Leveraging Clinical Imaging Archives for Radiomics: Reliability of Automated Methods for Brain Volume Measurement.

Purpose To validate the use of thick-section clinically acquired magnetic resonance (MR) imaging data for estimating total brain volume (TBV), gray matter (GM) volume (GMV), and white matter (WM) volume (WMV) by using three widely used automated toolboxes: SPM ( www.fil.ion.ucl.ac.uk/spm/ ), FreeSurfer ( surfer.nmr.mgh.harvard.edu ), and FSL (FMRIB software library; Oxford Centre for Functional MR Imaging of the Brain, Oxford, England, https://fsl.fmrib.ox.ac.uk/fsl ). Materials and Methods MR images from a clinical archive were used and data were deidentified. The three methods were applied to estimate brain volumes from thin-section research-quality brain MR images and routine thick-section clinical MR images acquired from the same 38 patients (age range, 1-71 years; mean age, 22 years; 11 women). By using these automated methods, TBV, GMV, and WMV were estimated. Thin- versus thick-section volume comparisons were made for each method by using intraclass correlation coefficients (ICCs). Results SPM exhibited excellent ICCs (0.97, 0.85, and 0.83 for TBV, GMV, and WMV, respectively). FSL exhibited ICCs of 0.69, 0.51, and 0.60 for TBV, GMV, and WMV, respectively, but they were lower than with SPM. FreeSurfer exhibited excellent ICC of 0.63 only for TBV. Application of SPM's voxel-based morphometry on the modulated images of thin-section images and interpolated thick-section images showed fair to excellent ICCs (0.37-0.98) for the majority of brain regions (88.47% [306924 of 346916 voxels] of WM and 80.35% [377 282 of 469 502 voxels] of GM). Conclusion Thick-section clinical-quality MR images can be reliably used for computing quantitative brain metrics such as TBV, GMV, and WMV by using SPM. © RSNA, 2017 Online supplemental material is available for this article.

Scholte wave generation during single tracking location shear wave elasticity imaging of engineered tissues.

The physical environment of engineered tissues can influence cellular functions that are important for tissue regeneration. Thus, there is a critical need for noninvasive technologies capable of monitoring mechanical properties of engineered tissues during fabrication and development. This work investigates the feasibility of using single tracking location shear wave elasticity imaging (STL-SWEI) for quantifying the shear moduli of tissue-mimicking phantoms and engineered tissues in tissue engineering environments. Scholte surface waves were observed when STL-SWEI was performed through a fluid standoff, and confounded shear moduli estimates leading to an underestimation of moduli in regions near the fluid-tissue interface.

Spatial patterning of endothelial cells and vascular network formation using ultrasound standing wave fields.

The spatial organization of cells is essential for proper tissue assembly and organ function. Thus, successful engineering of complex tissues and organs requires methods to control cell organization in three dimensions. In particular, technologies that facilitate endothelial cell alignment and vascular network formation in three-dimensional tissue constructs would provide a means to supply essential oxygen and nutrients to newly forming tissue. Acoustic radiation forces associated with ultrasound standing wave fields can rapidly and non-invasively organize cells into distinct multicellular planar bands within three-dimensional collagen gels. Results presented herein demonstrate that the spatial pattern of endothelial cells within three-dimensional collagen gels can be controlled by design of acoustic parameters of the sound field. Different ultrasound standing wave field exposure parameters were used to organize endothelial cells into either loosely aggregated or densely packed planar bands. The rate of vessel formation and the morphology of the resulting endothelial cell networks were affected by the initial density of the ultrasound-induced planar bands of cells. Ultrasound standing wave fields provide a rapid, non-invasive approach to pattern cells in three-dimensions and direct vascular network formation and morphology within engineered tissue constructs.

A Feature-based Affine Registration Method for Capturing Background Lung Tissue Deformation for Ground Glass Nodule Tracking.

Lung nodule tracking assessment relies on cross-sectional measurements of the largest lesion profile depicted in initial and follow-up computed tomography (CT) images. However, apparent changes in nodule size assessed via simple image-based measurements may also be compromised by the effect of the background lung tissue deformation on the GGN between the initial and follow-up images, leading to erroneous conclusions about nodule changes due to disease. To compensate for the lung deformation and enable consistent nodule tracking, here we propose a feature-based affine registration method and study its performance vis-a-vis several other registration methods. We implement and test each registration method using both a lung- and a lesion-centered region of interest on ten patient CT datasets featuring twelve nodules, including both benign and malignant GGO lesions containing pure GGNs, part-solid, or solid nodules. We evaluate each registration method according to the target registration error (TRE) computed across 30 - 50 homologous fiducial landmarks surrounding the lesions and selected by expert radiologists in both the initial and follow-up patient CT images. Our results show that the proposed feature-based affine lesion-centered registration yielded a 1.1 ± 1.2 mm TRE, while a Symmetric Normalization deformable registration yielded a 1.2 ± 1.2 mm TRE, and a least-square fit registration of the 30-50 validation fiducial landmark set yielded a 1.5 ± 1.2 mm TRE. Although the deformable registration yielded a slightly higher registration accuracy than the feature-based affine registration, it is significantly more computationally efficient, eliminates the need for ambiguous segmentation of GGNs featuring ill-defined borders, and reduces the susceptibility of artificial deformations introduced by the deformable registration, which may lead to increased similarity between the registered initial and follow-up images, over-compensating for the background lung tissue deformation, and, in turn, compromising the true disease-induced nodule change assessment. We also assessed the registration qualitatively, by visual inspection of the subtraction images, and conducted a pilot pre-clinical study that showed the proposed feature-based lesion-centered affine registration effectively compensates for the background lung tissue deformation between the initial and follow-up images and also serves as a reliable baseline registration method prior to assessing lung nodule changes due to disease.

Evaluation of novel genetic algorithm generated schemes for positron emission tomography (PET)/magnetic resonance imaging (MRI) image fusion.

The use and benefits of a multimodality approach in the context of breast cancer imaging are discussed. Fusion techniques that allow multiple images to be viewed simultaneously are discussed. Many of these fusion techniques rely on the use of color tables. A genetic algorithm that generates color tables that have desired properties such as satisfying the order principle, the rows, and columns principle, have perceivable uniformity and have maximum contrast is introduced. The generated 2D color tables can be used for displaying fused datasets. The advantage the proposed method has over other techniques is the ability to consider a much larger set of possible color tables, ensuring that the best one is found. We asked radiologists to perform a set of tasks reading fused PET/MRI breast images obtained using eight different fusion techniques. This preliminary study clearly demonstrates the need and benefit of a joint display by estimating the inaccuracies incurred when using a side-by-side display. The study suggests that the color tables generated by the genetic algorithm are good choices for fusing MR and PET images. It is interesting to note that popular techniques such as the Fire/Gray and techniques based on the HSV color space, which are prevalent in the literature and clinical practice, appear to give poorer performance.

A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features.

Recent studies monitoring severity of abdominal aortic aneurysm (AAA) suggested that reliance on only the maximum transverse diameter ( D max ) may be insufficient to predict AAA rupture risk. Moreover, geometric indices, biomechanical parameters, material properties, and patient-specific historical data affect AAA morphology, indicating the need for an integrative approach that incorporates all factors for more accurate estimation of AAA severity. We implemented a machine learning algorithm using 45 features extracted from 66 patients. The model was generated using the J48 decision tree algorithm with the aim of maximizing model accuracy. Three different feature sets were used to assess the prediction rate: i) using D max as a single-feature set, ii) using a set of all features, and, lastly iii) using a feature set selected via the BestFirst feature selection algorithm. Our results indicate that BestFirst feature selection yielded the highest prediction accuracy. These results indicate that a combination of several specific parameters that comprehensively capture AAA behavior may enable a suitable assessment of AAA severity, suggesting the potential benefit of machine learning for this application.

Toward an Affine Feature-Based Registration Method for Ground Glass Lung Nodule Tracking.

Lung nodule progression assessment from medical imaging is a critical biomarker for assessing the course of the disease or the patient's response to therapy. CT images are routinely used to identify the location and size and rack the progression of lung nodules. However, nodule segmentation is challenging and prone to error, due to the irregular nodule boundaries, therefore introducing error in the lung nodule quantification process. Here, we describe the development and evaluation of a feature-based affine image registration framework that enables us to register two time point thoracic CT images as a means to account for the back-ground lung tissue deformation, then use digital subtraction images to assess tumor progression/regression. We have demonstrated this method on twelve de-identified patient datasets and showed that the proposed method yielded a better than 1.5mm registration accuracy vis-à-vis the widely accepted non-rigid image registration techniques. To demonstrate the potential clinical value of our described technique, we conducted a study in which our collaborating clinician was asked to provide an assessment of nodule progression/regression using the digital subtraction images post-registration. This assessment was consistent, yet provided more confidence, than the traditional lung nodule tracking based on visual analysis of the CT images.

Fusion viewer: a new tool for fusion and visualization of multimodal medical data sets.

A new application, Fusion Viewer, available for free, has been designed and implemented with a modular object-oriented design. The viewer provides both traditional and novel tools to fuse 3D data sets such as CT (computed tomography), MRI (magnetic resonance imaging), PET (positron emission tomography), and SPECT (single photon emission tomography) of the same subject, to create maximum intensity projections (MIP) and to adjust dynamic range. In many situations, it is desirable and advantageous to acquire biomedical images in more than one modality. For example, PET can be used to acquire functional data, whereas MRI can be used to acquire morphological data. In some situations, a side-by-side comparison of the images provides enough information, but in most of the cases it may be necessary to have the exact spatial relationship between the modalities presented to the observer. To accomplish this task, the images need to first be registered and then combined (fused) to create a single image. In this paper, we discuss the options for performing such fusion in the context of multimodal breast imaging. Additionally, a novel spline-based dynamic range technique is presented in detail. It has the advantage of obtaining a high level of contrast in the intensity range of interest without discarding the intensity information outside of this range while maintaining a user interface similar to the standard window/level windowing procedure.

Toward modeling the effects of regional material properties on the wall stress distribution of abdominal aortic aneurysms.

The overall geometry and different biomechanical parameters of an abdominal aortic aneurysm (AAA), contribute to its severity and risk of rupture, therefore they could be used to track its progression. Previous and ongoing research efforts have resorted to using uniform material properties to model the behavior of AAA. However, it has been recently illustrated that different regions of the AAA wall exhibit different behavior due to the effect of the biological activities in the metalloproteinase matrix that makes up the wall at the aneurysm site. In this work, we introduce a non-invasive patient-specific regional material property model to help us better understand and investigate the AAA wall stress distribution, peak wall stress (PWS) severity, and potential rupture risk. Our results indicate that the PWS and the overall wall stress distribution predicted using the proposed regional material property model, are higher than those predicted using the traditional homogeneous, hyper-elastic model (p <1.43E-07). Our results also show that to investigate AAA, the overall geometry, presence of intra-luminal thrombus (ILT), and loading condition in a patient specific manner may be critical for capturing the biomechanical complexity of AAAs.

(PEAK) WALL STRESS AS AN INDICATOR OF ABDOMINAL AORTIC ANEURYSM SEVERITY.

Abdominal aortic aneurysms, which consist of dilatations of the infra-renal aorta by at least 1.5 times of its normal diameter, are becoming a leading cause of death worldwide. Rupture often occurs unexpectedly, before a repair procedure is conducted. The AAA maximum diameter has been used as a clinical criterion to monitor AAA severity. However, assessment of AAA rupture risk requires knowledge of wall stress and wall strength at the potential rupture location. We conducted a study on 37 patient specific CT datasets to investigate the benefits of using peak wall stress instead of Dmax for AAA rupture severity. Correlation between PWS and 24 geometric indices and biomechanical factors was studied where eleven of them showed a statistically significant correlation with PWS. A Finite Element Analysis Rupture Index was used to conclude that the use of D max as a single predictor of AAA behavior and severity may be insufficient based on our patient population with a Dmax smaller than the 5.5 cm, clinically recommended repair threshold.

Noninvasive Quantitative Imaging of Collagen Microstructure in Three-Dimensional Hydrogels Using High-Frequency Ultrasound.

Collagen I is widely used as a natural component of biomaterials for both tissue engineering and regenerative medicine applications. The physical and biological properties of fibrillar collagens are strongly tied to variations in collagen fiber microstructure. The goal of this study was to develop the use of high-frequency quantitative ultrasound to assess collagen microstructure within three-dimensional (3D) hydrogels noninvasively and nondestructively. The integrated backscatter coefficient (IBC) was employed as a quantitative ultrasound parameter to detect, image, and quantify spatial variations in collagen fiber density and diameter. Collagen fiber microstructure was varied by fabricating hydrogels with different collagen concentrations or polymerization temperatures. IBC values were computed from measurements of the backscattered radio-frequency ultrasound signals collected using a single-element transducer (38-MHz center frequency, 13-47 MHz bandwidth). The IBC increased linearly with increasing collagen concentration and decreasing polymerization temperature. Parametric 3D images of the IBC were generated to visualize and quantify regional variations in collagen microstructure throughout the volume of hydrogels fabricated in standard tissue culture plates. IBC parametric images of corresponding cell-embedded collagen gels showed cell accumulation within regions having elevated collagen IBC values. The capability of this ultrasound technique to noninvasively detect and quantify spatial differences in collagen microstructure offers a valuable tool to monitor the structural properties of collagen scaffolds during fabrication, to detect functional differences in collagen microstructure, and to guide fundamental research on the interactions of cells and collagen matrices.

Estimating cell concentration in three-dimensional engineered tissues using high frequency quantitative ultrasound.

Histology and biochemical assays are standard techniques for estimating cell concentration in engineered tissues. However, these techniques are destructive and cannot be used for longitudinal monitoring of engineered tissues during fabrication processes. The goal of this study was to develop high-frequency quantitative ultrasound techniques to nondestructively estimate cell concentration in three-dimensional (3-D) engineered tissue constructs. High-frequency ultrasound backscatter measurements were obtained from cell-embedded, 3-D agarose hydrogels. Two broadband single-element transducers (center frequencies of 30 and 38 MHz) were employed over the frequency range of 13-47 MHz. Agarose gels with cell concentrations ranging from 1 × 10(4) to 1 × 10(6) cells mL(-1) were investigated. The integrated backscatter coefficient (IBC), a quantitative ultrasound spectral parameter, was calculated and used to estimate cell concentration. Accuracy and precision of this technique were analyzed by calculating the percent error and coefficient of variation of cell concentration estimates. The IBC increased linearly with increasing cell concentration. Axial and lateral dimensions of regions of interest that resulted in errors of less than 20% were determined. Images of cell concentration estimates were employed to visualize quantitatively regional differences in cell concentrations. This ultrasound technique provides the capability to rapidly quantify cell concentration within 3-D tissue constructs noninvasively and nondestructively.

Ultrasound imaging and characterization of biofilms based on wavelet de-noised radiofrequency data.

The ability to non-invasively image and characterize bacterial biofilms in children during nasopharyngeal colonization with potential otopathogens and during acute otitis media would represent a significant advance. We sought to determine if quantitative high-frequency ultrasound techniques could be used to achieve that goal. Systematic time studies of bacterial biofilm formation were performed on three preparations of an isolated Haemophilus influenzae (NTHi) strain, a Streptococcus pneumoniae (Sp) strain and a combination of H. influenzae and S. pneumoniae (NTHi + Sp) in an in vitro environment. The process of characterization included conditioning of the acquired radiofrequency data obtained with a 15-MHz focused, piston transducer by using a seven-level wavelet decomposition scheme to de-noise the individual A-lines acquired. All subsequent spectral parameter estimations were done on the wavelet de-noised radiofrequency data. Various spectral parameters-peak frequency shift, bandwidth reduction and integrated backscatter coefficient-were recorded. These parameters were successfully used to map the progression of the biofilms in time and to differentiate between single- and multiple-species biofilms. Results were compared with those for confocal microscopy and theoretical evaluation of form factor. We conclude that high-frequency ultrasound may prove a useful modality to detect and characterize bacterial biofilms in humans as they form on tissues and plastic materials.

Execution of the SimSET Monte Carlo PET/SPECT simulator in the condor distributed computing environment.

SimSET is a package for simulation of emission tomography data sets. Condor is a popular distributed computing environment. Simple C/C++ applications and shell scripts are presented which allow the execution of SimSET on the Condor environment. This is accomplished without any modification to SimSET by executing multiple instances and using its combinebin utility. This enables research facilities without dedicated parallel computing systems to utilize the idle cycles of desktop workstations to greatly reduce the run times of their SimSET simulations. The necessary steps to implement this approach in other environments are presented along with sample results.