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ArticleRadiological physics and technology2025

A simplified method for generating maximum slope maps in ultrafast dynamic contrast-enhanced breast magnetic resonance imaging.

Ayumu Funaki, Masaki Ohkubo, Kazunori Ohashi, Toshiro Shukuya, Yuka Yashima, Kazunori Kubota

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Article in Radiological physics and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Ayumu FunakiDepartment of Radiological Technology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 323-0845, Japan. afunaki@dokkyomed.ac.jp.ORCID http://orcid.org/0009-0001-8645-5692
Masaki OhkuboGraduate School of Health Sciences, Niigata University, Niigata, Japan.
Kazunori OhashiDepartment of Radiological Technology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 323-0845, Japan.
Toshiro ShukuyaDepartment of Radiological Technology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 323-0845, Japan.
Yuka YashimaDepartment of Radiology, Dokkyo Medical University Saitama Medical Center, Koshigaya, Saitama, Japan.
Kazunori KubotaDepartment of Radiology, Dokkyo Medical University Saitama Medical Center, Koshigaya, Saitama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical measurement of the maximum slope (MS) using ultrafast dynamic contrast-enhanced (UF-DCE) breast magnetic resonance imaging (MRI) is typically performed by placing a region of interest (ROI) in the most enhanced area within a lesion. However, previous studies have not clarified whether visually identified enhanced areas consistently exhibit the highest MS values. These ROI-based MS measurements require MS maps to ensure appropriate ROI placement. However, generating MS maps requires specialized software capable of pixel-by-pixel MS calculations, which are available only at a few facilities. Therefore, this study proposed a simplified method for generating MS maps. This method involves subtracting consecutive UF-DCE images, applying temporal maximum intensity projection, normalizing the resulting image by dividing it by the pre-contrast image signal intensity, and converting it to a slope by dividing it by the temporal resolution. The MS maps generated using the proposed method were compared with those obtained using a robust pixel-by-pixel curve-fitting method, in addition to the final-phase UF-DCE images. In all cases with breast lesions (n = 13), the signal intensity distributions on the proposed MS maps closely resembled those on the curve-fitting maps, with a significantly higher similarity than those on the final-phase UF-DCE images (p < 0.001). The derived mean absolute error of MS values after regression-based modification was 0.78 ± 0.72 (%/s). The proposed method improves the reliability of ROI placement in conventional ROI-based MS measurements and supports the direct quantification of MS values from map pixel data.

Indexed as

BreastBreast NeoplasmsContrast MediaImage Processing, Computer-AssistedMagnetic Resonance ImagingAdultFemaleHumansMiddle AgedTime FactorsContrast MediaBreast cancerDynamic contrast-enhanced MRIMagnetic resonance imagingMaximum slopeUltrafast

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.