Access to magnetic resonance imaging (MRI) scans on the same subjects, encompassing various contrasts and field strengths, is crucial for brain studies involving supervised image translation for predicting missing or unavailable MRI data. However, there is a scarcity of such datasets covering both low and high fields. To bridge this gap, we propose a semi-synthesized dataset including Paired Multi-Contrast magnetic resonance (MR) images in T1, T2, and PD contrasts at both 1.5T and 3T for the same subjects. We also present it in both 2- and 3-dimensional formats, making it compatible with a wide range of models. We evaluate our proposed dataset using evaluation metrics along with morphology-based methods, and showcase the performance of a U-Net based architecture in diferent applications using our dataset. Finally, we release our dataset to facilitate future research involving multi-contrast MR image translation.
imaging (MRI) provides unrivaled soft tissue contrast and is now the leading imaging modality for clinical research precise diagnostics, and vigilant treatment monitoring across diverse age groups [1]. The distinctive feature of
detailed 3-dimensional (3D) images, with a particular focus on capturing the intricacies of soft tissues, such as gray and white matters. This unique attribute positions
ities of the brain’s internal structure and function [2]. Magnetic resonance (MR) images are acquired across diverse biophysical contrasts (e.g., T1, T2, and PD) and at diferent magnetic field strengths (i.e., 0.2 to 7T), each capturing specific characteristics of the underlying anatomy [3, 4]. Consequently, higher field strengths, along with higher spatial resolution can reveal richer information and superior image quality of the brain tissue relative to images acquired at lower field strength and resolution.
Machine Learning for Cognitive and Mental Health Workshop subjects. nEvelop-O
0009-0001-7860-0992 (F. Bagheri)
CEUR
ceur-ws.org (i.e., cross-modality) and from low- to high-field MR images for the same contrast. Although, this technique can be applied using both supervised and unsupervised approaches, supervised learning has shown higher performance as it enables the generation of high-quality formance [5, 6]. However, the requirement for paired datasets imposes a significant challenge as there is almost no accessible dataset available that includes paired
same subjects and in multiple contrasts. For instance, the most widely used datasets in previous in the field of MRI include Alzheimer’s Disease Neuroimaging Initiative (ADNI)1 [7], Information eXtraction from Images (IXI)2, and datasets sourced from the Human Connectome Project (HCP)3, each of which has limitations. For example, in all mentioned datsets, only raw 3D MR images are presented, which necessitates intricate pre-processing steps including registration and brain extraction. Moreover, they include either MR images of paired subjects limited to a single contrast, or multiple contrasts but limited to one field strength.
includes unpaired 3D MRI scans in T1, T2, and PD for diferent subjects at 1.5T and 3T. We propose a semi-synthesized dataset, PMC, which includes Paired
The PMC dataset is pre-processed and ready to use for supervised and semi-supervised learning methods in tasks, such as cross-modality, high-field MR image pre- 2.1. Data Synthesis Pipeline diction, super-resolution, and multi-contrast MR image translation. This comprehensive dataset comprises MR To create a dataset consist of MR images in multiple conimages from 181 subjects, preciously crafted in both 2- trasts at both 1.5T and 3T for the same pseudo-subjects, dimensional (2D) and 3D to accommodate a diverse range a series of processing steps are undertaken as illustrated of models compatible with each of these formats. As Fig- in Figure 2. ure 1 represents, the dataset includes paired images in T1, T2, and PD contrasts at both 1.5T and 3T for each Using FSL subject generated from the IXI dataset. 1.5T . . . its augmentations) is distributed across diferent subsets.
All versions of our proposed dataset will be released through our GitHub repository4.
MRI-Dataset-at-1.5T-and-3T-for-Supervised-Image2ImageTranslation 5https://fsl.fmrib.ox.ac.uk/fsl/fslwiki to the diference in the angle and position of the head diferences in the relative signal intensities in gray and during acquisition of the data. Secondly, 1.5T MR images white matter and accordingly in the resulting output conare taken as reference and 3T MR images at each contrast trast [12]. Consequently, to investigate the quality of the are registered to their respective contrast at 1.5T using synthesized images and minimize the impact of contrast non-linear registration. For the registration steps, we uti- diferences during evaluation, we conduct morphologylize Advanced Normalization Tools (ANTs)6 software as based comparative analyses which have been proven to it is widely recognized as an advanced medical image reg- be reliable in the state-of-the-art studies in related fields istration and segmentation toolkit that efectively man- [13]. We extract the morphological patterns of images ages, interprets, and visualizes multidimensional data (using edge detection techniques) at both 1.5T and 3T for [10]. Also, it should be noted that all aforementioned each contrast as shown in Figure 3 to assess whether the processing steps are applied to the 3D MR images, result- patterns and morphology of the synthesized data at 3T ing in PMC dataset in 3D format. align with the reference data at 1.5T. Next, we evaluate
Moreover, to extend the data generalizability to net- the extracted patterns using MSE and structural index works solely employing 2D data and increase the number similarity measure (SSIM) [14] as reported in Table 2. of samples, 3D MR images are transformed to 2D. Specif- Also, to compare the synthesized images with references ically, we select slices that predominantly contain the within diferent spatial frequency ranges and accordingly brain (i.e., 10 slices per 3D MR image) while avoiding diferent levels of details, we perform 2D wavelet analysis slices with minimal or no brain content. Additionally, on the synthesized images and corresponding references to increase the size and generalizability of the dataset, to decompose them into four diferent frequency comdata augmentation techniques, including flipping, rota- ponents and select the three most high frequency ones tion (with an angle of ±5 degrees), noise addition (e.g., named as Subband 1, 2, and 3, respectively [15] as Figure Gaussian with random standard deviation in range of 4 illustrates. Table 3 displays the subband-wise compara[5,10] and salt-and-pepper with a probability uniformly tive results. sampled from the interval of [0.05,0.1]), and scaling (with a factor of 1.2) are applied. As a result, the data size for Image Extracted Pattern each contrast at each field strength increased to 6576. 2.2. Data quality assessment To assess the quality of the synthesized MR images at 3T compared to the reference images at 1.5T, we first employ evaluation metrics including mean squared error (MSE), peak signal-to-noise ratio (PSNR), Pearson correlation (CORR), and mutual information (MI) [11]. We compare the synthesized 3T images with corresponding reference images at 1.5T as there are no labels available at 3T for checking the synthesis quality. Thus, utilizing these metrics, we assess how close 3T images are synthesized compared to 1.5T ones in terms of contrast and overall structure as reported in Table 1.
quired at 1.5T and 3T even for the same contrast, there are
works for tasks such as cross-modality, super-resolution, and multi-contrast MR image translation [16, 13, 17, 18]. Thus, to further investigate the application of the proposed dataset, a U-Net based architecture, which was previously proposed in [17] and has shown high performance in the mentioned applications, is implemented in this paper for the following tasks:
2. 3T MR image prediction from the same contrast at 1.5T 3. 3T MR image prediction using 1.5T multi-contrast
MR images
dataset in developing models based on cross-dataset evaluation scenarios, we utilize the latest release of the Open Access Series of Imaging Studies (OASIS)7, known as OASIS3 dataset [19], which includes MR images at 1.5T and 3T in T2, for Task 2 (3T MR image prediction from the same contrast at 1.5T ). First, we train and test the model on the OASIS3 dataset. Then, to compare the efectiveness of using the PMC dataset, we use it to train the model and test the model on the OASIS3 dataset. The results for both approaches shown in Table 5, suggest that our dataset demonstrates acceptable performance. Specifically, the U-Net, demonstrates higher eficacy when trained on PMC for 1.5T T2 to 3T T2 MR image translation.
involving MR image translation, in particular, image gen- In this study, we introduced the PMC dataset, which coneration, diferent stages of model development, and pre- sists of paired MR images in multiple contrasts of T1, training models for small target dataset sizes. In the T2, and PD and at both 1.5T and 3T field strengths for following, we investigate the capability of our dataset in supervised methods for the aforementioned tasks. 7https://www.oasis-brains.org/#data the same subjects. The dataset is pre-processed and presented in 3D, 2D, and a split version of 2D, ensuring compatibility with a wide range of models and application in image translation tasks within MRI. Quality evaluation of the proposed dataset involved the use of MSE, PSNR, CORR, SSIM, and MI evaluation metrics, along with morphology-based methods. We also demonstrated the applicability of the data for supervised methods, particularly in cross-modality MR image translation, 3T MR image prediction from the same contrast at 1.5T, and 3T MR image prediction using 1.5T multi-contrast MR images. Moreover, we highlighted its extendability to cross-dataset evaluation scenarios. strength using multi-to-one translation, CMBES
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