Super resolution using sparse sampling at portable ultra-low field MR

Ultra-low field (ULF) magnetic resonance imaging (MRI) holds the potential to make MRI more accessible, given its cost-effectiveness, reduced power requirements, and portability. However, signal-to-noise ratio (SNR) drops with field strength, necessitating imaging with lower resolution and longer scan times. This study introduces a novel Fourier-based Super Resolution (FouSR) approach, designed to enhance the resolution of ULF MRI images with minimal increase in total scan time. FouSR combines spatial frequencies from two orthogonal ULF images of anisotropic resolution to create an isotropic T2-weighted fluid-attenuated inversion recovery (FLAIR) image. We hypothesized that FouSR could effectively recover information from under-sampled slice directions, thereby improving the delineation of multiple sclerosis (MS) lesions and other significant anatomical features. Importantly, the FouSR algorithm can be implemented on the scanner with changes to the k-space trajectory. Paired ULF (Hyperfine SWOOP, 0.064 tesla) and high field (Siemens, Skyra, 3 Tesla) FLAIR scans were collected on the same day from a phantom and a cohort of 10 participants with MS or suspected MS (6 female; mean ± SD age: 44.1 ± 4.1). ULF scans were acquired along both coronal and axial planes, featuring an in-plane resolution of 1.7 mm × 1.7 mm with a slice thickness of 5 mm. FouSR was evaluated against registered ULF coronal and axial scans, their average (ULF average) and a gold standard SR (ANTs SR). FouSR exhibited higher SNR (47.96 ± 12.6) compared to ULF coronal (36.7 ± 12.2) and higher lesion conspicuity (0.12 ± 0.06) compared to ULF axial (0.13 ± 0.07) but did not exhibit any significant differences contrast-to-noise-ratio (CNR) compared to other methods in patient scans. However, FouSR demonstrated superior image sharpness (0.025 ± 0.0040) compared to all other techniques (ULF coronal 0.021 ± 0.0037, q = 5.9, p-adj. = 0.011; ULF axial 0.018 ± 0.0026, q = 11.1, p-adj. = 0.0001; ULF average 0.019 ± 0.0034, q = 24.2, p-adj. < 0.0001) and higher lesion sharpness (−0.97 ± 0.31) when compared to the ULF average (−1.02 ± 0.37, t(543) = −10.174, p = <0.0001). Average blinded qualitative assessment by three experienced MS neurologists showed no significant difference in WML and sulci or gyri visualization between FouSR and other methods. FouSR can, in principle, be implemented on the scanner to produce clinically useful FLAIR images at higher resolution on the fly, providing a valuable tool for visualizing lesions and other anatomical structures in MS

Doctor of Philosophy

dissertationEach year in the United States, a quarter million cases of stroke are caused directly by atherosclerotic disease of the cervical carotid artery. This represents a significant portion of health care costs that could be avoided if high-risk carotid artery lesions could be detected early on in disease progression. There is mounting evidence that Magnetic Resonance Imaging of the carotid artery can better classify subjects who would benefit from interventions. Turbo Spin Echo sequences are a class of Magnetic Resonance Imaging sequences that provide a variety of tissue contrasts. While high resolution Turbo Spin Echo images have demonstrated important details of carotid artery morphology, it is evident that pulsatile blood and wall motion related to the cardiac cycle are still significant sources of image degradation. In addition, patient motion artifacts due to the relatively long scan times of Turbo Spin Echo sequences result in an unacceptable fraction of noninterpretable studies. This dissertation presents work done to detect and correct for types of voluntary and physiological patient motion

Fast and radiation-free high-resolution MR cranial bone imaging for pediatric patients

AbstractEach year, 2.2 million pediatric head computed tomography (CT) scans are performed in the United States. Head trauma and craniosynostosis are two of the most common pediatric conditions requiring head CT scans. Head trauma is common in children and one-third of the patients that present to the emergency room undergoes head CT imaging. Craniosynostosis is a congenital disability defined by a prematurely fused cranial suture. Standard clinical care for pediatric patients with head trauma or craniosynostosis uses high-resolution head CT to identify cranial fractures or cranial sutures. Unfortunately, the ionizing radiation of CT imaging imposes a risk to patients, particularly pediatric patients who are vulnerable to radiation. Moreover, multiple CT scans are often performed during follow-up, exacerbating their cumulative risk. The National Cancer Institute reported that radiation exposure from multiple head CT scans will triple the risk of leukemia and brain cancer. Many medical centers have recently removed CT from the postoperative care of craniosynostosis, limiting postoperative evaluation and highlighting the urgent need for radiation-free imaging. Several “Black bone” magnetic resonance imaging (MRI) methods have been introduced as radiation-free alternatives. Despite the initially encouraging results, these methods have not translated into clinical practice due to several challenges, including 1) subjective manual image processing; 2) long acquisition time. Due to poor signal contrast between bone and its surrounding tissues in MR images, existing post-processing methods rely on extensive manual MR segmentation which is subjective, prone to noise and artifacts, hard to reproduce, and time-consuming. As a result, they do not meet the need for clinical diagnosis and have not been employed clinically. A CT scan takes tens of seconds; however, a high-resolution MR scan takes minutes, which may be challenging for pediatric subject compliance and limit clinical adoption. The overall objective of this study is to develop rapid and radiation-free 3D high-resolution MRI methods to provide CT-equivalent information in diagnosing cranial fractures and cranial suture patency for pediatric patients. Two specific aims are proposed to achieve the overall objective. Aim 1: Develop a fully automated deep learning method to synthesize high-resolution pseudo-CT (pCT) of pediatric cranial bone from MR images. Aim 2: Develop a deep learning image reconstruction method to reduce MR acquisition time. Aim 1 is to address the issues of subjective manual image processing. In this aim, we developed a robust and fully automated deep learning method to create pCT images from MRI, which facilitates translating MR cranial bone imaging into clinical practice for pediatric patients. Two 3D patch-based ResUNets were trained using paired MR and CT patches randomly selected from the whole head (NetWH) or in the vicinity of bone, fractures/sutures, or air (NetBA) to synthesize pCT. A third ResUNet was trained to generate a binary brain mask using only MRI. The pCT images from NetWH (pCTNetWH) in the brain area and NetBA (pCTNetBA) in the non-brain area were combined to generate pCTCom. A manual processing method using inverted MR images (iMR) was also employed for comparison. pCTCom had significantly smaller mean absolute errors (MAE) than pCTNetWH and pCTNetBA in the whole head. Dice Similarity Coefficient (DSC) of the segmented bone was significantly higher in pCTCom than in pCTNetWH, pCTNetBA, and iMR. DSC from pCTCom demonstrated significantly reduced age dependence than iMR. Furthermore, pCTCom provided excellent suture and fracture visibility comparable to CT. A fast MR acquisition is highly desirable to translate novel MR cranial to clinical practice in place of CT. However, fast MR acquisition usually results in under-sampled data below the Nyquist rate, leading to artifacts and high noise. Recently, numerous deep learning MR reconstruction methods have been employed to mitigate artifacts and minimize noise. Despite many successes, existing deep learning methods have not accounted for MR k-space sampling density variations. In aim 2, we developed a self-supervised and physics-guided deep learning method by weighting k-space sampling Density in network training Loss (wkDeLo). The proposed method uses an unrolled network with a data consistency (DC) and a regularization (R). A forward Fourier model was used to transform the reconstructed image into k-space. The data consistency between the transformed k-space and the acquired k-space data is enforced in the DC layer. This unrolled network is regularized by k-space deep-learning prior using a convolution neural network. In total, 400 radial spokes were acquired with an acquisition time of 5 minutes. Two disjoint k-space data sets, including the first 1 minute (80 radial spokes) and the remaining 4 minutes (320 radial spokes), were used as the network training input and target. A unique feature of our proposed method is to use a L1 loss weighted by k-space sampling density in an end-to-end training of the unrolled network. Moreover, we also reconstructed images using the same unrolled network structure but without accounting for the k-space sampling density variations in the loss for comparison. In other words, a uniform weighted k-space is used in the training loss (un-wkDeLo). Furthermore, we implemented a well-accepted deep learning reconstruction method, Self-Supervision via Data Undersampling (SSDU) as a baseline method reference. Using the images reconstructed from a 5-min scan as the gold standard, we computed the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) for reconstructed images from 1-min k-space data using SSDU, un-wkDeLo, and wkDeLo. The SSIM and PSNR of the wkDeLo images are significantly higher than both SSDU and un-wkDeLo. Moreover, the wkDeLo reconstructed images have the highest sharpness and the least artifacts and noise. In aim 2, we have demonstrated that high quality MR images at a spatial resolution of 0.6x0.6x0.8 mm3 could be achieved using only 1 min acquisition time. Finally, we evaluated the clinical utility of the proposed MR cranial bone imaging in identifying cranial fractures and cranial suture patency. Clinicians by consensus evaluated the MR-derived pCT images. Acceptable image quality was achieved in greater than 90% of all MR scans; diagnoses were 100% accurate in the subset of patients with acceptable image quality. We have demonstrated that the proposed 3D high-resolution MR cranial bone method provided CT-equivalent images for pediatric patients with head trauma or craniosynostosis. This work will have a profound impact on pediatric health by providing clinicians with a rapid diagnostic tool without radiation safety concerns

Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Nouvelles stratégies d'acquisitions non cartésiennes pour l'IRM cardiovasculaire du petit animal

Cardiovascular imaging using NMR is still a real challenge. The difficulty relies on the need toacquire images with high temporal and spatial resolutions, in a limited acquisition time and in somecases of moving areas. While most images are acquired with cartesian trajectories, the use of 3D radialtrajectories was explored as an alternative. Indeed, they benefit from various advantages like their lowsensitivity to flow and motion artefacts as well as the opportunity to highly undersample acquisitions.Thus, the aim of this thesis was to develop new acquisition strategies using radial trajectory propertiesfor 3D cardiovascular anatomical and functional imaging in small animals at high magnetic fields.First, a method for measuring blood flow in 3D was developped, based on a time-of-flight effect.The use of radial trajectories allowed to highly reduce acquisition times while increasing spatial andtemporal resolutions compared to cartesian acquisitions.Then, combining the injection of iron nanoparticles which have a long vascular remanence withultrashort echot time radial acquisitions, we showed that anatomical cardiac images with a high spatialresolution could be obtained prospectively or restrospectively by adding a navigator echo in thesequence in order to synchronize the reconstruction to the cardiac cycle.Finally, this method was used to perform 4D flow imaging on the entire cardiopulmonary systemof the animals.The sequences developed during this work and the results obtained in anatomical and functionalimaging show the interest and the robustness of non cartesian methods in preclinical imaging. Theypaves the way to the development of new strategies in clinical imaging.Keywords : Preclinical MRI, 3D+t, radial trajectories, cardiovascular, flow measurement.L’imagerie cardiovasculaire par RMN est encore aujourd’hui un véritable défi. La difficulté résidedans la nécessité d’acquérir des images avec de fortes résolutions spatiale et temporelle en un tempslimité, et dans certains cas sur des zones en mouvement. Alors que la plupart des images sont acquisesavec des trajectoires cartésiennes, notre choix s’est porté sur l’utilisation de trajectoires 3D radialescomme alternative. En effet, celles-ci bénéficient de nombreux avantages comme leur faible sensibilitéaux artefacts de mouvements et de flux ainsi que la possibilité de fortement sous-échantillonner lesacquisitions. Ainsi, l’objectif de cette thèse a été de développer de nouvelles méthodes utilisant lespropriétés des acquisitions radiales pour l’imagerie cardiovasculaire 3D anatomique et fonctionnellechez le petit animal à hauts champs magnétiques.Tout d’abord, une méthode de mesure des flux sanguins en 3D a été mise au point, basée sur lephénomène de temps-de-vol. L’utilisation de trajectoires radiales a permis de réduire fortement lestemps d’acquisition tout en améliorant les résolutions spatiale et temporelle des images par rapportaux méthodes cartésiennes.Ensuite, en combinant l’utilisation de nanoparticules de Fer qui possèdent une rémanence vasculaireimportante avec des séquences radiales à temps d’écho ultracourt, nous avons montré que l’acquisitiond’images anatomiques cardiaques et vasculaires très haute résolution pouvait être réalisée de manièreprospective ou bien retrospective grâce à l’ajout d’un écho-navigateur dans la séquence permettantl’auto-synchronisation cardiaque.Enfin, cette même méthode a été employée pour réaliser l’imagerie de flux 4D sur l’entièreté dusystème cardio-pulmonaire de l’animal.Les séquences développées lors de ce travail et les résultats obtenus en imagerie anatomique etfonctionnelle montrent l’intérêt et la robustesse des méthodes non cartésiennes en imagerie préclinique.Elles peuvent ouvrir la voie à de nouvelles stratégies en imagerie clinique