James A. Shackleford

On developing B-spline registration algorithms for multi-core processors.

Spline-based deformable registration methods are quite popular within the medical-imaging community due to their flexibility and robustness. However, they require a large amount of computing time to obtain adequate results. This paper makes two contributions towards accelerating B-spline-based registration. First, we propose a grid-alignment scheme and associated data structures that greatly reduce the complexity of the registration algorithm. Based on this grid-alignment scheme, we then develop highly data parallel designs for B-spline registration within the stream-processing model, suitable for implementation on multi-core processors such as graphics processing units (GPUs). Particular attention is focused on an optimal method for performing analytic gradient computations in a data parallel fashion. CPU and GPU versions are validated for execution time and registration quality. Performance results on large images show that our GPU algorithm achieves a speedup of 15 times over the single-threaded CPU implementation whereas our multi-core CPU algorithm achieves a speedup of 8 times over the single-threaded implementation. The CPU and GPU versions achieve near-identical registration quality in terms of RMS differences between the generated vector fields.

Motion Interplay as a Function of Patient Parameters and Spot Size in Spot Scanning Proton Therapy for Lung Cancer

PURPOSE To quantify the impact of respiratory motion on the treatment of lung tumors with spot scanning proton therapy. METHODS AND MATERIALS Four-dimensional Monte Carlo simulations were used to assess the interplay effect, which results from relative motion of the tumor and the proton beam, on the dose distribution in the patient. Ten patients with varying tumor sizes (2.6-82.3 cc) and motion amplitudes (3-30 mm) were included in the study. We investigated the impact of the spot size, which varies between proton facilities, and studied single fractions and conventionally fractionated treatments. The following metrics were used in the analysis: minimum/maximum/mean dose, target dose homogeneity, and 2-year local control rate (2y-LC). RESULTS Respiratory motion reduces the target dose homogeneity, with the largest effects observed for the highest motion amplitudes. Smaller spot sizes (σ ≈ 3 mm) are inherently more sensitive to motion, decreasing target dose homogeneity on average by a factor 2.8 compared with a larger spot size (σ ≈ 13 mm). Using a smaller spot size to treat a tumor with 30-mm motion amplitude reduces the minimum dose to 44.7% of the prescribed dose, decreasing modeled 2y-LC from 87.0% to 2.7%, assuming a single fraction. Conventional fractionation partly mitigates this reduction, yielding a 2y-LC of 71.6%. For the large spot size, conventional fractionation increases target dose homogeneity and prevents a deterioration of 2y-LC for all patients. No correlation with tumor volume is observed. The effect on the normal lung dose distribution is minimal: observed changes in mean lung dose and lung V20 are <0.6 Gy(RBE) and <1.7%, respectively. CONCLUSIONS For the patients in this study, 2y-LC could be preserved in the presence of interplay using a large spot size and conventional fractionation. For treatments using smaller spot sizes and/or in the delivery of single fractions, interplay effects can lead to significant deterioration of the dose distribution and lower 2y-LC.

Integrated plasmonic lens photodetector

Metal-semiconductor-metal (MSM) photodetectors may see increased responsivity when a plasmonic lens is integrated with the photodetector. The increased responsivity of the photodetector may be a result of effectively ‘guiding’ photons into the active area of the device in the form of a surface plasmon polariton. In one embodiment, the plasmonic lens may not substantially decrease the speed of the MSM photodetector. In another embodiment, the Shottkey contacts of the MSM photodetector may be corrugated to provide integrated plasmonic lens. For example, one or more of the cathodes and anodes can be modified to create a plurality of corrugations. These corrugations may be configured as a plasmonic lens on the surface of a photodetector. The corrugations may be configured as parallel linear corrugations, equally spaced curved corrugations, curved parallel corrugations, approximately equally spaced concentric circular corrugations, chirped corrugations or the like.

Analytic regularization of uniform cubic b-spline deformation fields

Image registration is inherently ill-posed, and lacks a unique solution. In the context of medical applications, it is desirable to avoid solutions that describe physically unsound deformations within the patient anatomy. Among the accepted methods of regularizing non-rigid image registration to provide solutions applicable to medical practice is the penalty of thin-plate bending energy. In this paper, we develop an exact, analytic method for computing the bending energy of a three-dimensional B-spline deformation field as a quadratic matrix operation on the spline coefficient values. Results presented on ten thoracic case studies indicate the analytic solution is between 61-1371x faster than a numerical central differencing solution.

Computing proton dose to irregularly moving targets

PURPOSE While four-dimensional computed tomography (4DCT) and deformable registration can be used to assess the dose delivered to regularly moving targets, there are few methods available for irregularly moving targets. 4DCT captures an idealized waveform, but human respiration during treatment is characterized by gradual baseline shifts and other deviations from a periodic signal. This paper describes a method for computing the dose delivered to irregularly moving targets based on 1D or 3D waveforms captured at the time of delivery. METHODS The procedure uses CT or 4DCT images for dose calculation, and 1D or 3D respiratory waveforms of the target position at time of delivery. Dose volumes are converted from their Cartesian geometry into a beam-specific radiological depth space, parameterized in 2D by the beam aperture, and longitudinally by the radiological depth. In this new frame of reference, the proton doses are translated according to the motion found in the 1D or 3D trajectory. These translated dose volumes are weighted and summed, then transformed back into Cartesian space, yielding an estimate of the dose that includes the effect of the measured breathing motion. The method was validated using a synthetic lung phantom and a single representative patient CT. Simulated 4DCT was generated for the phantom with 2 cm peak-to-peak motion. RESULTS A passively-scattered proton treatment plan was generated using 6 mm and 5 mm smearing for the phantom and patient plans, respectively. The method was tested without motion, and with two simulated breathing signals: a 2 cm amplitude sinusoid, and a 2 cm amplitude sinusoid with 3 cm linear drift in the phantom. The tumor positions were equally weighted for the patient calculation. Motion-corrected dose was computed based on the mid-ventilation CT image in the phantom and the peak exhale position in the patient. Gamma evaluation was 97.8% without motion, 95.7% for 2 cm sinusoidal motion, 95.7% with 3 cm drift in the phantom (2 mm, 2%), and 90.8% (3 mm, 3%)for the patient data. CONCLUSIONS We have demonstrated a method for accurately reproducing proton dose to an irregularly moving target from a single CT image. We believe this algorithm could prove a useful tool to study the dosimetric impact of baseline shifts either before or during treatment.

Automated Protein Localization of Blood Brain Barrier Vasculature in Brightfield IHC Images

In this paper, we present an objective method for localization of proteins in blood brain barrier (BBB) vasculature using standard immunohistochemistry (IHC) techniques and bright-field microscopy. Images from the hippocampal region at the BBB are acquired using bright-field microscopy and subjected to our segmentation pipeline which is designed to automatically identify and segment microvessels containing the protein glucose transporter 1 (GLUT1). Gabor filtering and k-means clustering are employed to isolate potential vascular structures within cryosectioned slabs of the hippocampus, which are subsequently subjected to feature extraction followed by classification via decision forest. The false positive rate (FPR) of microvessel classification is characterized using synthetic and non-synthetic IHC image data for image entropies ranging between 3 and 8 bits. The average FPR for synthetic and non-synthetic IHC image data was found to be 5.48% and 5.04%, respectively.

Different cyclical intermittent hypoxia severities have different effects on hippocampal microvasculature.

Recent studies have shown an association between obstructive sleep apnea (OSA) and cognitive impairment. This study was done to investigate whether varied levels of cyclical intermittent hypoxia (CIH) differentially affect the microvasculature in the hippocampus, operating as a mechanistic link between OSA and cognitive impairment. We exposed C57BL/6 mice to sham [continuous air, arterial O2 saturation (SaO2 ) 97%], severe CIH to inspired O2 fraction (FiO2 ) = 0.10 (CIH10; SaO2 nadir of 61%), or very severe CIH to FiO2 = 0.05 (CIH5; SaO2 nadir of 37%) for 12 h/day for 2 wk. We quantified capillary length using neurostereology techniques in the dorsal hippocampus and utilized quantitative PCR methods to measure changes in sets of genes related to angiogenesis and to metabolism. Next, we employed immunohistochemistry semiquantification algorithms to quantitate GLUT1 protein on endothelial cells within hippocampal capillaries. Capillary length differed among CIH severity groups (P = 0.013) and demonstrated a linear relationship with CIH severity (P = 0.002). There was a strong association between CIH severity and changes in mRNA for VEGFA (P < 0.0001). Less strong, but nominally significant associations with CIH severity were also observed for ANGPT2 (PANOVA = 0.065, PTREND = 0.040), VEGFR2 (PANOVA = 0.032, PTREND = 0.429), and TIE-2 (PANOVA = 0.006, PTREND = 0.010). We found that the CIH5 group had increased GLUT1 protein relative to sham (P = 0.006) and CIH10 (P = 0.001). There was variation in GLUT1 protein along the microvasculature in different hippocampal subregions. An effect of CIH5 on GLUT1 mRNA was seen (PANOVA = 0.042, PTREND = 0.012). Thus CIH affects the microvasculature in the hippocampus, but consequences depend on CIH severity.

TH‐C‐WAB‐03: A Robust Intensity Similarity Measure for Multi‐Atlas Segmentation

PURPOSE Atlas-based segmentation is a general approach to automatic segmentation that labels regions of an image based on their alignment to existing structures in an atlas image. The atlas-based approach can be improved by aligning multiple atlases with the target image, and fusing their results. A typical strategy for multi-atlas segmentation is weighted voting that combines structure distance with intensity similarity. This abstract investigates the use of a robust measure for penalizing the similarity of voxel intensities when voting. METHODS Experiments were performed comparing the robust measure, a truncated quadratic penalty, with the more commonly used quadratic penalty. An atlas database of 20 subjects with structures segmented on head and neck CT were evaluated. Training parameters were tuned using leave-one-out cross validation. RESULTS Automatic segmentation results were evaluated using the Dice similarity coefficient. The average Dice scores for segmentations produced with a quadratic penalty were 0.78 for brainstem; 0.78 and 0.77 for left and right eye balls; 0.66 and 0.64 for left and right parotids. The average Dice scores for segmentations produced with the truncated quadratic penalty were 0.82 for brainstem; 0.85 and 0.84 for left and right eye balls; 0.74 and 0.73 for left and right parotids. CONCLUSION A robust intensity similarity measure, such as a truncated quadratic penalty, can be an effective approach for improving overall segmentation quality for multi-atlas methods. National Institutes of Health.

Organ localization and identification in thoracic CT volumes using 3D CNNs leveraging spatial anatomic relations

In this paper, we present a model to obtain prior knowledge for organ localization in CT thorax images using three dimensional convolutional neural networks (3D CNNs). Specifically, we use the knowledge obtained from CNNs in a Bayesian detector to establish the presence and location of a given target organ defined within a spherical coordinate system. We train a CNN to perform a soft detection of the target organ potentially present at any point, x = [r,Θ,Φ]T. This probability outcome is used as a prior in a Bayesian model whose posterior probability serves to provide a more accurate solution to the target organ detection problem. The likelihoods for the Bayesian model are obtained by performing a spatial analysis of the organs in annotated training volumes. Thoracic CT images from the NSCLC–Radiomics dataset are used in our case study, which demonstrates the enhancement in robustness and accuracy of organ identification. The average value of the detector accuracies for the right lung, left lung, and heart were found to be 94.87%, 95.37%, and 90.76% after the CNN stage, respectively. Introduction of spatial relationship using a Bayes classifier improved the detector accuracies to 95.14%, 96.20%, and 95.15%, respectively, showing a marked improvement in heart detection. This workflow improves the detection rate since the decision is made employing both lower level features (edges, contour etc) and complex higher level features (spatial relationship between organs). This strategy also presents a new application to CNNs and a novel methodology to introduce higher level context features like spatial relationship between objects present at a different location in images to real world object detection problems.

An octree based approach to multi-grid B-spline registration

In this paper we propose a new strategy for the recovery of complex anatomical deformations that exhibit local discontinuities, such as the shearing found at the lung-ribcage interface, using multi-grid octree B-splines. B- spline based image registration is widely used in the recovery of respiration induced deformations between CT images. However, the continuity imposed upon the computed deformation field by the parametrizing cubic B- spline basis function results in an inability to correctly capture discontinuities such as the sliding motion at organ boundaries. The proposed technique efficiently captures deformation within and at organ boundaries without the need for prior knowledge, such as segmentation, by selectively increasing deformation freedom within image regions exhibiting poor local registration. Experimental results show that the proposed method achieves more physically plausible deformations than traditional global B-spline methods.