Publications by authors named "Hamid-Reza Saligheh-Rad"

2 Publications

  • Page 1 of 1

A novel quantification method for low-density gel dosimeter.

J Cancer Res Ther 2018 Jan-Mar;14(2):292-299

Department of Physics, Kent State University, Kent, OH, USA.

Aim: Low signal-to-noise ratio (SNR) images of lung-like (low-density [LD]) gel dosimeters, compared to unit-density (UD) gels, necessitate the use of different quantification methods.

Setting And Design: In this study, a new method is introduced based on noise correction and exponential (NCEXP) fitting. The feasibility of NCEXP method for quantifying dose absorption in LD gels is evaluated.

Materials And Methods: Sensitivity, dose resolution, detectable dynamic range, and correlation of the calibration curve for both UD and LD gel dosimeters are the parameters, which we analyze to investigate the consequences of new method. Results of NCEXP method are compared to maximum likelihood estimation of rician distribution (MLE-R) and variable echo number (VAREC) quantification methods.

Results: Dose response of LD gel dosimeter shows wider detectable dynamic range as compared to UD gel. Using NCEXP method for both LD and UD dosimeter gels, a more sensitive calibration curve with a superior dose resolution is obtained. The advantage of new quantification method is more significant for LD dosimeter gel analysis, where SNR decreases as a result of higher absorbed doses (≥10 Gy). Despite the inverse effect of the VAREC method on detectable dose range of UD gel, no specific changes are observed in dynamic dose range of LD gel dosimeter with different quantification methods. The correlations obtained with different methods were approximately of the same order for UD and LD gels.

Conclusion: NCEXP method seems to be more effective than the MLE-R and VAREC methods for quantification of LD dosimeter gel, especially where high-dose absorption and steep-dose gradients exist such as those in intensity-modulated radiation therapy and stereotactic radiosurgery.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.4103/jcrt.JCRT_1_17DOI Listing
August 2018

Spatiotemporal features of DCE-MRI for breast cancer diagnosis.

Comput Methods Programs Biomed 2018 Mar 12;155:153-164. Epub 2017 Dec 12.

Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Medical Imaging Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran.

Background And Objective: Breast cancer is a major cause of mortality among women if not treated in early stages. Previous works developed non-invasive diagnosis methods using imaging data, focusing on specific sets of features that can be called spatial features or temporal features. However, limited set of features carry limited information, requiring complex classification methods to diagnose the disease. For non-invasive diagnosis, different imaging modalities can be used. DCE-MRI is one of the best imaging techniques that provides temporal information about the kinetics of the contrast agent in suspicious lesions along with acceptable spatial resolution.

Methods: We have extracted and studied a comprehensive set of features from spatiotemporal space to obtain maximum available information from the DCE-MRI data. Then, we have applied a feature fusion technique to remove common information and extract a feature set with maximum information to be used by a simple classification method. We have also implemented conventional feature selection and classification methods and compared them with our proposed approach.

Results: Experimental results obtained from DCE-MRI data of 26 biopsy or short-term follow-up proven patients illustrate that the proposed method outperforms alternative methods. The proposed method achieves a classification accuracy of 99% without missing any of the malignant cases.

Conclusions: The proposed method may help physicians determine the likelihood of malignancy in breast cancer using DCE-MRI without biopsy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cmpb.2017.12.015DOI Listing
March 2018