1,721,268 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Improved Acquisition Methods for Hyperpolarized Carbon-13 Magnetic Resonance Imaging
Get PDFMagnetic resonance imaging with hyperpolarized 13C-labeled compounds via dynamic nuclear polarization (DNP) has been used to non-invasively study metabolic processes in vivo. This method provides a transient signal enhancement of more than 10,000 fold compared to imaging 13C compounds at thermal equilibrium. However, as soon as the pre-polarized 13C-labeled compound leaves the polarizer, its hyperpolarized state would irreversibly decay to the thermal equilibrium with a decay constant characterized by T1, which is typically less than one minute. The rapid loss of nonrenewable polarization brings challenges in hyperpolarized 13C magnetic resonance imaging. This dissertation presents improved acquisition methods for hyperpolarized 13C imaging with the injection of hyperpolarized [1-13C]pyruvate, which is the most widely studied substrate to date. The improved acquisition methods include a regional bolus tracking sequence for automatic acquisition timing, real-time calibration of frequency and RF power for more robust acquisitions, metabolite specific balanced steady state free precession (bSSFP) sequence and metabolite specific fast spin echo sequence for efficient use of polarization in hyperpolarized [1-13C] imaging. The proposed acquisition methods have been demonstrated in various clinical applications on a MR 3T scanner. Bolus tracking and real-time acquisition methods have been used in imaging human brain, heart, kidney and prostate. Metabolite specific bSSFP sequence has been applied in imaging human kidney. Metabolite specific fast spin echo sequence has been demonstrated in imaging human brain
Improving Pyruvate Kinetic Measurements Using Variable Flip Angle Schemes in bSSFP Hyperpolarized Carbon-13 MR Imaging
Get PDFHyperpolarized MR imaging of 13C-labeled probes provide increased sensitivity to investigate metabolic kinetics. The characterization of [1-13C]pyruvate kinetics has been correlated to increased aggressiveness of Renal Cell Carcinoma (RCC) and pyruvate kinetics have the potential to be a valuable tool for assessing disease burden. Increased signal-to-noise ratio (SNR) has already been proven through the use of balanced steady-state free precession (bSSFP) sequences over accelerated gradient echo (GRE) acquisitions. A sigmoid-based variable flip angle scheme was implemented in a pyruvate-specific 3D bSSFP dynamic imaging protocol to further optimize the SNR across multiple acquisitions. The effects of varying flip angle across a dynamic imaging study was first simulated with a physics-based 2-site pyruvate-lactate kinetic model and then evaluated using Monte Carlo simulations for sensitivities to kinetic and imaging parameters. An increase in early lactate signal as well as a delayed pyruvate signal peak and extended signal window were confirmed with subsequent animal studies on a healthy adult rat. The resulting signal changes present the possibility of improved spatial resolution and improved kPL fitting. Additional studies and simulations to optimize associated sequence parameters are needed to further characterize the use-case for sigmoid-based variable flip angle schemes
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Tissue Segmentation and Classification for PET/MR MR-based Attenuation Correction Using Zero-echo Time (ZTE) MRI
Get PDFTo reduce errors in the reconstructed PET image, the photon attenuation of all tissues needs to be accounted for. Current sequence-based methods to generate an MR-derived attenuation map are unable to account for all tissue classes. Recent work has demonstrated that there is phase contrast in ultrashort echo time (UTE) or zero echo time (ZTE) images that would allow classification of all necessary tissue classes (bone, air, fat, and water) from only a single ZTE scan. The aim of this thesis is to demonstrate the feasibility of generating a pseudo-CT attenuation map based on bone, air, fat, and water classifications from a single ZTE acquisition. A 3D image of the pelvis was acquired using a ZTE pulse sequence on a 3T GE Signa PET/MRI system. Semi-automated algorithms were used to segment bone, air, and soft tissue from the ZTE magnitude image. Air was segmented using an intensity limited region growing algorithm and global thresholding. Bone was segmented by enhancing bone, and then using global thresholding. Soft tissue was defined as regions where bone and air were absent. A continuous-value fat/water map was then generated with fuzzy c-means clustering using the ZTE phase image and the soft tissue mask. Appropriate HU values were assigned to the segmented tissue maps, and combined to produce the pseudo-CT attenuation map. Qualitative comparisons with CT, and Dixon pseudo-CT images presented similar tissue classification results. Preliminary results demonstrate that bone, air, fat, and water can be classified using a single ZTE acquisition
3D balanced SSFP UTE MRI for multiple contrasts whole brain imaging
No full textPurpose: This study aimed to develop a new high-resolution MRI sequence for the imaging of the ultra-short transverse relaxation time (uT2) components in the brain, while simultaneously providing proton density (PD) contrast for reference and quantification. Theory: The sequence combines low flip angle balanced SSFP (bSSFP) and UTE techniques, together with a 3D dual-echo rosette k-space trajectory for readout. Methods: The expected image contrast was evaluated by simulations. A study cohort of six healthy volunteers and eight multiple sclerosis (MS) patients was recruited to test the proposed sequence. Subtraction between two TEs was performed to extract uT2 signals. In addition, conventional longitudinal relaxation time (T1) weighted, T2-weighted, and PD-weighted MRI sequences were also acquired for comparison. Results: Typical PD-contrast was found in the second TE images, while uT2 signals were selectively captured in the first TE images. The subtraction images presented signals primarily originating from uT2 components, but only if the first TE is short enough. Lesions in the MS subjects showed hyperintense signals in the second TE images but were hypointense signals in the subtraction images. The lesions had significantly lower signal intensity in subtraction images than normal white matter (WM), which indicated a reduction of uT2 components likely associated with myelin. Conclusion: 3D isotropic sub-millimeter (0.94 mm) spatial resolution images were acquired with the novel bSSFP UTE sequence within 3 min. It provided easy extraction of uT2 signals and PD-contrast for reference within a single acquisition
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Development of a Novel 2D RF Pulse Sequence to Achieve An Improved Localization of Hyperpolarized 13C Imaging
Get PDFHyperpolarization of metabolically active compounds labeled with 13C has been recently utilized for imaging metabolic processes in vivo. This work focused on developing a new 2D radiofrequency (RF) pulse for tracking a 13C-labeled pyruvate bolus injection in vivo. This pulse was designed for a clinical scanner, which allows for rapid translation to human studies. To meet clinical needs, we examined the flexibility of our design, and specifically the ability to shift the pulse location. The off-resonance response due to inhomogeneity, [1-13C]alanine and [1-13C]lactate have also been examined. Experimental results have validated the profile of our designed 2D RF pulse and off-resonance signals. To prepare for in vivo studies, our proposed pulse has been incorporated into a hyperpolarized 13C imaging sequence
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Deep Generative and Hardware Accelerated MRI
Get PDFMagnetic Resonance Imaging (MRI) offers exceptional diagnostic capabilities but faces limitations in acquisition speed and reconstruction complexity. While deep learning methods show promise for accelerating MRI, their clinical translation is hindered by limited diverse raw training data and high computational demands.This dissertation addresses these challenges through two complementary approaches. First, we develop generative frameworks that synthesize realistic complex-valued MRI data from widely available magnitude-only images (typical clinically). We demonstrate that reconstruction networks trained on synthetically generated data perform comparably to those trained on real acquisitions. By implementing latent space exploration and an RF coil sensitivity library, we enable the creation of diverse, realistic multi-coil k-space data from magnitude images, bridging the gap between limited research datasets and vast clinical repositories.Second, we investigate hardware and algorithmic efficiency improvements. We implement deep learning reconstruction models on custom RISC-V architectures with specialized accelerators, exploring quantization and parallelism strategies. Additionally, we develop mixed-precision implementations of iterative reconstruction methods, particularly leveraging 8-bit floating-point formats to reduce memory requirements and computational complexity while preserving image quality.The contributions presented collectively address barriers to clinical translation of accelerated deep learning based MRI by enabling the use of existing clinical archives for training data generation and reducing computational requirements for deployment
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Comparing the Training Performance of a Deep Neural Network for Accelerated MRI Reconstruction Using Synthesized and Realistic k-Space Data
Get PDFMagnetic Resonance Imaging (MRI) is a powerful medical imaging modality used as a diagnostic tool. There is a steady rise in the imagining examination. Trends from 2000 - 2016 showed that nearly 16 million to 21 million patients had enrolled annually in various US health care systems. The number of MRIs per 1000 increased from 62 per 1000 to 139 per 1000 patients from 2000 to 2016. MR images are usually stored in Picture Archiving and Communication Systems (PACS) in Digital Imaging and Communication in Medicine (DICOM). DICOM format includes a header and imaging data. MRI k-space is the raw data obtained during the MR signal acquisition. The file size of complex MR data is huge. It is generally transformed into the anatomical imaging data, and raw data is discarded and not transferred to the PACS. The abundant DICOM data has the potential to be used for training neural networks. Deep Neural Network models depend on the extensive training datasets. DICOM images are magnitude images without the image phase. It is essential to understand the effect of missing image phase information to use the DICOM data for this training task effectively. My thesis attempts to compare a deep neural network's performance for accelerated MRI reconstruction using the k-space to DICOM only data. MR imaging offers a great deal of control to the user to acquire the data and reconstruct the clinical images. All this comes at the cost of an increase in the acquisition time. Typical scan times are between 30 to 40 mins. Scan times go up to 60 mins if a contrast agent needs to be administered. Such long acquisition times are not only expensive but a cause of inconvenience to the subject as it is impossible to stay motionless in the bore during the whole duration. Two areas are of interest to reduce the scan time, (i) accelerated acquisition and (ii) fast and efficient reconstruction. Methods like compressed sensing and parallel imaging are used to accelerate MRI acquisition. Compressed sensing achieves scan acceleration by overcoming the requirement of Nyquist sampling criteria. An undersampling pattern like the Poisson Disk undersampling pattern is used to acquire an incoherent random sparse signal instead of the full k-space. The "sigpy.mri" python library's "Poisson" API was used to simulate this undersampling. This Python API generates a variable-density Poisson-disc sampling pattern. Compressed Sensing theory mentions that image reconstruction would be possible using signals less than the number indicated by Nyquist as long as the k-space undersampling is done incoherently, which does not lead to structural aliasing when the anatomical image is constructed. This algorithm combines the undersampling with partial Fourier imaging. This API uses a fully sampled calibration region at the center of the k-space in addition to the acceleration factor. The acceleration factor is used for undersampling the region outside the fully sampled center region. Poisson disk undersampling does random sampling while constraining the maximum and minimum distance. This scheme leads to incoherent sampling and avoids structural artifacts. After the image acquisition comes, the reconstruction of the fully sampled k-space or the anatomical image with good SNR. A deep-learning neural network was trained to perform the reconstruction of the retrospectively undersampled data. The undersampled raw k-space data's training performance is compared with that of the undersampled k-space data obtained from the DICOM data. Our experiments have shown that the magnitude obtained from raw k-space data has consistently shown better initial training performance and faster convergence when compared to the magnitude image obtained from the DICOM image. It is also observed that after training enough epochs, the performance of the model trained using raw data is comparable to that of the DICOM images. The significance of this finding is in the fact that the abundantly available DICOM data can be used to train a deep neural network to perform reconstruction of the undersampled k-space. FastMRI is a research project from Facebook AI(FAIR) and NYU Langone Health. The dataset for this project is publicly available. This dataset has two types of scans, knee MRI and brain MRI. For this work, we have used single coil knee MRI data. For performing the training, 2D slices from these images are used from the training dataset's single-coil knee MRI volumes. The training dataset has 973 volumes and a total of 34,742 slices
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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