Despite its potential, 66% of the world's population lacks access to magnetic resonance imaging (MRI). The main factors contributing to the uneven distribution of this imaging modality worldwide are the elevated cost and intricate nature of MRI systems coupled with the high level of knowledge and expertise required for its operation and maintenance. To improve its worldwide accessibility, MRI technology and techniques must undergo modifications to deliver a more cost-effective system that is easier to site and use without compromising on the diagnostic quality of the images.
This thesis presents two deep learning methods, ArtifactID and GDCNet, developed for artifact detection and correction and tailored for their integration into accessible MRI systems. ArtifactID is targeted to resource-constrained settings where skilled personnel are scarce. It automates part of the quality assessment step, critical during image acquisition to ensure data quality and the success of downstream analysis or interpretation. This study utilized two types of T1-weighted neuroimaging datasets: publicly available and prospective. Combining the two, ArtifactID successfully identified wrap-around and rigid head motion in multi-field strength and multi-vendor data. We leveraged the public datasets for artifact simulation, model training, and testing. In contrast, prospective datasets were reserved for validation and testing and to assess the models’ performance in data representative of clinical and deployment settings. We trained individual convolutional neural networks for each artifact. The wrap-around models perform binary classification, while the multi-class motion classification model allows distinction between moderate and severe motion artifacts. Our models demonstrated strong agreement with ground truth labels and motion metrics and proved potential for generalization to various data distributions. Furthermore, Grad-CAM heatmaps allowed early identification of failure modes, artifact localization within the image, and fine-tuning the pre-processing steps.
GDCNet correction applies to imaging techniques highly susceptible to local B0 deviations and systems whose design entails high B0 inhomogeneity. The method estimates a geometric distortion map by non-linear registration to a reference image. The self-supervised model, consisting of a U-Net and a spatial transform function unit, learned the correction by optimizing the similarity between the distorted and the reference images. We initially developed the tool for distortion correction of echo-planar imaging functional MRI images at 3 T.
This method allows dynamic correction of the functional data as a distortion map is estimated for each temporal frame. For this model, we leveraged T1-weighted anatomical images as target images. We trained the model on publicly available datasets and tested it on in-distribution and out-of-distribution datasets consisting of other public datasets unseen during training and a prospectively acquired dataset. Comparing GDCNet to state-of-the-art EPI geometric distortion methods, our technique demonstrated statistically significant improvements in normalized mutual information between the corrected and reference images and 14 times faster processing times without requiring the acquisition of additional sequences for field map estimation.
We adapted the GDCNet method for distortion correction of low-bandwidth images acquired in a 47 mT permanent magnet system. These systems are characterized by large B0 spatial inhomogeneity and low signal sensitivity. In this case, the model used high-field images or images acquired with higher acquisition bandwidths as reference. The goal was to exploit the signal-to-noise ratio improvements that low bandwidth acquisition offers while limiting geometric distortion artifacts in the images. We investigated two versions of the model using different similarity loss functions. Both models were trained and tested on an in vitro dataset of image-quality phantoms. Additionally, we evaluated the models’ generalization ability to an in vivo dataset. The models successfully reduced distortions to levels comparable to those of the high bandwidth images in vitro and improved geometric accuracy in vivo. Furthermore, the method indicated robust performance on reference images with large levels of noise.
Incorporating the methods presented in this thesis into the software of a clinical MRI system will alleviate some of the barriers currently restricting the democratization of MR technology. First, automating the time-consuming process of artifact identification during image quality assessment will improve scan efficiency and augment expertise on-site by assisting non-skilled personnel. Second, efficient off-resonance correction during image reconstruction will ease the tight B0 homogeneity requirements of magnet design, allowing more compact and lightweight systems that are easier to refrigerate and site.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/x9f7-7569 |
| Date | January 2024 |
| Creators | Manso Jimeno, Marina |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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