Reslice3Dto2D: Introduction of a software tool to reformat 3D volumes into reference 2D slices in cardiovascular magnetic resonance imaging

BMC Research Notes Viezzer et al. BMC Research Notes (2024) 17:270 https://doi.org/10.1186/s13104-024-06931-4 Open Access R E S E A R C H N OT E Reslice3Dto2D: Introduction of a software tool to reformat 3D volumes into reference 2D slices in cardiovascular magnetic resonance imaging Darian Viezzer1,2,3*, Maximilian Fenski1,2,4, Thomas Hiroshi Grandy1,2,4, Johanna Kuhnt1,2, Thomas Hadler1,2,3, Steffen Lange5 and Jeanette Schulz-Menger1,2,3,4 Abstract Objective Cardiovascular magnetic resonance enables the quantification of functional and morphological parameters with an impact on therapeutical decision making. While quantitative assessment is established in 2D, novel 3D techniques lack a standardized approach. Multi-planar-reformatting functionality in available software relies on visual matching location and often lacks necessary functionalities for further post-processing. Therefore, the easy-to-use Reslice3Dto2D software tool was developed as part of another research project to fill this gap and is now introduced with this work. Results The Reslice3Dto2D reformats 3D data at the exact location of a reference slice with a two-step-based interpolation in order to reflect in-plane discretization and through-plane slice thickness including a slice profile selection. The tool was successfully validated on an artificial dataset and tested on 119 subjects with different underlying pathologies. The exported reformatted data could be imported into three different post-processing software tools. The quantified image sharpness by the Frequency Domain Image Blur Measure was significantly decreased by around 40% on rectangular slice profiles with 7 mm slice thickness compared to 0 mm due to partial volume effects. Consequently, Reslice3Dto2D enables the quantification of 3D data with conventional postprocessing tools as well as the comparison of 3D acquisitions with their established 2D version. Keywords 3D, 2D, Cardiovascular Magnetic Resonance, CMR, Reference slice position, Post-processing, Quantification *Correspondence: Darian Viezzer 1 Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Lindenberger Weg 80, 13125 Berlin, Germany 2 Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité – Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany 3 DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany 4 Department of Cardiology and Nephrology, Helios Hospital Berlin-Buch, Berlin, Germany 5 Faculty for Computer Sciences, Hochschule Darmstadt (University of Applied Sciences), Darmstadt, Germany © The Author(s) 2024. 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BMC Research Notes (2024) 17:270 Introduction In current guidelines cardiovascular magnetic resonance (CMR) is a recommended imaging modality for the characterization of cardiovascular diseases [1–3]. The quantification of functional and morphological parameters is highly relevant in clinical decision making [1, 2]. Due to technical restrictions, mainly two dimensional (2D) acquisition methods were available in the past [4] while novel technical developments enable three dimensional (3D) acquisitions for several sequence types like CINE [4], angiography [5], flow [6], Late Gadolinium Enhancement (LGE) [5, 7, 8], perfusion imaging [9], parametric mapping [10] and fat/water imaging [8]. The advantages of 3D acquisitions compared to 2D include the omission of a complex slice positioning during examination [4], the possibility to cover of the whole heart [4], an improved diagnosis of cardiovascular diseases with complex anatomic arrangement [3], a decreased impact of partial volume effects [7] and the potential of simultaneous sequence acquisition [8, 10]. Nonetheless, conventional and established 2D sequences provide the gold standard in most clinical settings [7] and represent the quantitative and qualitative validation for novel 3D sequences [4, 7]. As the post-processing of 3D data is neither standardized nor routinely available, a reformatting is currently necessary to enable usage of conventional 2D post-processing tools. Analysis software products offer multi-planar-reformatting (MPR) functionalities that rely on manual or basic oriented (axial, sagittal or coronal) 2D plane definitions [4, 11]. Some of these tools lack DICOM [12] export or processing functionalities and thus limits the post-processing capabilities. Furthermore, a difference in the actual scanner produced slice profile and an ideal rectangular shaped slice profile has been reported [13]. The inclusion of slice profile and respective slice thickness enables to reproduce more accurate scanner behavior. While opensource solutions, like 3D slicer [11], offer desired functionalities either intrinsic, by extension modules or by the possibility of self-developing extensions, the necessary expertise for the usage represents an obstacle. Hence, this work aims to introduce our self-developed and easy-to-use Reslice3Dto2D software as an intermediate processing tool for the clinical research of novel 3D acquisition methods. The tool was developed with a focused use-case on CMR and enables the reformatting of 3D data to the exact location of reference 2D acquisitions including slice thickness adjustments, slice profile options and a DICOM [12] export functionality. Methods As this work introduces software, the implementation of the Reslice3Dto2D tool is described first, followed by a description of its validation and testing. Page 2 of 8 Implementation The Reslice3Dto2D tool was fully implemented in Python (Version 3.8, Python Software Foundation) and includes a graphical user interface (GUI). The source-code is provided as supplemental material S1 and made publicly available [14]. Installation details are provided in the README.md of the source-code and user guidance is described in detail in the user manual of the supplemental material S2. The user manual lists also DICOM [12] tags (...truncated)