Effects on Lung Tissue After Breast Cancer Radiation: Comparing Photon and Proton Therapies.

Purpose: In breast cancer, improved treatment approaches that reduce injury to lung tissue and early diagnosis and intervention for lung toxicity are increasingly important in survivorship. The aims of this study are to (1) compare lung tissue radiographic changes in women treated with conventional photon radiation therapy and those treated with proton therapy (PT), (2) assess the volume of lung irradiated to 5 Gy (V5) and 20 Gy (V20) by treatment modality, and (3) quantify the effects of V5, V20, time, and smoking history on the severity of tissue radiographic changes. Patients and Methods: A prospective observational study of female breast cancer patients was conducted to monitor postradiation subclinical lung tissue radiographic changes. Repeated follow-up x-ray computed tomography scans were acquired through 2 years after treatment. In-house software was used to quantify an internally normalized measure of pulmonary tissue density change over time from the computed tomography scans, emphasizing the 6- and 12-month time points. Results: Compared with photon therapy, PT was associated with significantly lower lung V5 and V20. Lung V20 (but not V5) correlated significantly with increased subclinical lung tissue radiographic changes 6 months after treatment, and neither correlated with lung effects at 12 months. Significant lung tissue density changes were present in photon therapy patients at 6 and 12 months but not in PT patients. Significant lung tissue density change persisted at 12 months in ever-smokers but not in never-smokers. Conclusion: Patients treated with PT had significantly lower radiation exposure to the lungs and less statistically significant tissue density change, suggesting decreased injury and/or improved recovery compared to photon therapy. These findings motivate additional studies in larger, randomized, and more diverse cohorts to further investigate the contributions of treatment modality and smoking regarding the short- and long-term radiographic effects of radiation on lung tissue.

Accuracy of Left Ventricular Cavity Volume and Ejection Fraction for Conventional Estimation Methods and 3D Surface Fitting.

Background While left ventricular cavity volume ( LVV ) and ejection fraction ( LVEF ) are used routinely for clinical decision-making, the errors in LVV and LVEF estimates in the clinic have yet to be rigorously quantified and are perhaps underappreciated. Methods and Results The goal of this study was to quantify the accuracy and precision of several common geometric-model-based methods for estimating LVV and LVEF using a highly sampled, high-resolution magnetic resonance imaging data set and an independent ground truth. The effect on LVV and LVEF accuracy of slice number and orientation was also studied. When using the common geometric assumptions and limited short- and/or long-axis views, the expected LVEF measurement uncertainty can be as high as 49%. The composite midpoint rule applied to a stack of short-axis slices can achieve LVEF error <3% and LVV error of ≈10%, but in the clinic an additional ≈8% uncertainty is expected. An analogous approach applied to a series of radially prescribed long-axis slices can achieve higher LVEF accuracy, up to 3.9% with 12 slices, and more reliable LVV measurements than methods based solely on short-axis images. Using a mathematical 3-dimensional surface model that incorporates anatomic information from multiple views achieves superior accuracy, with LVEF error <4% and LVV error <2.5% when using 6 slices in each short- and long-axis view. Conclusions Combining anatomical information from multiple views into a conformal 3-dimensional surface model greatly reduces errors in LVV and LVEF estimates, with potential clinical benefit via improved early detection of cardiac disease.

Validation of the Gatortail method for accurate sizing of pulmonary vessels from 3D medical images.

PURPOSE: Detailed characterization of changes in vessel size is crucial for the diagnosis and management of a variety of vascular diseases. Because clinical measurement of vessel size is typically dependent on the radiologist's subjective interpretation of the vessel borders, it is often prone to high inter- and intra-user variability. Automatic methods of vessel sizing have been developed for two-dimensional images but a fully three-dimensional (3D) method suitable for vessel sizing from volumetric X-ray computed tomography (CT) or magnetic resonance imaging has heretofore not been demonstrated and validated robustly. METHODS: In this paper, we refined and objectively validated Gatortail, a method that creates a mathematical geometric 3D model of each branch in a vascular tree, simulates the appearance of the virtual vascular tree in a 3D CT image, and uses the similarity of the simulated image to a patient's CT scan to drive the optimization of the model parameters, including vessel size, to match that of the patient. The method was validated with a 2-dimensional virtual tree structure under deformation, and with a realistic 3D-printed vascular phantom in which the diameter of 64 branches were manually measured 3 times each. The phantom was then scanned on a conventional clinical CT imaging system and the images processed with the in-house software to automatically segment and mathematically model the vascular tree, label each branch, and perform the Gatortail optimization of branch size and trajectory. Previously proposed methods of vessel sizing using matched Gaussian filters and tubularity metrics were also tested. The Gatortail method was then demonstrated on the pulmonary arterial tree segmented from a human volunteer's CT scan. RESULTS: The standard deviation of the difference between the manually measured and Gatortail-based radii in the 3D physical phantom was 0.074 mm (0.087 in-plane pixel units for image voxels of dimension 0.85 × 0.85 × 1.0 mm) over the 64 branches, representing vessel diameters ranging from 1.2 to 7 mm. The linear regression fit gave a slope of 1.056 and an R2 value of 0.989. These three metrics reflect superior agreement of the radii estimates relative to previously published results over all sizes tested. Sizing via matched Gaussian filters resulted in size underestimates of >33% over all three test vessels, while the tubularity-metric matching exhibited a sizing uncertainty of >50%. In the human chest CT data set, the vessel voxel intensity profiles with and without branch model optimization showed excellent agreement and improvement in the objective measure of image similarity. CONCLUSIONS: Gatortail has been demonstrated to be an automated, objective, accurate and robust method for sizing of vessels in 3D non-invasively from chest CT scans. We anticipate that Gatortail, an image-based approach to automatically compute estimates of blood vessel radii and trajectories from 3D medical images, will facilitate future quantitative evaluation of vascular response to disease and environmental insult and improve understanding of the biological mechanisms underlying vascular disease processes.

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

PURPOSE: 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. MATERIALS AND METHODS: Spherical tumor appearance models were created to match the expected geometry of brain metastases while accounting for partial volume effects and offsets due to the cut of MRI sampling planes. A 3D normalized cross-correlation coefficient was calculated between the brain volume and spherical templates of varying radii using a fast frequency domain algorithm to identify likely positions of brain metastases. RESULTS: Algorithm parameters were optimized on training datasets, and then data were collected on 22 patient datasets containing 79 total brain metastases producing a sensitivity of 89.9% with a false positive rate of 0.22 per image slice when restricted to the brain mass. CONCLUSION: Study results demonstrate that the 3D template matching-based method can be an effective, fast, and accurate approach that could serve as a useful tool for assisting radiologists in providing earlier and more definitive diagnoses of metastases within the brain.

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.

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.

Long-term management of patients with multiple brain metastases after shaped beam radiosurgery. Case report and review of the literature.

The role of radiosurgery in the treatment of patients with advanced-stage metastatic disease is currently under debate. Previous randomized studies have not consistently supported the use of radiosurgery to treat patients with numbers of brain metastases. In negative-results studies, however, intracranial tumor control was high but extracranial disease progressed; thus, patient survival was not greatly affected, although neurocognitive function was generally maintained until death. Because the future promises improved systemic (extracranial) therapy, the successful control of brain disease is that much more crucial. Thus, for selected patients with multiple metastases to the brain who remain in good neurological condition, aggressive lesion-targeting radiosurgery should be very useful. Although a major limitation to success of this therapy is the lack of control of extracranial disease in most patients, it is clear that well-designed, aggressive treatment substantially decreases the progression of brain metastases and also improves neurocognitive survival. The authors present the management and a methodology for rational treatment of a patient with breast cancer who has harbored 24 brain metastases during a 3-year period.

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.

Optimal marker placement in photogrammetry patient positioning system.

A photogrammetry-based patient positioning system has been used instead of the conventional laser alignment technique for patient set-up in external beam radiotherapy. It tracks skin affixed reflective markers with multiple infrared cameras. The three-dimensional (3D) positions of the markers provide reference information to determine the treatment plan isocenter location and hence provide the ability to position the lesion at the isocenter of the treatment linear accelerator. However, in current clinical practice for lung or liver lesion treatments, fiducial markers are usually randomly affixed onto the patients' chest and abdomen, so that the actual target registration error (TRE) of the internal lesions inside the body may be large, depending on the fiducial registration error (FRE). There exists an optimal marker configuration that can minimize the TRE. In this paper, we developed methods to design the patient-specific optimal configurations of the surface makers to minimize the TRE, given the patient's surface contour, the lesion position and the FRE. Floating genetic algorithm (GA) optimization was used to optimize the positions of the skin markers. The surface curve of the patient body was determined by an automatic segmentation algorithm from the planning CT. The method was evaluated using a body phantom implanted with a metal ball (a simulated target). By registering two CT scans using the surface markers and measuring the displacement of the target, the TRE was measured. The TRE was also measured by taking two orthogonal portal films after positioning the phantom using the photogrammetry based patient positioning system. A 50% reduction in TRE has been achieved by using the optimal configuration compared to the random configuration. This result demonstrates that the optimization of a fiducial configuration can result in improved tumor targeting ability.