Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.
Modern microscopy techniques allow imaging of circulating blood components under vascular flow conditions.
The resulting video sequences provide unique insights into the behavior of blood cells within the vasculature and
can be used as a method to monitor and quantitate the recruitment of inflammatory cells at sites of vascular
injury/ inflammation and potentially serve as a pharmacodynamic biomarker, helping screen new therapies and
individualize dose and combinations of drugs. However, manual analysis of these video sequences is intractable,
requiring hours per 400 second video clip. In this paper, we present an automated technique to analyze the
behavior and recruitment of human leukocytes in whole blood under physiological conditions of shear through
a simple multi-channel fluorescence microscope in real-time. This technique detects and tracks the recruitment
of leukocytes to a bioactive surface coated on a flow chamber. Rolling cells (cells which partially bind to the
bioactive matrix) are detected counted, and have their velocity measured and graphed. The challenges here
include: high cell density, appearance similarity, and low (1Hz) frame rate. Our approach performs frame
differencing based motion segmentation, track initialization and online tracking of individual leukocytes.
Accurate needle placement is a common need in the medical environment. While the use
of small diameter needles for clinical applications such as biopsy, anesthesia and
cholangiography is preferred over the use of larger diameter needles, precision placement
can often be challenging, particularly for needles with a bevel tip. This is due to
deflection of the needle shaft caused by asymmetry of the needle tip. Factors such as the
needle shaft material, bevel design, and properties of the tissue penetrated determine the
nature and extent to which a needle bends. In recent years, several models have been
developed to characterize the bending of the needle, which provides a method of
determining the trajectory of the needle through tissue. This paper explores the use of a
nonholonomic model to characterize needle bending while providing added capabilities
of path planning, obstacle avoidance, and path correction for lung biopsy procedures. We
used a ballistic gel media phantom and a robotic needle placement device to
experimentally assess the accuracy of simulated needle paths based on the nonholonomic
model. Two sets of experiments were conducted, one for a single bend profile of the
needle and the second set of tests for double bending of the needle. The tests provided an
average error between the simulated path and the actual path of 0.8 mm for the single
bend profile and 0.9 mm for the double bend profile tests over a 110 mm long insertion
distance. The maximum error was 7.4 mm and 6.9 mm for the single and double bend
profile tests respectively. The nonholonomic model is therefore shown to provide a
reasonable prediction of needle bending.
We are investigating the feasibility of a computer-aided detection (CAD) system to assist radiologists in diagnosing
coronary artery disease in ECG gated cardiac multi-detector CT scans having calcified plaque. Coronary artery stenosis
analysis is challenging if calcified plaque or the iodinated blood pool hides viable lumen. The research described herein
provides an improved presentation to the radiologist by removing obscuring calcified plaque and blood pool. The
algorithm derives a Gaussian estimate of the point spread function (PSF) of the scanner responsible for plaque blooming
by fitting measured CTA image profiles. An initial estimate of the extent of calcified plaque is obtained from the image
evidence using a simple threshold. The Gaussian PSF estimate is then convolved with the initial plaque estimate to
obtain an estimate of the extent of the blooming artifact and this plaque blooming image is subtracted from the CT image
to obtain an image largely free of obscuring plaque. In a separate step, the obscuring blood pool is suppressed using
morphological operations and adaptive region growing. After processing by our algorithm, we are able to project the
segmented plaque-free lumen to form synthetic angiograms free from obstruction. We can also analyze the coronary
arteries with vessel tracking and centerline extraction to produce cross sectional images for measuring lumen stenosis.
As an additional aid to radiologists, we also produce plots of calcified plaque and lumen cross-sectional area along
selected blood vessels. The method was validated using digital phantoms and actual patient data, including in one case, a
validation against the results of a catheter angiogram.