Analisis Variasi Nilai Velocity Encoding (VENC) terhadap Informasi Anatomi Citra Magnetic Resonance Venography (MRV) Brain pada Modalitas MRI 3 Tesla

Sugiyanto Sugiyanto, Ardi Soesilo Wibowo, I.G. Agung Brihaspaty Bhuana

Abstract


Background: The imaging for vein vessels or Magnetic Resonance Venography (MRV) has own role and advantages on MRI examinations, specifically for evaluating intracranial blood vein circulation. MRV generally uses Phase Contrast (PC) method and  PC-MRV imaging, there is an  important parameter to be considered, its velocity encoding or VENC. VENC is selected before the examination and has to be adjusted  the anticipated blood flow velocity in the examined organs. The right selection of VENC will result in optimum intracranial vessels images and accurate diagnose. The aim of this study is to figure out if there is significant effect of VENC on anatomical information of brain MRV and to find out which VENC the best  intracranial veins demonstrates.

Methods: This research was a quantitative study with quasi – experimental approach. Data were obtained from five healthy volunteers who were scanned by using a 3 Tesla MRI device in Radiology Department Siloam Lippo Village Hospitals. Each volunteer was scanned with five VENC variations: 10 cm/s, 15 cm/s, 20 cm/s, 25 cm/s, and 30 cm/s. The MRV images were assessed by two radiologist as the respondent. The Data were analyzed by simple linear regression test and Friedman test.

Result: The results showed that there was significant effect of VENC on anatomical information of brain MRV, with significant value below 0,001 (p value < 0,05). Mean rank on Friedman test showed that the best VENC to demonstrate intracranial veins was 25 cm/s.

Conclution: There was a significant effect of VENC on anatomical information of brain MRV with the VENC of 25 cm/s gave the best image of intracranial veins in general.


Keywords


Phase contrast, Magnetic Resonance Venography (MRV), velocity encoding (VENC)

Full Text:

PDF


DOI: https://doi.org/10.31983/jimed.v3i1.3183

Article Metrics

Abstract view : 508
Download PDF : 957

Refbacks

  • There are currently no refbacks.



Creative Commons License  Statcounter Global Stats - Browser, OS, Search Engine including Mobile  Usage Share          Dimensions AI | The most advanced scientific research database

             
JURNAL IMEJING DIAGNOSTIK by http://ejournal.poltekkes-smg.ac.id/ojs/index.php/jimed is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats