L Cervino

Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy

Accurate lung tumor tracking in real time is a keystone to image-guided radiotherapy of lung cancers. Existing lung tumor tracking approaches can be roughly grouped into three categories: (1) deriving tumor position from external surrogates; (2) tracking implanted fiducial markers fluoroscopically or electromagnetically; (3) fluoroscopically tracking lung tumor without implanted fiducial markers. The first approach suffers from insufficient accuracy, while the second may not be widely accepted due to the risk of pneumothorax. Previous studies in fluoroscopic markerless tracking are mainly based on template matching methods, which may fail when the tumor boundary is unclear in fluoroscopic images. In this paper we propose a novel markerless tumor tracking algorithm, which employs the correlation between the tumor position and surrogate anatomic features in the image. The positions of the surrogate features are not directly tracked; instead, we use principal component analysis of regions of interest containing them to obtain parametric representations of their motion patterns. Then, the tumor position can be predicted from the parametric representations of surrogates through regression. Four regression methods were tested in this study: linear and two-degree polynomial regression, artificial neural network (ANN) and support vector machine (SVM). The experimental results based on fluoroscopic sequences of ten lung cancer patients demonstrate a mean tracking error of 2.1 pixels and a maximum error at a 95% confidence level of 4.6 pixels (pixel size is about 0.5 mm) for the proposed tracking algorithm.

4D CT sorting based on patient internal anatomy

Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95+/-0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68+/-0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our 4D CT internal sorting method eliminates the need of externally recorded surrogates of respiratory motion. It is an automatic, accurate, robust, cost efficient and yet simple method and therefore can be readily implemented in clinical settings.

The diaphragm as an anatomic surrogate for lung tumor motion.

Lung tumor motion due to respiration poses a challenge in the application of modern three-dimensional conformal radiotherapy. Direct tracking of the lung tumor during radiation therapy is very difficult without implanted fiducial markers. Indirect tracking relies on the correlation of the tumors motion and the surrogates motion. The present paper presents an analysis of the correlation between tumor motion and diaphragm motion in order to evaluate the potential use of diaphragm as a surrogate for tumor motion. We have analyzed the correlation between diaphragm motion and superior-inferior lung tumor motion in 32 fluoroscopic image sequences from ten lung cancer patients. A simple linear model and a more complex linear model that accounts for phase delays between the two motions have been used. Results show that the diaphragm is a good surrogate for tumor motion prediction for most patients, resulting in an average correlation factor of 0.94 and 0.98 with each model respectively. The model that accounts for delays leads to an average localization prediction error of 0.8 mm and an error at the 95% confidence level of 2.1 mm. However, for one patient studied, the correlation is much weaker compared to other patients. This indicates that, before using diaphragm for lung tumor prediction, the correlation should be examined on a patient-by-patient basis.

MRI-guided tumor tracking in lung cancer radiotherapy

Precise tracking of lung tumor motion during treatment delivery still represents a challenge in radiation therapy. Prototypes of MRI-linac hybrid systems are being created which have the potential of ionization-free real-time imaging of the tumor. This study evaluates the performance of lung tumor tracking algorithms in cine-MRI sagittal images from five healthy volunteers. Visible vascular structures were used as targets. Volunteers performed several series of regular and irregular breathing. Two tracking algorithms were implemented and evaluated: a template matching (TM) algorithm in combination with surrogate tracking using the diaphragm (surrogate was used when the maximum correlation between the template and the image in the search window was less than specified), and an artificial neural network (ANN) model based on the principal components of a region of interest that encompasses the target motion. The mean tracking error ē and the error at 95% confidence level e(95) were evaluated for each model. The ANN model led to ē = 1.5 mm and e(95) = 4.2 mm, while TM led to ē = 0.6 mm and e(95) = 1.0 mm. An extra series was considered separately to evaluate the benefit of using surrogate tracking in combination with TM when target out-of-plane motion occurs. For this series, the mean error was 7.2 mm using only TM and 1.7 mm when the surrogate was used in combination with TM. Results show that, as opposed to tracking with other imaging modalities, ANN does not perform well in MR-guided tracking. TM, however, leads to highly accurate tracking. Out-of-plane motion could be addressed by surrogate tracking using the diaphragm, which can be easily identified in the images.

Markerless lung tumor tracking and trajectory reconstruction using rotational cone-beam projections: a feasibility study

Algorithms for direct tumor tracking in rotational cone-beam projections and for reconstruction of phase-binned 3D tumor trajectories were developed. The feasibility of the algorithm was demonstrated on a digital phantom, a physical phantom and two patients. Tracking results were obtained by comparing reference templates generated from 4DCT to rotational cone-beam projections. The 95th percentile absolute errors (e(95)) in phantom tracking results did not exceed 1.7 mm in either imager dimension, while e(95) in the patients was 3.3 mm or less. Accurate phase-binned trajectories were reconstructed in each case, with 3D maximum errors of no more than 1.0 mm in the phantoms and 2.0 mm in the patients. This work shows the feasibility of a direct tumor tracking technique for rotational images, and demonstrates that an accurate 3D tumor trajectory can be reconstructed from relatively less accurate tracking results. The ability to reconstruct the tumors average trajectory from a 3D cone-beam CT scan on the day of treatment could allow for better patient setup and quality assurance, while direct tumor tracking in rotational projections could be clinically useful for rotational therapy such as volumetric modulated arc therapy (VMAT).

Using surface imaging and visual coaching to improve the reproducibility and stability of deep-inspiration breath hold for left-breast-cancer radiotherapy

Late cardiac complications may arise after left-breast radiation therapy. Deep-inspiration breath hold (DIBH) allows reduction of the irradiated heart volume at the same time as it reduces tumor bed motion and increases lung sparing. In the present study, we have evaluated the improvement in reproducibility and stability of the DIBH for left-breast-cancer treatment when visual coaching is provided with the aid of 3D video surface imaging and video goggles. Five left-breast-cancer patients and fifteen healthy volunteers were asked to perform a series of DIBHs without and with visual coaching. Reproducibility and stability of DIBH were measured for each individual with and without visual coaching. The average reproducibility and stability changed from 2.1 mm and 1.5 mm, respectively, without visual feedback to 0.5 mm and 0.7 mm with visual feedback, showing a significant statistical difference (p < 0.001 for reproducibility, p < 0.01 for stability). Significant changes (>2 mm) in reproducibility and stability were observed in 35% and 15% of the subjects, respectively. The average chest wall excursion of the DIBH with respect to the free breathing preceding the DIBH was found to be 11.3 mm. The reproducibility and stability of the DIBH improve significantly from the visual coaching provided to the patient, especially in those patients with poor reproducibility and stability.

A comprehensive study on the relationship between the image quality and imaging dose in low-dose cone beam CT

While compressed sensing (CS)-based algorithms have been developed for the low-dose cone beam CT (CBCT) reconstruction, a clear understanding of the relationship between the image quality and imaging dose at low-dose levels is needed. In this paper, we qualitatively investigate this subject in a comprehensive manner with extensive experimental and simulation studies. The basic idea is to plot both the image quality and imaging dose together as functions of the number of projections and mAs per projection over the whole clinically relevant range. On this basis, a clear understanding of the tradeoff between the image quality and imaging dose can be achieved and optimal low-dose CBCT scan protocols can be developed to maximize the dose reduction while minimizing the image quality loss for various imaging tasks in image-guided radiation therapy (IGRT). Main findings of this work include (1) under the CS-based reconstruction framework, image quality has little degradation over a large range of dose variation. Image quality degradation becomes evident when the imaging dose (approximated with the x-ray tube load) is decreased below 100 total mAs. An imaging dose lower than 40 total mAs leads to a dramatic image degradation, and thus should be used cautiously. Optimal low-dose CBCT scan protocols likely fall in the dose range of 40-100 total mAs, depending on the specific IGRT applications. (2) Among different scan protocols at a constant low-dose level, the super sparse-view reconstruction with the projection number less than 50 is the most challenging case, even with strong regularization. Better image quality can be acquired with low mAs protocols. (3) The optimal scan protocol is the combination of a medium number of projections and a medium level of mAs/view. This is more evident when the dose is around 72.8 total mAs or below and when the ROI is a low-contrast or high-resolution object. Based on our results, the optimal number of projections is around 90 to 120. (4) The clinically acceptable lowest imaging dose level is task dependent. In our study, 72.8 mAs is a safe dose level for visualizing low-contrast objects, while 12.2 total mAs is sufficient for detecting high-contrast objects of diameter greater than 3 mm.

Frame-less and mask-less cranial stereotactic radiosurgery: a feasibility study

Currently, high-precision delivery in stereotactic radiosurgery (SRS) is achieved via high-precision target localization and rigid patient immobilization. Rigid patient immobilization can result in, however, patient discomfort, which is exacerbated by the long duration of SRS treatments and may induce patient movement. To address this issue, we developed a new SRS technique that is aimed to minimize patient discomfort while maintaining high-precision treatment, based on a less-rigid patient immobilization combined with continuous patient motion monitoring. In this paper, we examine the feasibility of this new technique. An anthropomorphic head phantom is used to check the accuracy of a 3D surface imaging system that provides the monitoring. Volunteers are used to study patient motion inside a new type of head mold that is used for minimal immobilization. Results show that for different couch angles, the difference between the phantom positions recorded by the surface imaging system and by an infrared optical tracking system was within 1 mm in displacements and 1 degrees in rotation. The motion detected by both systems during couch shifts is within 1 mm agreement. The average maximum volunteer head motion in the head mold during the 20 min interval in any direction was 0.7 mm (range: 0.4-1.1 mm). Patient motion due to couch motion was always less than 0.2 mm. We conclude that motion inside the minimally immobilizing head mold is small and can be accurately detected by real-time surface imaging.

Classification of breast computed tomography data

Differences in breast tissue composition are important determinants in assessing risk, identifying disease in images and following changes over time. This paper presents an algorithm for tissue classification that separates breast tissue into its three primary constituents of skin, fat and glandular tissue. We have designed and built a dedicated breast CT scanner. Fifty-five normal volunteers and patients with mammographically identified breast lesions were scanned. Breast CT voxel data were filtered using a 5 pt median filter and the image histogram was computed. A two compartment Gaussian fit of histogram data was used to provide an initial estimate of tissue compartments. After histogram analysis, data were input to region-growing algorithms and classified as to belonging to skin, fat or gland based on their value and architectural features. Once tissues were classified, a more detailed analysis of glandular tissue patterns and a more quantitative analysis of breast composition was made. Algorithm performance assessment demonstrated very good or excellent agreement between algorithm and radiologist observers in 97.7% of the segmented data. We observed that even in dense breasts the fraction of glandular tissue seldom exceeded 50%. For most individuals the composition is better characterized as being a 70% (fat)-30% (gland) composition than a 50% (fat)-50% (gland) composition.

Simulation of Mechanical Compression of Breast Tissue

Comparison of uncompressed volumetric breast data to compressed projection mammographic data poses a variety of challenges to accurately localize anatomy in both data sets. This work presents a method for simulating the mechanical compression of volumetric breast data. We use an approach based on a rectilinear-grid finite-element mesh and apply the method to known objects including volumetric breast data. Overall results show good agreement with theory and reasonable qualitative agreement with clinical results. Analysis times are sufficiently short to be used in the clinical setting for data comparison. The methods presented here provide a high quality method for simulating mechanical compression of breast data.