A.A Aris Diartama
Akademi Teknik Radiodiagnostik dan Radioterapi Bali

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Optimalisasi Kualitas Citra CT Scan Kepala Dengan Variasi Rekonstruksi Increment Pada Kasus Stroke Non Hemoragik Di Instalasi Radiologi RSAD TK II Udayana Denpasar Theresiani Dae; A.A Aris Diartama; Made Purwa Darmita
Compromise Journal : Community Proffesional Service Journal Vol. 1 No. 4 (2023): November : Compromise Journal : Community Proffesional Service Journal
Publisher : LPPM STIKES KESETIAKAWANAN SOSIAL INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57213/compromisejournal.v1i4.33

Abstract

Non-hemorrhagic stroke or commonly called ischemic stroke is a functional disorder of the body area due to decreased brain function caused by blockage of cerebral blood vessel flow. One of the modalities in radiology that can diagnose non-hemorrhagic stroke cases is computer tomography (CT). To get good image quality, one of them is by doing increment reconstruction. Increment reconstruction is the distance between reconstruction images in volume data. Increment reconstruction is grouped into three, namely increment overlapping, increment contiguous and increment gap. The type of research used is quantitative research with an experimental approach. The sample of this study used 10 patients who underwent CT scan examination of the head with non-hemorrhagic stroke cases in June-August 2023 in the Radiology Unit of RSAD TK II UDAYANA DENPASAR BALI. The findings of this study indicate that when using different increment reconstruction techniques, anatomical image information will vary. The results of the total Friedman test which shows a p value of 0.000 (<0.05) provide evidence of this. A p value of 0.000 was found (p value <0.05) based on differences in overall anatomical image information, indicating that there is a difference between non-hemorrhagic stroke cases and head CT scans. Of the four variations of increment reconstruction, variation 3 with a reconstruction increment of 2.5 mm has the largest mean rank value (3.23) so it is the most ideal anatomical image information.