Detailed characterization of vascular anatomy, in particular the quantification of changes in the distribution of vessel sizes and of vascular pruning, is essential for the diagnosis and management of a variety of pulmonary vascular diseases and for the care of cancer survivors who have received radiation to the thorax. Clinical estimates of vessel radii are typically based on setting a pixel intensity threshold and counting how many “On” pixels are present across the vessel cross-section. A more objective approach introduced recently involves fitting the image with a library of spherical Gaussian filters and utilizing the size of the best matching filter as the estimate of vessel diameter. However, both these approaches have significant accuracy limitations including mis-match between a Gaussian intensity distribution and that of real vessels. Here we introduce and demonstrate a novel approach for accurate vessel sizing using 3D appearance models of a tubular structure along a curvilinear trajectory in 3D space. The vessel branch trajectories are represented with cubic Hermite splines and the tubular branch surfaces represented as a finite element surface mesh. An iterative parameter adjustment scheme is employed to optimally match the appearance models to a patient’s chest X-ray computed tomography (CT) scan to generate estimates for branch radii and trajectories with subpixel resolution. The method is demonstrated on pulmonary vasculature in an adult human CT scan, and on 2D simulated test cases.
Each year in the U.S., 7.4 million surgical procedures involving the major vessels are performed. Many of our patients require multiple surgeries, and many of the procedures include “surgical exploration”. Procedures of this kind come with a significant amount of risk, carrying up to a 17.4% predicted mortality rate. This is especially concerning for our target population of pediatric patients with congenital abnormalities of the heart and major pulmonary vessels. This paper offers a novel approach to surgical planning which includes studying virtual and physical models of pulmonary vasculature of an individual patient before operation obtained from conventional 3D X-ray computed tomography (CT) scans of the chest. These models would provide clinicians with a non-invasive, intricately detailed representation of patient anatomy, and could reduce the need for invasive planning procedures such as exploratory surgery. Researchers involved in the AirPROM project have already demonstrated the utility of virtual and physical models in treatment planning of the airways of the chest. Clinicians have acknowledged the potential benefit from such a technology. A method for creating patient-derived physical models is demonstrated on pulmonary vasculature extracted from a CT scan with contrast of an adult human. Using a modified version of the NIH ImageJ program, a series of image processing functions are used to extract and mathematically reconstruct the vasculature tree structures of interest. An auto-generated STL file is sent to a 3D printer to create a physical model of the major pulmonary vasculature generated from 3D CT scans of patients.
Purpose: Detailed characterization of pulmonary vascular anatomy has important applications for the diagnosis and management of a variety of vascular diseases. Prior efforts have emphasized using vessel segmentation to gather information on the number or branches, number of bifurcations, and branch length and volume, but accurate traversal of the vessel tree to identify and repair erroneous interconnections between adjacent branches and neighboring tree structures has not been carefully considered. In this study, we endeavor to develop and implement a successful approach to distinguishing and characterizing individual vascular trees from among a complex intermingling of trees. Methods: We developed strategies and parameters in which the algorithm identifies and repairs false branch inter-tree and intra-tree connections to traverse complicated vessel trees. A series of two-dimensional (2D) virtual datasets with a variety of interconnections were constructed for development, testing, and validation. To demonstrate the approach, a series of real 3D computed tomography (CT) lung datasets were obtained, including that of an anthropomorphic chest phantom; an adult human chest CT; a pediatric patient chest CT; and a micro-CT of an excised rat lung preparation. Results: Our method was correct in all 2D virtual test datasets. For each real 3D CT dataset, the resulting simulated vessel tree structures faithfully depicted the vessel tree structures that were originally extracted from the corresponding lung CT scans. Conclusion: We have developed a comprehensive strategy for traversing and labeling interconnected vascular trees and successfully implemented its application to pulmonary vessels observed using 3D CT images of the chest.
The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided
detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the
number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many
algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on
tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or
major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely
affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been
developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm
comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling,
and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and
bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large
masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections
appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm
was developed and trained to on the first 100 datasets of the LIDC image database.
The development of computer-aided diagnosis (CAD) methods for the processing of CT lung scans continues to become
increasingly popular due to the potential of these algorithms to reduce image reading time, errors caused by user fatigue,
and user subjectivity when screening for the presence of malignant lesions. This study seeks to address the critical need
for a realistic simulated lung nodule CT image dataset based on real tumor morphologies that can be used for the
quantitative evaluation and comparison of these CAD algorithms. The manual contouring of 17 different lung
metastases was performed and reconstruction of the full 3-D surface of each tumor was achieved through the utilization
of an analytical equation comprised of a spherical harmonics series. 2-D nodule slice representations were then
computed based on these analytical equations to produce realistic simulated nodules that can be inserted into CT datasets
with well-circumscribed, vascularized, or juxtapleural borders and also be scaled to represent nodule growth. The 3-D
shape and intensity profile of each simulated nodule created from the spherical harmonics reconstruction was compared
to the real patient CT lung metastasis from which its contour points were derived through the calculation of a 3-D
correlation coefficient, producing an average value of 0.8897 (±0.0609). This database of realistic simulated nodules can
fulfill the need for a reproducible and reliable gold standard for CAD algorithms with regards to nodule detection and
sizing, especially given its virtually unlimited capacity for expansion to other nodule shape variants, organ systems, and
This research addresses the problem of determining the location of a pulmonary nodule in a radiograph with the aid of a
pre-existing computed tomographic (CT) scan. The nodule is segmented in the radiograph using a level set segmentation
method that incorporates characteristics of the nodule in a digitally reconstructed radiograph (DRR) that is calculated
from the CT scan. The segmentation method includes two new level set energy terms. The contrast energy seeks to
increase the contrast of the segmented region relative to its surroundings. The gradient direction convergence energy is
minimized when the intensity gradient direction in the region converges to a point. The segmentation method was tested
on 23 pulmonary nodules from 20 cases for which both a radiographic image and CT scan were collected. The mean
nodule effective diameter is 22.5 mm. The smallest nodule has an effective diameter of 12.0 mm and the largest an
effective diameter of 48.1 mm. Nodule position uncertainty was simulated by randomly offsetting the true nodule center
from an aim point. The segmented region is initialized to a circle centered at the aim point with a radius that is equal to
the effective radius of the nodule plus a 10.0 mm margin. When the segmented region that is produced by the proposed
method is used to localize the nodule, the average reduction in nodule-position uncertainty is 46%. The relevance of this
method to the detection of radiotherapy targets at the time of treatment is discussed.
The ability of a clinician to properly detect changes in the size of lung nodules over time is a vital element to both the
diagnosis of malignant growths and the monitoring of the response of cancerous lesions to therapy. We have developed
a novel metastasis sizing algorithm based on 3-D template matching with spherical tumor appearance models that were
created to match the expected geometry of the tumors of interest while accounting for potential spatial offsets of nodules
in the slice thickness direction. The spherical template that best-fits the overall volume of each lung metastasis was
determined through the optimization of the 3-D normalized cross-correlation coefficients (NCCC) calculated between
the templates and the nodules. A total of 17 different lung metastases were extracted manually from real patient CT
datasets and reconstructed in 3-D using spherical harmonics equations to generate simulated nodules for testing our
algorithm. Each metastasis 3-D shape was then subjected to 10%, 25%, 50%, 75% and 90% scaling of its volume to allow for 5 possible volume change combinations relative to the original size per each reconstructed nodule and inserted back into CT datasets with appropriate blurring and noise addition. When plotted against the true volume change, the nodule volume changes calculated by our algorithm for these 85 data points exhibited a high degree of accuracy (slope = 0.9817, R2 = 0.9957). Our results demonstrate that the 3-D template matching method can be an effective, fast, and accurate tool for automated sizing of metastatic tumors.
Purpose: To implement a new non-invasive in-vivo assay to compute the dose-response relationship following radiation-induced
injury to normal lung tissue, using computed tomography (CT) scans of the chest.
Methods and Materials: Follow-up volumetric CT scans were acquired in patients with metastatic tumors to the lung
treated using stereotactic radiation therapy. The images reveal a focal region of fibrosis corresponding to the high-dose
region and no observable long-term damage in distant sites. For each pixel in the follow-up image the treatment dose
and the change in apparent tissue density was compiled. For each of 12 pre-selected dose levels the average pixel tissue
density change was computed and fit to a two-parameter dose-response model. The sensitivity of the resulting fits to
registration error was also quantified.
Results: Complete in vivo dose-response relationships in human normal lung tissue were computed. Increasing radiation
sensitivity was found with larger treatment volume. Radiation sensitivity increased also over time up to 12 months, but
decreased at later time points. The time-course of dose response correlated with the time-course of levels of circulating
IL-1&agr;, TGF&bgr; and MCP-1. The method was found to be robust to registration errors up to 3 mm.
Conclusions: This approach for the first time enables the quantification of the full range dose response relationship in
human subjects. The method may be used to assess quantitatively the efficacy of various agents thought to illicit
radiation protection to the lung.
Precise needle placement is vital for the success of a wide variety of percutaneous surgical procedures. Insertions into soft tissues can be difficult to learn and to perform, due to tissue deformation, needle deflection and limited visual feedback. Little quantitative information is known about the interaction between needles and soft tissues during puncture. We are carrying out a "smart needling" project in which a fairly long, but slender biopsy needle will be controlled to hit the target that is inside human body, automatically and precisely. This paper reports the preliminary work which is to prove that translational oscillation of the needle can reduce target movement, and at the same time to find the optimal settings of the important factors that will produce the least target movement. The experiment platform comprises of an oscillatory needle restricted to translate horizontally. A position-trackable catheter was embedded in the phantom to act as the target. Two-Level factorial design was adopted and an exploratory data analysis (EDA) approach was used for analysis. The final results showed that oscillation at high frequency band from 2kHz to 20kHz can reduce target movement. Translation speed, oscillation frequency and amplitude are all important factors. But phantoms with different elasticities may have different best settings of these factors. For example, for soft phantoms, lower frequency, higher speed and smaller amplitude are desired for minimal target movement. Optimization searching engine will be designed correspondingly to control the needle in optimal working conditions that can produce minimal target movement.
In this paper, we demonstrate a technique for modeling liver motion during the respiratory cycle using intensity-based free-form deformation registration of gated MR images. We acquired 3D MR image sets (multislice 2D) of the abdomen of four volunteers at end-inhalation, end-exhalation, and eight time points in between using respiratory gating. We computed the deformation field between the images using intensity-based rigid and non-rigid registration algorithms. The non-rigid transformation is a free-form deformation with B-spline interpolation between uniformly-spaced control points. The transformations between inhalation and exhalation were visually inspected. Much of the liver motion is cranial-caudal translation, and thus the rigid transformation captures much of the motion. However, there is still substantial residual deformation of up to 2 cm. The free-form deformation produces a motion field that appears on visual inspection to be accurate. This is true for the liver surface, internal liver structures such as the vascular tree, and the external skin surface. We conclude that abdominal organ motion due to respiration can be satisfactorily modeled using an intensity-based non-rigid 4D image registration approach. This allows for an easier and potentially more accurate and patient-specific deformation field computation than physics-based models using assumed tissue properties and acting forces.