MRI protocols are instruction sheets that radiology technologists use in routine clinical practice for guidance (e.g., slice
position, acquisition parameters etc.). In Mayo Clinic Arizona (MCA), there are over 900 MR protocols (ranging across
neuro, body, cardiac, breast etc.) which makes maintaining and updating the protocol instructions a labor intensive effort.
The task is even more challenging given different vendors (Siemens, GE etc.). This is a universal problem faced by all
the hospitals and/or medical research institutions. To increase the efficiency of the MR practice, we designed and
implemented a web-based platform (eProtocol) to automate the management of MRI protocols. It is built upon a database
that automatically extracts protocol information from DICOM compliant images and provides a user-friendly interface to
the technologists to create, edit and update the protocols. Advanced operations such as protocol migrations from scanner
to scanner and capability to upload Multimedia content were also implemented. To the best of our knowledge, eProtocol
is the first MR protocol automated management tool used clinically. It is expected that this platform will significantly
improve the radiology operations efficiency including better image quality and exam consistency, fewer repeat
examinations and less acquisition errors. These protocols instructions will be readily available to the technologists during
scans. In addition, this web-based platform can be extended to other imaging modalities such as CT, Mammography, and
Interventional Radiology and different vendors for imaging protocol management.
KEYWORDS: Magnetic resonance imaging, Kidney, Sensors, 3D image processing, Image processing, Detection and tracking algorithms, 3D magnetic resonance imaging, Image segmentation, Blob detection, Tissues
The glomeruli of the kidney perform the key role of blood filtration and the number of glomeruli in a kidney is correlated with susceptibility to chronic kidney disease and chronic cardiovascular disease. This motivates the development of new technology using magnetic resonance imaging (MRI) to measure the number of glomeruli and nephrons in vivo. However, there is currently a lack of computationally efficient techniques to perform fast, reliable and accurate counts of glomeruli in MR images due to the issues inherent in MRI, such as acquisition noise, partial volume effects (the mixture of several tissue signals in a voxel) and bias field (spatial intensity inhomogeneity). Such challenges are particularly severe because the glomeruli are very small, (in our case, a MRI image is ~16 million voxels, each glomerulus is in the size of 8~20 voxels), and the number of glomeruli is very large. To address this, we have developed an efficient Hessian based Difference of Gaussians (HDoG) detector to identify the glomeruli on 3D rat MR images. The image is first smoothed via DoG followed by the Hessian process to pre-segment and delineate the boundary of the glomerulus candidates. This then provides a basis to extract regional features used in an unsupervised clustering algorithm, completing segmentation by removing the false identifications occurred in the pre-segmentation. The experimental results show that Hessian based DoG has the potential to automatically detect glomeruli,from MRI in 3D, enabling new measurements of renal microstructure and pathology in preclinical and clinical studies.
DICOM Index Tracker (DIT) is an integrated platform to harvest rich information available from Digital Imaging and Communications in Medicine (DICOM) to improve quality assurance in radiology practices. It is designed to capture and maintain longitudinal patient-specific exam indices of interests for all diagnostic and procedural uses of imaging modalities. Thus, it effectively serves as a quality assurance and patient safety monitoring tool. The foundation of DIT is an intelligent database system which stores the information accepted and parsed via a DICOM receiver and parser. The database system enables the basic dosimetry analysis. The success of DIT implementation at Mayo Clinic Arizona calls for the DIT deployment at the enterprise level which requires significant improvements. First, for geographically distributed multi-site implementation, the first bottleneck is the communication (network) delay; the second is the scalability of the DICOM parser to handle the large volume of exams from different sites. To address this issue, DICOM receiver and parser are separated and decentralized by site. To facilitate the enterprise wide Quality Assurance (QA), a notable challenge is the great diversities of manufacturers, modalities and software versions, as the solution DIT Enterprise provides the standardization tool for device naming, protocol naming, physician naming across sites. Thirdly, advanced analytic engines are implemented online which support the proactive QA in DIT Enterprise.
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