Haris Saybasili

Bio: Haris Saybasili is an academic researcher from Siemens. The author has contributed to research in topics: Real-time MRI & Iterative reconstruction. The author has an hindex of 9, co-authored 19 publications receiving 327 citations. Previous affiliations of Haris Saybasili include Case Western Reserve University & Boğaziçi University.

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

Abstract: Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

Quantitative high-resolution renal perfusion imaging using 3-dimensional through-time radial generalized autocalibrating partially parallel acquisition.

Abstract: ObjectivesDynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) examinations of the kidneys provide quantitative information on renal perfusion and filtration. However, these examinations are often difficult to implement because of respiratory motion and their need for a high spatiotempor

Interventional MRI using Multiple 3D Angiography Roadmaps with Real-time Imaging

Abstract: Purpose: To enhance real-time magnetic resonance (MR)-guided catheter navigation by overlaying colorized multiphase MR angiography (MRA) and cholangiopancreatography (MRCP) roadmaps in an anatomic context. Materials and Methods: Time-resolved MRA and respiratory-gated MRCP were acquired prior to real-time imaging in a pig model. MRA and MRCP data were loaded into a custom real-time MRI reconstruction and visualization workstation where they were displayed as maximum intensity projections (MIPs) in distinct colors. The MIPs were rendered in 3D together with real-time multislice imaging data using alpha blending. Interactive rotation allowed different views of the combined data. Results: Fused display of the previously acquired MIP angiography data with real-time imaging added anatomical context during endovascular interventions in swine. The use of multiple MIPs rendered in different colors facilitated differentiation of vascular structures, improving visual feedback during device navigation. Conclusion: Interventional real-time MRI may be enhanced by combining with previously acquired multiphase angiograms. Rendered as 3D MIPs together with 2D slice data, this technique provided useful anatomical context that enhanced MRI-guided interventional applications. J. Magn. Reson. Imaging 2010;31:1015–1019. ©2010 Wiley-Liss, Inc.

Visualization of active devices and automatic slice repositioning (“SnapTo”) for MRI‐guided interventions

Abstract: The accurate visualization of interventional devices is crucial for the safety and effectiveness of MRI-guided interventional procedures. In this paper, we introduce an improvement to the visualization of active devices. The key component is a fast, robust method (“CurveFind”) that reconstructs the three-dimensional trajectory of the device from projection images in a fraction of a second. CurveFind is an iterative prediction-correction algorithm that acts on a product of orthogonal projection images. By varying step size and search direction, it is robust to signal inhomogeneities. At the touch of a key, the imaged slice is repositioned to contain the relevant section of the device (“SnapTo”), the curve of the device is plotted in a three-dimensional display, and the point on a target slice, which the device will intersect, is displayed. These features have been incorporated into a real-time MRI system. Experiments in vitro and in vivo (in a pig) have produced successful results using a variety of single- and multichannel devices designed to produce both spatially continuous and discrete signals. CurveFind is typically able to reconstruct the device curve, with an average error of approximately 2 mm, even in the case of complex geometries. Magn Reson Med 63:1070–1079, 2010. © 2010 Wiley-Liss, Inc.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Magnetic Resonance Imaging Physical Principles And Sequence Design

Abstract: Thank you for downloading magnetic resonance imaging physical principles and sequence design. As you may know, people have look numerous times for their chosen books like this magnetic resonance imaging physical principles and sequence design, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop.

Gadgetron: an open source framework for medical image reconstruction.

Abstract: This work presents a new open source framework for medical image reconstruction called the “Gadgetron.” The framework implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or “Gadgets” from raw data to reconstructed images. The data processing pipeline is configured dynamically at run-time based on an extensible markup language configuration description. The framework promotes reuse and sharing of reconstruction modules and new Gadgets can be added to the Gadgetron framework through a plugin-like architecture without recompiling the basic framework infrastructure. Gadgets are typically implemented in C/C++, but the framework includes wrapper Gadgets that allow the user to implement new modules in the Python scripting language for rapid prototyping. In addition to the streaming framework infrastructure, the Gadgetron comes with a set of dedicated toolboxes in shared libraries for medical image reconstruction. This includes generic toolboxes for data-parallel (e.g., GPU-based) execution of compute-intensive components. The basic framework architecture is independent of medical imaging modality, but this article focuses on its application to Cartesian and non-Cartesian parallel magnetic resonance imaging. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

Abstract: Demonstrate a novel fast method for reconstruction of multi-dimensional MR fingerprinting (MRF) data using deep learning methods.A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed with the extended phase graph formalism. The NN reconstruction accuracy for noiseless and noisy data is compared to conventional MRF template matching as a function of training data size and is quantified in simulated numerical brain phantom data and International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom data measured on 1.5T and 3T scanners with an optimized MRF EPI and MRF fast imaging with steady state precession (FISP) sequences with spiral readout. The utility of the method is demonstrated in a healthy subject in vivo at 1.5T.Network training required 10 to 74 minutes; once trained, data reconstruction required approximately 10 ms for the MRF EPI and 76 ms for the MRF FISP sequence. Reconstruction of simulated, noiseless brain data using the NN resulted in a RMS error (RMSE) of 2.6 ms for T1 and 1.9 ms for T2 . The reconstruction error in the presence of noise was less than 10% for both T1 and T2 for SNR greater than 25 dB. Phantom measurements yielded good agreement (R2 = 0.99/0.99 for MRF EPI T1 /T2 and 0.94/0.98 for MRF FISP T1 /T2 ) between the T1 and T2 estimated by the NN and reference values from the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom.Reconstruction of MRF data with a NN is accurate, 300- to 5000-fold faster, and more robust to noise and dictionary undersampling than conventional MRF dictionary-matching.

Nanoparticles for cell labeling

Abstract: Cell based therapeutics are emerging as powerful regimens. To better understand the migration and proliferation mechanisms of implanted cells, a means to track cells in living subjects is essential, and to achieve that, a number of cell labeling techniques have been developed. Nanoparticles, with their superior physical properties, have become the materials of choice in many investigations along this line. Owing to inherent magnetic, optical or acoustic attributes, these nanoparticles can be detected by corresponding imaging modalities in living subjects at a high spatial and temporal resolution. These features allow implanted cells to be separated from host cells; and have advantages over traditional histological methods, as they permit non-invasive, real-time tracking in vivo. This review attempts to give a summary of progress in using nanotechnology to monitor cell trafficking. We will focus on direct cell labeling techniques, in which cells ingest nanoparticles that bear traceable signals, such as iron oxide or quantum dots. Ferritin and MagA reporter genes that can package endogenous iron or iron supplement into iron oxide nanoparticles will also be discussed.