Analysis of axial T2 TSE images using deep learning reconstruction in MRI of brain tumors
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Mar 25, 2025
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Abstract
Magnetic Resonance Imaging (MRI) Brain examinations often encounter uncooperative patients, necessitating rapid scanning techniques that yield optimal results. To address this challenge, advanced technologies such as deep learning can be leveraged to accelerate scan time, reduce noise, and enhance image precision. This study aims to evaluate the disparity in MRI Brain image quality with and without deep learning in tumor cases to achieve superior diagnostic imaging. Employing a quantitative experimental approach, this research analyzed a sample of 30 patients collected from January to February 2025. Three Radiologist Specialists assessed the images using a questionnaire based on the Visual Grading Analysis (VGA) method. The obtained responses were statistically examined through Cohen’s Kappa consistency test and Wilcoxon Signed-Rank Test. Findings revealed a statistically significant difference in image information between deep learning-assisted and conventional MRI scans. In T2 TSE sequences, deep learning reconstruction demonstrated superior anatomical visualization of the Gray Matter, White Matter, Lateral Ventricles, Basal Ganglia, and Parafalx Cerebri. However, in brain tumor pathology visualization, conventional MRI exhibited sharper and more distinct tumor delineation. Although deep learning-enhanced T2 TSE sequences reduced scan duration and improved overall image quality, they provided suboptimal diagnostic information in tumor cases.
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How to Cite
Muzdalifah, N. P., Utami, H. S., Hidayat, F. R., Wibowo, K. M., Jadmika, M. R. and Samudra, A. (2025) “Analysis of axial T2 TSE images using deep learning reconstruction in MRI of brain tumors”, Science Midwifery, 13(1), pp. 52-59. doi: 10.35335/midwifery.v13i1.1867.
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Inaoka, T., Wada, A., Sugeta, M., Sonoda, M., Nakazawa, H., Sakai, R., Tomobe, H., Nakagawa, K., Aoki, S., & Terada, H. (2024). Enhancement of Image Quality in Low-Field Knee MR Imaging Using Deep learning. Cureus. https://doi.org/10.7759/cureus.71277
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Kaniewska, M., Deininger-Czermak, E., Getzmann, J. M., Wang, X., Lohezic, M., & Guggenberger, R. (2022). Application of Deep learning–based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time. European Radiology, 33(3), 1513–1525. https://doi.org/10.1007/s00330-022-09151-1
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Lee, J., Jung, M., Park, J., Kim, S., Im, Y., Lee, N., Song, H.-T., & Lee, Y. H. (2023). Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)–based reconstruction: Prospective, multi-reader, multi-vendor study. Scientific Reports, 13(1), 17264. https://doi.org/10.1038/s41598-023-44248-7
Nugroho, P. A., Fenriana, I., Arijanto, R., & Kom, M. (2020). Implementasi Deep Learning Menggunakan Convolutional Neural Network ( Cnn ) Pada Ekspresi Manusia. 2(1).
Pardosi, I. A., & Lubis, A. A. (2019). Analisis Kualitas Citra Hasil Reduksi Noise Menggunakan Spatial Median Filter dan Adaptive Fuzzy Filter Terhadap Variasi Kedalaman Citra. Indonesian Journal of Information Systems, 1(2), 78–89. https://doi.org/10.24002/ijis.v1i2.1939
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Primartha, R. (2018). Belajar machine learning teori dan praktik. INFORMATIKA.
Putri, M. N., Katili, I., Hariri, A., Budiarti, T. A., & Wibowo, G. M. (2021). Perbandingan Pegukuran Volume Tumor Brain MRI Menggunakan Teknik Manual Dan Metode Active Contour. Jurnal Imejing Diagnostik (JImeD), 7(2), 94–97. https://doi.org/10.31983/jimed.v7i2.7474
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Sartoretti, E., Wyss, M., Eichenberger, B., Van Smoorenburg, L., Binkert, C. A., Sartoretti-Schefer, S., & Sartoretti, T. (2021). Rapid T2-weighted turbo spin echo MultiVane Brain MRI using compressed SENSE: A qualitative analysis. Clinical Radiology, 76(10), 786.e15-786.e22. https://doi.org/10.1016/j.crad.2021.06.017
Sriyatun, S., Sari, G., Maulana Budianto, R., Heriyanto, H., Pramono, P., & Darmiyeti, D. (2020). PEMERIKSAAN MRI PELVIS WANITA DENGAN KLINIS ENDOMETRIOSIS. JRI (Jurnal Radiografer Indonesia), 3(2), 59–62. https://doi.org/10.55451/jri.v3i2.69
Suh, P. S., Park, J. E., Roh, Y. H., Kim, S., Jung, M., Koo, Y. S., Lee, S.-A., Choi, Y., & Kim, H. S. (2024). Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy. Korean Journal of Radiology, 25(4), 374. https://doi.org/10.3348/kjr.2023.0842
Susanto, F., Santoso, A. G., & Abimanyu, B. (2016). Analisis Pembobotan T2 Turbo Spin Echo (TSE) Brain MRI Potongan Axial dengan Penggunaan Sensitivity Encoding (SENSE) dan Tanpa Penggunaan Sense: Evaluasi pada Signal to Noise Ratio (SNR) dan Scan Time. Jurnal Imejing Diagnostik (JImeD), 2(2), 148–153. https://doi.org/10.31983/jimed.v2i2.3174
Suta, I. B. L. M., Hartati, R. S., & Divayana, Y. (2019). Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging). Majalah Ilmiah Teknologi Elektro, 18(2). https://doi.org/10.24843/MITE.2019.v18i02.P01
Westbrook, Catherine. 2014. Handbook of MRI Technique, Fourth Edition, Blackwell Science Ltd., United Kingdom
Xie, Y., Tao, H., Li, X., Hu, Y., Liu, C., Zhou, B., Cai, J., Nickel, D., Fu, C., Xiong, B., & Chen, S. (2024). Prospective Comparison of Standard and Deep learning–reconstructed Turbo Spin-Echo MRI of the Shoulder. Radiology, 310(1), e231405. https://doi.org/10.1148/radiol.231405