen Docef, Ph.D. profile photo

Alen Docef, Ph.D.

Associate Professor and Associate Chair, Department of Electrical and Computer Engineering

Engineering West Hall, Room 242, Richmond, VA, UNITED STATES

(804) 827-7032

Professor Docef's research interests lie in medical image processing









Industry Expertise

  • Education/Learning
  • Research

Areas of Expertise

Medical Image ProcessingSignal Processor ArchitecturesDocument Compression for ArchivingEfficient Error-Resilient Network-Optimized Image and Video Coding


Georgia Institute of Technology

Ph.D., Electrical and Computer Engineering


Georgia Institute of Technology

M.S.E.E., Engineering


Polytechnic Institute of Bucharest

B.E., Engineering



  • IEEE Senior Member

Selected Articles

Reconstruction of a cone-beam CT image via forward iterative projection matching | Medical Physics


To demonstrate the feasibility of reconstructing a cone-beam CT(CBCT)image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set.

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On-line versus Off-line Accelerated Kernel Feature Analysis: Application to Computer-Aided Detection of Polyps in CT Colonography | Signal Processing


A semi-supervised learning method, the on-line accelerated kernel feature analysis (On-line AKFA) is presented. In On-line AKFA, features are extracted while data are being fed to the algorithm in small batches as the algorithm proceeds. The paper compares and contrasts the use of On-line AKFA and Off-line AKFA in CT colonography. On-line AKFA provides the flexibility to allow the feature space to dynamically adjust to changes in the input data with time during the training phase. The computational time, reconstruction accuracy, projection variance, and classification performance of the proposed method are experimentally evaluated for kernel principal component analysis (KPCA), Off-line AKFA, and On-line AKFA. Experimental results demonstrate a significant reduction in computation time for On-line AKFA compared to the other feature extraction methods considered in this paper.

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Reconstruction of 4D deformed CT for moving anatomy | International Journal of Computer Assisted Radiology and Surgery


To develop a 4DCT reconstruction technique that improves time resolution when the anatomy moves with respiration.

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Nonlinear Set Membership time series prediction of breathing | Signal Processing Conference


In radiation therapy, tumor motion induced by patient's respiration may lead to significant differences between the planned and delivered radiation dose. Compensating for tumor motion is therefore crucial for accurate and efficient treatment. The focus of the presented research is on real-time tumor tracking, due to its potential to overcome the limitations of other approaches, such as margin expansion, breath-holding, and gating. A real challenge in tumor tracking is the presence of delays in the treatment system. Prediction of tumor displacement is then necessary to overcome such delays. In this paper, we propose a method for the prediction of breathing signals based on a Nonlinear Set Membership (NSM) algorithm. The algorithm does not require the choice of a predefined functional form for the prediction model, and addresses the issue of measurement noise with minimal assumptions on its statistical properties. The NSM method was tested on nine clinical signals and its performance compared favorably with reported results as well as an optimized nonlinear neural network predictor.

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Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery | Medical Physics


The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiationdelivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiationdelivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiationdelivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy(IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position.

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