Enhancing Diagnostic Precision: Evaluation of Preprocessing Filters in Simple Diffusion Kurtosis Imaging for Head and Neck Tumors

Author:

Nakamitsu Yuki1,Kuroda Masahiro1ORCID,Shimizu Yudai2,Kuroda Kazuhiro13,Yoshimura Yuuki14,Yoshida Suzuka2,Nakamura Yoshihide2,Fukumura Yuka2,Kamizaki Ryo15,Al-Hammad Wlla E.26,Oita Masataka7,Tanabe Yoshinori1ORCID,Sugimoto Kohei17,Sugianto Irfan8ORCID,Barham Majd9ORCID,Tekiki Nouha2,Asaumi Junichi2

Affiliation:

1. Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan

2. Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan

3. Department of Health and Welfare Science, Graduate School of Health and Welfare Science, Okayama Prefectural University, Okayama 719-1197, Japan

4. Radiology Diagnosis, Okayama Saiseikai General Hospital, Okayama 700-8558, Japan

5. Department of Radiology, Matsuyama Red Cross Hospital, Matsuyama 790-8524, Japan

6. Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan

7. Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, Japan

8. Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University, Sulawesi 90245, Indonesia

9. Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University, Nablus 44839, Palestine

Abstract

Background: Our initial clinical study using simple diffusion kurtosis imaging (SDI), which simultaneously produces a diffusion kurtosis image (DKI) and an apparent diffusion coefficient map, confirmed the usefulness of SDI for tumor diagnosis. However, the obtained DKI had noticeable variability in the mean kurtosis (MK) values, which is inherent to SDI. We aimed to improve this variability in SDI by preprocessing with three different filters (Gaussian [G], median [M], and nonlocal mean) of the diffusion-weighted images used for SDI. Methods: The usefulness of filter parameters for diagnosis was examined in basic and clinical studies involving 13 patients with head and neck tumors. Results: The filter parameters, which did not change the median MK value, but reduced the variability and significantly homogenized the MK values in tumor and normal tissues in both basic and clinical studies, were identified. In the receiver operating characteristic curve analysis for distinguishing tumors from normal tissues using MK values, the area under curve values significantly improved from 0.627 without filters to 0.641 with G (σ = 0.5) and 0.638 with M (radius = 0.5). Conclusions: Thus, image pretreatment with G and M for SDI was shown to be useful for improving tumor diagnosis in clinical practice.

Funder

Grants-in-Aid for Scientific Research from the Ministry of Health, Labor, and Welfare of Japan

Publisher

MDPI AG

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