Publications by authors named "Lionel T Cheng"

4 Publications

  • Page 1 of 1

Computer-aided detection of intracranial aneurysms in MR angiography.

J Digit Imaging 2011 Feb 24;24(1):86-95. Epub 2009 Nov 24.

Mayo Clinic, Medical Imaging Informatics Innovation Center, Rochester, MN 55905, USA.

Intracranial aneurysms represent a significant cause of morbidity and mortality. While the risk factors for aneurysm formation are known, the detection of aneurysms remains challenging. Magnetic resonance angiography (MRA) has recently emerged as a useful non-invasive method for aneurysm detection. However, even for experienced neuroradiologists, the sensitivity to small (<5 mm) aneurysms in MRA images is poor, on the order of 30~60% in recent, large series. We describe a fully automated computer-aided detection (CAD) scheme for detecting aneurysms on 3D time-of-flight (TOF) MRA images. The scheme locates points of interest (POIs) on individual MRA datasets by combining two complementary techniques. The first technique segments the intracranial arteries automatically and finds POIs from the segmented vessels. The second technique identifies POIs directly from the raw, unsegmented image dataset. This latter technique is useful in cases of incomplete segmentation. Following a series of feature calculations, a small fraction of POIs are retained as candidate aneurysms from the collected POIs according to predetermined rules. The CAD scheme was evaluated on 287 datasets containing 147 aneurysms that were verified with digital subtraction angiography, the accepted standard of reference for aneurysm detection. For two different operating points, the CAD scheme achieved a sensitivity of 80% (71% for aneurysms less than 5 mm) with three mean false positives per case, and 95% (91% for aneurysms less than 5 mm) with nine mean false positives per case. In conclusion, the CAD scheme showed good accuracy and may have application in improving the sensitivity of aneurysm detection on MR images.
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http://dx.doi.org/10.1007/s10278-009-9254-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3046787PMC
February 2011

Cell accelerated cryoablation simulation.

Comput Methods Programs Biomed 2010 Jun 24;98(3):241-52. Epub 2009 Oct 24.

Department of Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.

Tumor cryoablation is a clinical procedure where supercooled probes are used to destroy cancerous lesions. Cryoablation is a safe and effective palliative treatment for skeletal metastases, providing immediate and long term pain relief, increasing mobility and improving quality of life. Ideally, lesions are encompassed by an ice ball and frozen to a sufficiently low temperature to ensure cell death. "Lethal ice" is the term used to describe regions within the ice ball where cell death occurs. Failure to achieve lethal ice in all portions of a lesion may explain the high recurrence rate currently observed. Tracking growth of lethal ice is critical to success of percutaneous ablations, however, no practical methods currently exist for non-invasive temperature monitoring. Physicians lack planning tools which provide accurate estimation of the ice formation. Simulation of ice formation, while possible, is computationally demanding and too time consuming to be of clinical utility. We developed the computational framework for the simulation, acceleration strategies for multicore Intel x86 and IBM Cell architectures, and performed preliminary validation of the simulation. Our results demonstrate that the streaming SIMD implementation has better performance and scalability. Both accelerated and non-accelerated algorithms demonstrate good agreement between simulation and manually identified ice ball boundaries in phantom and patient images. Our results show promise for the development of novel cryoablation planning tools with real-time monitoring capability for clinical use.
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http://dx.doi.org/10.1016/j.cmpb.2009.09.004DOI Listing
June 2010

Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing.

J Digit Imaging 2010 Apr 30;23(2):119-32. Epub 2009 May 30.

Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000--2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases.
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http://dx.doi.org/10.1007/s10278-009-9215-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837158PMC
April 2010

Teleradiology in Singapore--taking stock and looking ahead.

Ann Acad Med Singap 2006 Aug;35(8):552-6

Department of Diagnostic Radiology, Singapore General Hospital, Singapore.

Teleradiology will have a significant impact on the delivery of healthcare and the practice of medicine. In order to ensure a positive outcome, the expected benefits, limitations and potential pitfalls of teleradiology must be carefully considered. For Singapore, teleradiology can be used to facilitate a quantum leap in the standards of radiological services. This can be achieved through the development of an integrated, nationwide, high-speed radiology network which will allow patients to have access to high-quality and responsive subspecialty radiology expertise located throughout the country. If judiciously implemented, teleradiology has the potential to propel Singapore radiology to an unprecedented level of professional quality and service delivery, and will provide the framework for sustainable radiological insourcing from other countries.
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August 2006