Average Subtraction Method for Image Reconstruction of Brain using ECVT for Tumor Detection
Corresponding email: rizkiedmiedison@uhamka.ac.id
Published at : 27 Nov 2020
Volume : IJtech
Vol 11, No 5 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i5.4325
Maharani, R., Edison, R.E., Ihsan, M.F., Taruno, W.P., 2020. Average Subtraction Method for Image Reconstruction of Brain using ECVT for Tumor Detection. International Journal of Technology. Volume 11(5), pp. 995-1004
| Rizky Maharani | Neuroscience Center – University of Muhammadiyah Prof Dr. HAMKA, Gandaria IV no. 24, South Jakarta 12120, Indonesia |
| Rizki Edmi Edison | Neuroscience Center – University of Muhammadiyah Prof Dr. HAMKA, Gandaria IV no. 24, South Jakarta 12120, Indonesia |
| Muhammad Fathul Ihsan | CTECH Labs Edwar Technology, Jalur Sutera Kav. Spektra Blok 23 BC No. 10-12, Alam Sutera, Tangerang, Banten 15320, Indonesia |
| Warsito Purwo Taruno | CTECH Labs Edwar Technology, Jalur Sutera Kav. Spektra Blok 23 BC No. 10-12, Alam Sutera, Tangerang, Banten 15320, Indonesia |
Brain
Electrical Capacitance Volume Tomography (Brain ECVT) is a new technique that
realizes real-time volumetric imaging of dynamic changes in electrical activity
of the brain and allows total interrogation of the whole volume inside a
helmet-shaped sensor. The technique has been used for investigating numerous
brain functional abnormalities, including brain tumors, based on permittivity
different from the tumor case, as compared to the normal brain. However,
interpretation of the conventional Brain ECVT image is not practical for
clinical purposes, as the image resolutions are high in the cortical area and
lower in the middle region. The technique provides relatively good sensitivity
when the tumor is located near the cortex. In this study, we developed a novel
method, namely the average subtraction technique, to process the reconstructed
image of the brain obtained by Brain ECVT. The technique generates a
three-dimensional intracranial distribution of permittivity that correlates
with the electrical activity map of the brain with improved resolution in the
center region of the brain. The technique provides better insight into brain
intracranial electrical activity, which is quite distinctive when there is
tumor development inside the brain. The technique may lead to better detection
of brain tumors inside the brain based on the electrical activity scan.
Brain ECVT; Brain tumor; Image reconstruction
In neurosciences, non-invasive imaging methods could provide
useful information in two broad areas: imaging of variations or abnormalities
in structure and imaging of normal or abnormal functional activity of the
brain. While the coding of information in brain pathways has long been of
interest in this field, the lack of methods capable of measuring the brain
structure in a simple and non-invasive fashion has hampered research. The ease
of diagnosing structural abnormalities in neurology has been transformed since
the development of X-ray CT in the 1970s and, more recently, MRI.
Real-time volumetric imaging of
human brain structural abnormalities based on the electric field was proposed
for the first time in 2013, with a device called a four- dimensional brain
electrical capacitance volume tomography (4D Brain ECVT; Taruno et al., 2013a). ECVT is an advanced
electrical capacitance tomography (ECT),
a technique for
However, brain tumors are
already known to have dielectric properties and different permittivity from the
normal brain (Yoo, 2004). Generally, an
abnormal mass in the brain may cause propagation signals that reach the cortex
of the brain. Furthermore, the phenomenon named “brain signal death” will
decrease the signals detected by the ECVT sensor (Taruno
et al., 2013b) producing specific image construction. However,
interpreting such images is not yet practical. In this research, we propose an
alternative algorithm for image reconstruction that will make it easier to
interpret brain tumor detection by using Brain ECVT.
In
this study, we developed an image reconstruction method, called average
subtraction, and applied it to a brain image obtained by Brain ECVT. The
technique provides a three-dimensional distribution of permittivity that
correlates with the electrical activity of the whole brain with an improved
resolution, particularly in the center region of the brain. The technique
showed a distinctive difference in the electrical activity of the intracranial
brain with tumor cases, as compared to the normal brain. The technique may lead
to better detection of brain tumors based on an electrical activity scan using
ECVT. Further quantification of electrical activity differences is still needed
for tumor detection, as the electrical activity may also vary in the presence
of brain stimulations or other abnormalities that could affect brain
electricity.
Berawi, M.A., 2015. Technology Breakthrough: A
Need for Continuous Improvement. International
Journal of Technology, Volume 6(3), pp. 302–305
Bunyaratavej, K., Siwanuwatn, R., Chantra, K.,
Khaoroptham, S., 2010. Duration of Symptoms in Brain Tumors: Influencing
Factors and Its Value in Predicting Malignant Tumors. Journal of the Medical Association of Thailand, Volume 93(8), pp.
903–910
Butowski, N.A., 2015. Epidemiology and
Diagnosis of Brain Tumors. Continuum,
Volume 21(2 Neuro-oncology), pp. 301–313
Elfani, M., Putra, N.K., 2013. Biomedical
Engineering and Its Potential for Employment in Indonesia. International Journal of Technology, Volume 4(1), pp. 34–44
Hamilton, W., Kernick, D., 2007. Clinical Features
of Primary Brain Tumors: A Case-Control Study using Electronic Primary Care
Records. The British Journal of General
Practice, Volume 57(542), pp. 695–699
Hanid, M., Mohamed, O., Othman, M., Danuri,
M.S.M., Mei Ye, K., Berawi, M.A., 2019. Critical Success Factors (CSFS) in
University-Industry Collaboration (UIC) Projects in Research Universities. International Journal of Technology.
Volume 10(4), pp. 667–676
Holle, D., Obermann, M., 2013. The Role of
Neuroimaging in the Diagnosis of Headache Disorders. Therapeutic Advances in Neurological Disorders, Volume 6(6), pp.
369–374
Taruno, W.P., Baidillah, M.R., Sulaiman, R.I.,
Ihsan, M.F., Fatmi, S.E., Muhtadi, A.H., Haryanto, F., 2013a. 4D Brain Activity
Scanner using Electrical Capacitance Volume Tomography (ECVT). In: Biomedical Imaging (ISBI), IEEE 10th
International Symposium on, San Francisco, pp. 1006–1009
Taruno, W.P., Baidiliah, M.R., Sulaiman, R.I.,
Ishan, M.F., Yusaf, A., Widada, W., Aljohani, M., 2013b. Brain Tumor Detection
using Electrical Capacitance Volume Tomography (ECVT). In: Neural Engineering, 6th International IEEE EMBS
Conference, pp. 743–746
Taruno, W.P., Ihsan, M.F., Baidiliah, M.R.,
Tandian, T.A., Mahandra, M., Aljohani, M., 2014. Electrical Capacitance Volume
Tomography for Human Brain Motion Activity Observation. In: Middle East Conference on Biomedical Engineering (MECBME), pp.
147–150
Walker, D., Hamilton, W., Waller, F.M., Watts,
C., 2013. Strategies to Accelerate Diagnosis of Primary Brain Tumors at the
Primary-Secondary Care Interface in Children and Adults. CNS Oncology, Volume 2(5), pp. 447–462
Wang, F., Marashdeh, Q., Fan, L.S., Warsito,
W., 2010. Electrical Capacitance Volume Tomography: Design and Applications. Sensors, Volume 10(3), pp. 1890–1917
Warsito, W., Marashdeh, Q., Fan, L.S., 2007.
Electrical Capacitance Volume Tomography. Sensor
Journal, Volume 27(4), pp. 525–535
Wrench, M., Minn, Y., Chew, T., Bondy, M.,
Berger, M., 2002. Epidemiology of Primary Brain Tumors: Current Concepts and
Review of the Literature. Neuro-Oncology,
Volume 4(4), pp. 278–299
Yoo, D.S., 2004. The Dielectric Properties of
Cancerous Tissues in a Nude Mouse Xenograft Model. Bioelectromagnetics, Volume 25 (7), pp. 492–497