Walter G. O'Dell

Modeling liver motion and deformation during the respiratory cycle using intensity‐based nonrigid registration of gated MR images

We present a technique for modeling liver motion during the respiratory cycle using intensity-based nonrigid registration of gated magnetic resonance (MR) images. Three-dimensional MR images of the abdomens of four volunteers were acquired at end-inspiration, end-expiration, and eight time points in between using respiratory gating. The deformation fields between the images were computed using intensity-based rigid and nonrigid registration algorithms. Global motion is modeled by a rigid transformation while local motion is modeled by a free-form deformation based on B-splines. Much of the liver motion was cranial-caudal translation, which was captured by the rigid transformation. However, there was still substantial residual deformation (approximately 10 mm averaged over the entire liver in four volunteers, and 34 mm at one place in the liver of one volunteer). The computed organ motion model can potentially be used to determine an appropriate respiratory-gated radiotherapy window during which the position of the target is known within a specified excursion.

Modeling liver motion and deformation during the respiratory cycle using intensity-based free-form registration of gated MR images

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.

Left ventricular motion reconstruction from planar tagged MR images: a comparison

Through recent development of MR techniques, it is now possible to assess regional myocardial wall function in a non-invasive way. Using MR tagging, space is marked with planes which deform with the tissue, providing markers for tracking the local motion of the myocardium. Numerous methods to reconstruct the three-dimensional displacement field have been developed. The aim of this article is to provide a framework to quantitatively compare the performance of four methods the authors have developed. Five sets of experiments are described, and their results are reported. Instructions are also provided to perform similar tests on any method using the same data. The experiments show that some characteristic properties of the methods, such as sensitivity to noise or spatial resolution, can be quantitatively classified. Cross-comparison of performances show what range values for these properties can be considered acceptable.

Dose broadening due to target position variability during fractionated breath-held radiation therapy

Recent advances in Stereotactic Radiosurgery/Conformal Radiotherapy have made it possible to deliver surgically precise radiation therapy to small lesions while preserving the surrounding tissue. However, because of physiologic motion, the application of conformal radiotherapy to extra-cranial tumors is, at present, geared toward slowing the progression of disease rather than obtaining a cure. At the University of Rochester, we are investigating the use of patient breath-holding to reduce respiratory-derived motion in fractional radiotherapy. The primary targeting problem then becomes the small variation in tumor location over repeated breath-holds. This paper describes the effects of residual target position uncertainty on the dose distribution observed by small extra-cranial tumors and their neighboring tissues during fractional radiation treatment using breath holding. We employ two computational methods to study these effects: numerical analysis via Monte Carlo simulation and analytical computation using three-dimensional convolution. These methods are demonstrated on a 2-arc, 10-fraction treatment plan used to treat a representative lung tumor in a human subject. In the same human subject, the variability in position of a representative lung tumor was measured over repeated end-expiration breath-holds using volumetric imaging. For the 7 x 7 x 10 mm margin used to treat this 12 mm diameter tumor and the measured target position variability, we demonstrated that the entire tumor volume was irradiated to at least 48 Gy-well above the tumoricidal threshold. The advantages, in terms of minimizing the volume of surrounding lung tissue that is radiated to high dose during treatment, of using end-expiration breath holding compared with end-inspiration breath-holding are demonstrated using representative tumor size and position variability parameters. It is hoped that these results will ultimately lead to improved, if not curative, treatment for small (5-20 mm diameter) lung, liver, and other extra-cranial lesions.

Lung metastases detection in CT images using 3D template matching

The aim of this study is to demonstrate a novel, fully automatic computer detection method applicable to metastatic tumors to the lung with a diameter of 4-20 mm in high-risk patients using typical computed tomography (CT) scans of the chest. Three-dimensional (3D) spherical tumor appearance models (templates) of various sizes were created to match representative CT imaging parameters and to incorporate partial volume effects. Taking into account the variability in the location of CT sampling planes cut through the spherical models, three offsetting template models were created for each appearance model size. Lung volumes were automatically extracted from computed tomography images and the correlation coefficients between the subregions around each voxel in the lung volume and the set of appearance models were calculated using a fast frequency domain algorithm. To determine optimal parameters for the templates, simulated tumors of varying sizes and eccentricities were generated and superposed onto a representative human chest image dataset. The method was applied to real image sets from 12 patients with known metastatic disease to the lung. A total of 752 slices and 47 identifiable tumors were studied. Spherical templates of three sizes (6, 8, and 10 mm in diameter) were used on the patient image sets; all 47 true tumors were detected with the inclusion of only 21 false positives. This study demonstrates that an automatic and straightforward 3D template-matching method, without any complex training or postprocessing, can be used to detect small lung metastases quickly and reliably in the clinical setting.

Evidence That MR Diffusion Tensor Imaging (Tractography) Predicts the Natural History of Regional Progression in Patients Irradiated Conformally for Primary Brain Tumors

PURPOSE Stereotactic radiotherapy (SRT) is fast becoming the method of choice for treatment of nonsuperficial brain lesions. SRT treatment plans of malignant brain tumors typically incorporate a 20-mm isotropic margin to account for microscopic tumor spread; however, distant or progressive tumors occur outside this margin. Our hypothesis is that paths of elevated water diffusion may provide a preferred route for transport or migration of cancer cells. If our hypothesis is correct, then future SRT treatment volumes could be modified to provide elongated treatment margins along the paths of elevated water diffusion, thereby creating a biologically better treatment plan that may reduce the incidence of progression. METHODS AND MATERIALS Magnetic resonance diffusion tensor imaging (DTI) datasets were acquired on patient subjects before the appearance of >5 mm diameter progressive lesions or secondary tumors. DTI was performed using an echo-planar imaging sequence on a 1.5T clinical General Electric scanner with voxel dimensions of 0.98 x 0.98 x 6 mm. After SRT, patients were given repeated magnetic resonance imaging follow-ups at regular intervals to identify early tumor progression. When progressive disease was detected, DTIstudio and FMRIB Software Library software was used to compute paths of preferred water diffusion through the primary tumor site and the site of progression. RESULTS Our preliminary results on 14 patient datasets suggest a strong relationship between routes of elevated water diffusion from the primary tumor and the location of tumor progression. CONCLUSIONS Further investigation is therefore warranted. Future work will employ more sophisticated fiber analysis in a prospective study.

Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching.

To demonstrate the efficacy of an automated three‐dimensional (3D) template matching‐based algorithm in detecting brain metastases on conventional MR scans and the potential of our algorithm to be developed into a computer‐aided detection tool that will allow radiologists to maintain a high level of detection sensitivity while reducing image reading time.

Triptolide Mitigates Radiation-Induced Pulmonary Fibrosis

Triptolide (TPL) may mitigate radiation-induced late pulmonary side effects through its inhibition of global pro-inflammatory cytokines. In this study, we evaluated the effect of TPL in C57BL/6 mice, the animals were exposed to radiation with vehicle (15 Gy), radiation with TPL (0.25 mg/kg i.v., twice weekly for 1, 2 and 3 months), radiation and celecoxib (CLX) (30 mg/kg) and sham irradiation. Cultured supernatant of irradiated RAW 264.7 and MLE-15 cells and lung lysate in different groups were enzyme-linked immunosorbent assays at 33 h. Respiratory rate, pulmonary compliance and pulmonary density were measured at 5 months in all groups. The groups exposed to radiation with vehicle and radiation with TPL exhibited significant differences in respiratory rate and pulmonary compliance (480 ± 75/min vs. 378 ± 76/min; 0.6 ± 0.1 ml/cm H2O/p kg vs. 0.9 ± 0.2 ml/cm H2O/p kg). Seventeen cytokines were significantly reduced in the lung lysate of the radiation exposure with TPL group at 5 months compared to that of the radiation with vehicle group, including profibrotic cytokines implicated in pulmonary fibrosis, such as IL-1β, TGF- β1 and IL-13. The radiation exposure with TPL mice exhibited a 41% reduction of pulmonary density and a 25% reduction of hydroxyproline in the lung, compared to that of radiation with vehicle mice. The trichrome-stained area of fibrotic foci and pathological scaling in sections of the mice treated with radiation and TPL mice were significantly less than those of the radiation with vehicle-treated group. In addition, the radiation with TPL-treated mice exhibited a trend of improved survival rate compared to that of the radiation with vehicle-treated mice at 5 months (83% vs. 53%). Three radiation-induced profibrotic cytokines in the radiation with vehicle-treated group were significantly reduced by TPL treatment, and this partly contributed to the trend of improved survival rate and pulmonary density and function and the decreased severity of pulmonary fibrosis at 5 months. Our findings indicate that TPL could be a potential new agent to mitigate radiation-induced pulmonary fibrosis.

Automatic segmentation of tumor-laden lung volumes from the LIDC database

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.

Realistic simulated lung nodule dataset for testing CAD detection and sizing

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 imaging modalities.