Multiple Sclerosis (MS) is a common neurological disease affecting the central nervous system characterized by
pathologic changes including demyelination and axonal injury. MR imaging has become the most important tool to
evaluate the disease progression of MS which is characterized by the occurrence of white matter lesions. Currently,
radiologists evaluate and assess the multiple sclerosis lesions manually by estimating the lesion volume and amount of
lesions. This process is extremely time-consuming and sensitive to intra- and inter-observer variability. Therefore, there
is a need for automatic segmentation of the MS lesions followed by lesion quantification. We have developed a fully
automatic segmentation algorithm to identify the MS lesions. The segmentation algorithm is accelerated by parallel
computing using Graphics Processing Units (GPU) for practical implementation into a clinical environment.
Subsequently, characterized quantification of the lesions is performed. The quantification results, which include lesion
volume and amount of lesions, are stored in a structured report together with the lesion location in the brain to establish a
standardized representation of the disease progression of the patient. The development of this structured report in
collaboration with radiologists aims to facilitate outcome analysis and treatment assessment of the disease and will be
standardized based on DICOM-SR. The results can be distributed to other DICOM-compliant clinical systems that support DICOM-SR such as PACS. In addition, the implementation of a fully automatic segmentation and quantification system together with a method for storing, distributing, and visualizing key imaging and informatics data in DICOM-SR for MS lesions improves the clinical workflow of radiologists and visualizations of the lesion segmentations and will provide 3-D insight into the distribution of lesions in the brain.
Bone Age Assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate
the stage of skeletal maturation based on the left hand x-ray radiograph. The current BAA standard in the US is using the
Greulich & Pyle (G&P) Hand Atlas, which was developed fifty years ago and was only based on Caucasian population
from the Midwest US. To bring the BAA procedure up-to-date with today's population, a Digital Hand Atlas (DHA)
consisting of 1400 hand images of normal children of different ethnicities, age, and gender. Based on the DHA and to
solve inter- and intra-observer reading discrepancies, an automatic computer-aided bone age assessment system has been
developed and tested in clinical environments. The algorithm utilizes features extracted from three regions of interests:
phalanges, carpal, and radius. The features are aggregated into a fuzzy logic system, which outputs the calculated bone
age. The previous BAA system only uses features from phalanges and carpal, thus BAA result for children over age of
15 is less accurate. In this project, the new radius features are incorporated into the overall BAA system. The bone age
results, calculated from the new fuzzy logic system, are compared against radiologists' readings based on G&P atlas, and
exhibits an improvement in reading accuracy for older children.
Multiple sclerosis (MS) is a demyelinating disease of the central nervous system that affects approximately
2.5 million people worldwide. Magnetic resonance imaging (MRI) is an established tool for the assessment
of disease activity, progression and response to treatment. The progression of the disease is variable and
requires routine follow-up imaging studies. Currently, MRI quantification of multiple sclerosis requires a
manual approach to lesion measurement and yields an estimate of lesion volume and interval change. In the
setting of several prior studies and a long treatment history, trends related to treatment change quickly
become difficult to extrapolate. Our efforts seek to develop an imaging informatics based MS lesion
computer aided detection (CAD) package to quantify and track MS lesions including lesion load, volume,
and location. Together, with select clinical parameters, this data will be incorporated into an MS specific e-
Folder to provide decision support to evaluate and assess treatment options for MS in a manner tailored
specifically to an individual based on trends in MS presentation and progression.
Bone age assessment is a radiological procedure to evaluate a child's bone age based on his or her left-hand x-ray image.
The current standard is to match patient's hand with Greulich & Pyle hand atlas, which is outdated by 50 years and only
uses subjects from one region and one ethnicity. To improve bone age assessment accuracy for today's children, an
automated race- and gender-specific bone age assessment (BAA) system has been developed in IPILab. 1390 normal
left-hand x-ray images have been collected at Children's Hospital of Los Angeles (CHLA) to form the digital hand atlas
(DHA). DHA includes both male and female children of ages one to eighteen and of four ethnic groups: African
American, Asian American, Caucasian, and Hispanic. In order to apply DHA and BAA CAD into a clinical
environment, a web-based BAA CAD system and graphical user interface (GUI) has been implemented in Women and
Children's Hospital at Los Angeles County (WCH-LAC). A CAD server has been integrated in WCH's PACS
environment, and a clinical validation workflow has been designed for radiologists, who compare CAD readings with
G&P readings and determine which reading is more suited for a certain case. Readings are logged in database and
analyzed to assess BAA CAD performance in a clinical setting. The result is a successful installation of web-based BAA
CAD system in a clinical setting.
KEYWORDS: Radiology, Picture Archiving and Communication System, Databases, Data mining, Image retrieval, Information science, Medicine, Magnetic resonance imaging, Classification systems, Speaker recognition
Radiology Information Systems (RIS) contain a wealth of information that can be used for research, education, and
practice management. However, the sheer amount of information available makes querying specific data difficult and
time consuming. Previous work has shown that a clinical RIS database and its RIS text reports can be extracted,
duplicated and indexed for searches while complying with HIPAA and IRB requirements. This project's intent is to
provide a software tool, the RadSearch Toolkit, to allow intelligent indexing and parsing of RIS reports for easy yet
powerful searches. In addition, the project aims to seamlessly query and retrieve associated images from the Picture
Archiving and Communication System (PACS) in situations where an integrated RIS/PACS is in place - even
subselecting individual series, such as in an MRI study. RadSearch's application of simple text parsing techniques to
index text-based radiology reports will allow the search engine to quickly return relevant results. This powerful
combination will be useful in both private practice and academic settings; administrators can easily obtain complex
practice management information such as referral patterns; researchers can conduct retrospective studies with specific,
multiple criteria; teaching institutions can quickly and effectively create thorough teaching files.
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