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Four scanners were used to acquire an MRI of a nonanatomic phantom as part of the TCIA RIDER database. In fast spin-echo inversion recovery (IR) sequences, several inversion durations were employed, including 50, 100, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 ms. In addition, a 3D fast spoiled gradient recalled echo (FSPGR) sequence was used to investigate several flip angles (FA): 2, 5, 10, 15, 20, 25, and 30 degrees. Nineteen phantom compartments were manually segmented. Different approaches were used to pre-process each image: Bin discretization, Wavelet filter, Laplacian of Gaussian, logarithm, square, square root, and gradient. Overall, 92 first-, second-, and higher-order statistical radiomic features were extracted. ComBat harmonization was also applied to the extracted radiomic features. Finally, the Intraclass Correlation Coefficient (ICC) and Kruskal-Wallis\u2019s (KW) tests were implemented to assess the robustness of radiomic features. The number of non-significant features in the KW test ranged between 0\u20135 and 29\u201374 for various scanners, 31\u201391 and 37\u201392 for three times tests, 0\u201333 to 34\u201390 for FAs, and 3\u201368 to 65\u201389 for IRs before and after ComBat harmonization, with different image pre-processing techniques, respectively. The number of features with ICC over 90% ranged between 0\u20138 and 6\u201360 for various scanners, 11\u201375 and 17\u201380 for three times tests, 3\u201383 to 9\u201384 for FAs, and 3\u201349 to 3\u201363 for IRs before and after ComBat harmonization, with different image pre-processing techniques, respectively. The use of various scanners, IRs, and FAs has a great impact on radiomic features. However, the majority of scanner-robust features is also robust to IR and FA. Among the effective parameters in MR images, several tests in one scanner have a negligible impact on radiomic features. Different scanners and acquisition parameters using various image pre-processing might affect radiomic features to a large extent. ComBat harmonization might significantly impact the reproducibility of MRI radiomic features.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s11517-024-03071-6","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T03:03:46Z","timestamp":1711508626000},"page":"2319-2332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study"],"prefix":"10.1007","volume":"62","author":[{"given":"Ghasem","family":"Hajianfar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyyed Ali","family":"Hosseini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Bagherieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehrdad","family":"Oveisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isaac","family":"Shiri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7559-5297","authenticated-orcid":false,"given":"Habib","family":"Zaidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"issue":"4","key":"3071_CR1","doi-asserted-by":"publisher","first-page":"2272","DOI":"10.1007\/s00330-020-07284-9","volume":"31","author":"F Orlhac","year":"2021","unstructured":"Orlhac F, Lecler A, Savatovski J, Goya-Outi J, Nioche C, Charbonneau F et al (2021) How can we combat multicenter variability in MR radiomics? 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