Showing papers in "International Journal of Biomedical Imaging in 2012"

A review of indocyanine green fluorescent imaging in surgery

Abstract: The purpose of this paper is to give an overview of the recent surgical intraoperational applications of indocyanine green fluorescence imaging methods, the basics of the technology, and instrumentation used. Well over 200 papers describing this technique in clinical setting are reviewed. In addition to the surgical applications, other recent medical applications of ICG are briefly examined.

Quantifying optical microangiography images obtained from a spectral domain optical coherence tomography system

Abstract: The blood vessel morphology is known to correlate with several diseases, such as cancer, and is important for describing several tissue physiological processes, like angiogenesis. Therefore, a quantitative method for characterizing the angiography obtained from medical images would have several clinical applications. Optical microangiography (OMAG) is a method for obtaining three-dimensional images of blood vessels within a volume of tissue. In this study we propose to quantify OMAG images obtained with a spectral domain optical coherence tomography system. A technique for determining three measureable parameters (the fractal dimension, the vessel length fraction, and the vessel area density) is proposed and validated. Finally, the repeatability for acquiring OMAG images is determined, and a new method for analyzing small areas from these images is proposed.

Editorial: machine learning in medical imaging

Abstract: Medical imaging is becoming indispensable for patients' healthcare. Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation, and image database retrieval. With advances in medical imaging, new imaging modalities and methodologies such as cone-beam/multislice CT, 3D ultrasound imaging, tomosynthesis, diffusion-weighted magnetic resonance imaging (MRI), positron-emission tomography (PET)/CT, electrical impedance tomography, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Because of large variations and complexityit is generally difficult to derive analytic solutions or simple equations to represent objects such as lesions and anatomy in medical images. Therefore, tasks in medical imaging require “learning from examples” for accurate representation of data and prior knowledge. Because of its essential needs, machine learning in medical imaging is one of the most promising, growing fields. The main aim of this special issue is to help advance the scientific research within the broad field of machine learning in medical imaging. The special issue was planned in conjunction with the International Workshop on Machine Learning in Medical Imaging (MLMI 2010) [1], which was the first workshop on this topic, held at the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010) in September, 2010, in Beijing, China. This special issue is one in a series of special issues of journals on this topic [2]; it focuses on major trends and challenges in this area, and it presents work aimed at identifying new cutting-edge techniques and their use in medical imaging. The quality level of the submissions for this special issue was very high. A total of 17 papers were submitted to this issue in response to the call for papers. Based on a rigorous review process, 10 papers (59%) were accepted for publication in the special issue. The special issue starts by a review of studies on a class of machine-learning techniques, called pixel/voxel-based machine learning, in medical imaging by K. Suzuki. A series of medical imaging applications of machine-learning techniques are presented. A large variety of applications are well represented here, including organ modeling by D. Wang et al. and X. Qiao and Y.-W. Chen, brain function estimation by V. Michel et al., image reconstruction by H. Shouno et al., lesion classification by P. Wighton et al., modality classification by X.-H. Han and Y.-W. Chen, lesion segmentation by M. Zortea et al., organ segmentation by S. Alzubi et al., and visualization of molecular signals by F. Mattoli et al. Also, the issue covers various biomedical imaging modalities, including MRI by D. Wang et al., CT by X. Qiao and Y.-W. Chen and H. Shouno et al., functional MRI by V. Michel et al., dermoscopy by P. Wighton et al. and M. Zortea et al., scintigraphy by X.-H. Han and Y.-W. Chen, ultrasound imaging by X.-H. Han and Y.-W. Chen, radiography by X.-H. Han and Y.-W. Chen, MR angiography by S. Alzubi et al., and microscopy by F. Mattoli et al. as well as a variety of organs, including the kidneys by D. Wang et al., liver by X. Qiao and Y.-W. Chen, brain by V. Michel et al. and F. Mattoli et al., chest by S. Alzubi et al., skin by P. Wighton et al. and M. Zortea et al., and heart by F. Mattoli et al. Various machine-learning techniques were developed/used to solve the respective problems, including structured dictionary learning by D. Wang et al., generalized N-dimensional principal component analysis by X. Qiao et al., multiclass sparse Bayesian regression by V. Michel et al., Bayesian hyperparameter inference by H. Shouno et al., supervised learning of probabilistic models based on maximum aposteriori estimation and conditional random fields by P. Wighton et al., joint kernel equal contribution in support vector classification by X.-H. Han and Y.-W. Chen, and iterative hybrid classification by M. Zortea et al. We are grateful to all authors for their excellent contributions to this special issue and to all reviewers for their reviews and constructive suggestions. We hope that this special issue will inspire further ideas for creative research, advance the field of machine learning in medical imaging, and facilitate the translation of the research from bench to bedside. Kenji Suzuki Pingkun Yan Fei Wang Dinggang Shen

A prototype system for measuring microwave frequency reflections from the breast

Abstract: Microwave imaging of the breast is of interest for monitoring breast health, and approaches to active microwave imaging include tomography and radar-based methods. While the literature contains a growing body of work related to microwave breast imaging, there are only a few prototype systems that have been used to collect data from humans. In this paper, a prototype system for monostatic radar-based imaging that has been used in an initial study measuring reflections from volunteers is discussed. The performance of the system is explored by examining the mechanical positioning of sensor, as well as microwave measurement sensitivity. To gain insight into the measurement of reflected signals, simulations and measurements of a simple phantom are compared and discussed in relation to system sensitivity. Finally, a successful scan of a volunteer is described.

Erratum to arterial spin labeling (ASL) fMRI: advantages, theoretical constrains and experimental challenges in neurosciences

Abstract: Cerebral blood flow (CBF) is a well-established correlate of brain function and therefore an essential parameter for studying the brain at both normal and diseased states. Arterial spin labeling (ASL) is a noninvasive fMRI technique that uses arterial water as an endogenous tracer to measure CBF. ASL provides reliable absolute quantification of CBF with higher spatial and temporal resolution than other techniques. And yet, the routine application of ASL has been somewhat limited. In this review, we start by highlighting theoretical complexities and technical challenges of ASL fMRI for basic and clinical research. While underscoring the main advantages of ASL versus other techniques such as BOLD, we also expound on inherent challenges and confounds in ASL perfusion imaging. In closing, we expound on several exciting developments in the field that we believe will make ASL reach its full potential in neuroscience research.

Bone dielectric property variation as a function of mineralization at microwave frequencies

Abstract: A critical need exists for new imaging tools to more accurately characterize bone quality beyond the conventional modalities of dual energy X-ray absorptiometry (DXA), ultrasound speed of sound, and broadband attenuation measurements. In this paper we investigate the microwave dielectric properties of ex vivo trabecular bone with respect to bulk density measures. We exploit a variation in our tomographic imaging system in conjunction with a new soft prior regularization scheme that allows us to accurately recover the dielectric properties of small, regularly shaped and previously spatially defined volumes. We studied six excised porcine bone samples from which we extracted cylindrically shaped trabecular specimens from the femoral heads and carefully demarrowed each preparation. The samples were subsequently treated in an acid bath to incrementally remove volumes of hydroxyapatite, and we tested them with both the microwave measurement system and a micro-CT scanner. The measurements were performed at five density levels for each sample. The results show a strong correlation between both the permittivity and conductivity and bone volume fraction and suggest that microwave imaging may be a good candidate for evaluating overall bone health.

Pixel-based machine learning in medical imaging

Abstract: Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computeraided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or nonlesions) based on input features (e.g., contrast and circularity) obtained from segmented object candidates. Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which use pixel/voxel values in images directly instead of features calculated from segmented objects as input information; thus, feature calculation or segmentation is not required. Because the PML can avoid errors caused by inaccurate feature calculation and segmentation which often occur for subtle or complex objects, the performance of the PML can potentially be higher for such objects than that of common classifiers (i.e., feature-based MLs). In this paper, PMLs are surveyed to make clear (a) classes of PMLs, (b) similarities and differences within (among) different PMLs and those between PMLs and feature-based MLs, (c) advantages and limitations of PMLs, and (d) their applications in medical imaging.

Fracture detection in traumatic pelvic CT images

Abstract: Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately.

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods.

Abstract: Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images.

Microwave breast imaging system prototype with integrated numerical characterization

Abstract: The increasing number of experimental microwave breast imaging systems and the need to properly model them have motivated our development of an integrated numerical characterization technique. We use Ansoft HFSS and a formalism we developed previously to numerically characterize an S-parameter- based breast imaging system and link it to an inverse scattering algorithm. We show successful reconstructions of simple test objects using synthetic and experimental data. We demonstrate the sensitivity of image reconstructions to knowledge of the background dielectric properties and show the limits of the current model.

Tissue-based MRI intensity standardization: application to multicentric datasets

Abstract: Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image.

Measurement and analysis of microwave frequency signals transmitted through the breast

Abstract: Microwave approaches to breast imaging include the measurement of signals transmitted through and reflected from the breast. Prototype systems typically feature sensors separated from the breast, resulting in measurements that include the effects of the environment and system. To gain insight into transmission of microwave signals through the breast, a system that places sensors in direct contact with the breast is proposed. The system also includes a lossy immersion medium that enables measurement of the signal passing through the breast while significantly attenuating signals traveling along other paths. Collecting measurements at different separations between sensors also provides the opportunity to estimate the average electrical properties of the breast tissues. After validation through simulations and measurements, a study of 10 volunteers was performed. Results indicate symmetry between the right and left breast and demonstrate differences in attenuation, maximum frequency for reliable measurement, and average properties that likely relate to variations in breast composition.

Quantification of lung damage in an elastase-induced mouse model of emphysema

Abstract: To define the sensitivity of microcomputed tomography- (micro-CT-) derived descriptors for the quantification of lung damage caused by elastase instillation. Materials and Methods. The lungs of 30 elastase treated and 30 control A/J mice were analyzed 1, 6, 12, and 24 hours and 7 and 17 days after elastase instillation using (i) breath-hold-gated micro-CT, (ii) pulmonary function tests (PFTs), (iii) RT-PCR for RNA cytokine expression, and (iv) histomorphometry. For the latter, an automatic, parallel software toolset was implemented that computes the airspace enlargement descriptors: mean linear intercept (Lm) and weighted means of airspace diameters (D0, D1, and D2). A Support Vector Classifier was trained and tested based on three nonhistological descriptors using D2 as ground truth. Results. D2 detected statistically significant differences (P < 0.01) between the groups at all time points. Furthermore, D2 at 1 hour (24 hours) was significantly lower (P < 0.01) than D2 at 24 hours (7 days). The classifier trained on the micro-CT-derived descriptors achieves an area under the curve (AUC) of 0.95 well above the others (PFTS AUC = 0.71; cytokine AUC = 0.88). Conclusion. Micro-CT-derived descriptors are more sensitive than the other methods compared, to detect in vivo early signs of the disease.

MRI texture analysis in multiple sclerosis

Abstract: Multiple sclerosis (MS) is a complicated disease characterized by heterogeneous pathology that varies across individuals. Accurate identification and quantification of pathological changes may facilitate a better understanding of disease pathogenesis and progression and help identify novel therapies for MS patients. Texture analysis evaluates interpixel relationships that generate characteristic organizational patterns in an image, many of which are beyond the ability of visual perception. Given its promise detecting subtle structural alterations texture analysis may be an attractive means to evaluate disease activity and evolution. It may also become a new tool to assess therapeutic efficacy if technique issues are resolved and pathological correlates are further confirmed. This paper describes the concept, strategies, and considerations of MRI texture analysis; summarizes applications of texture analysis in MS as a measure of tissue integrity and its clinical relevance; then discusses potentially future directions of texture analysis in MS.

Surface wave multipath signals in near-field microwave imaging

Abstract: Microwave imaging techniques are prone to signal corruption from unwanted multipath signals. Near-field systems are especially vulnerable because signals can scatter and reflect from structural objects within or on the boundary of the imaging zone. These issues are further exacerbated when surface waves are generated with the potential of propagating along the transmitting and receiving antenna feed lines and other low-loss paths. In this paper, we analyze the contributions ofmulti-path signals arising from surface wave effects. Specifically, experiments were conducted with a near-field microwave imaging array positioned at variable heights from the floor of a coupling fluid tank. Antenna arrays with different feed line lengths in the fluid were also evaluated. The results show that surface waves corrupt the received signals over the longest transmission distances across the measurement array. However, the surface wave effects can be eliminated provided the feed line lengths are sufficiently long independently of the distance of the transmitting/receiving antenna tips from the imaging tank floor. Theoretical predictions confirm the experimental observations.

Heterogeneous breast phantom development for microwave imaging using regression models

Abstract: As new algorithms for microwave imaging emerge, it is important to have standard accurate benchmarking tests. Currently, most researchers use homogeneous phantoms for testing new algorithms. These simple structures lack the heterogeneity of the dielectric properties of human tissue and are inadequate for testing these algorithms for medical imaging. To adequately test breast microwave imaging algorithms, the phantom has to resemble different breast tissues physically and in terms of dielectric properties. We propose a systematic approach in designing phantoms that not only have dielectric properties close to breast tissues but also can be easily shaped to realistic physicalmodels. The approach is based on regression model tomatch phantom's dielectric properties with the breast tissue dielectric properties found in Lazebnik et al. (2007). However, the methodology proposed here can be used to create phantoms for any tissue type as long as ex vivo, in vitro, or in vivo tissue dielectric properties are measured and available. Therefore, using this method, accurate benchmarking phantoms for testing emerging microwave imaging algorithms can be developed.

3D rigid registration of intraoperative ultrasound and preoperative MR brain images based on hyperechogenic structures

Abstract: The registration of intraoperative ultrasound (US) images with preoperative magnetic resonance (MR) images is a challenging problem due to the difference of information contained in each image modality. To overcome this difficulty, we introduce a new probabilistic function based on the matching of cerebral hyperechogenic structures. In brain imaging, these structures are the liquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hyperechogenic. The registration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in both modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation framework is based on (i) visual assessment, (ii) manual expert estimations, and (iii) a robustness study. Results show that the proposed method (i) is visually efficient, (ii) produces no statistically different registration accuracy compared to manual-based expert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with intraoperative use.

Landmark optimization using local curvature for point-based nonlinear rodent brain image registration

Abstract: Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (n = 5 each). In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected.Normalizedmutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (P < 0.05) in registration accuracy by landmark optimization in most data sets and trends towards improvement (P < 0.1) in others as compared to manual landmark selection.

Three-dimensional near-field microwave holography for tissue imaging

Abstract: This paper reports the progress toward a fast and reliable microwave imaging setup for tissue imaging exploiting near-field holographic reconstruction. The setup consists of two wideband TEM horn antennas aligned along each other's boresight and performing a rectangular aperture raster scan. The tissue sensing is performed without coupling liquids. At each scanning position, wideband data is acquired. Then, novel holographic imaging algorithms are implemented to provide three-dimensional images of the inspected domain. In these new algorithms, the required incident field and Green's function are obtained from numerical simulations. They replace the plane (or spherical) wave assumption in the previous holographic methods and enable accurate nearfield imaging results. Here, we prove that both the incident field and Green's function can be obtained from a single numerical simulation. This eliminates the need for optimization-based deblurring which was previously employed to remove the effect of realistic non-point-wise antennas.

Improving intensity-based lung CT registration accuracy utilizing vascular information

Abstract: Accurate pulmonary image registration is a challenging problem when the lungs have a deformation with large distance. In this work, we present a nonrigid volumetric registration algorithm to track lung motion between a pair of intrasubject CT images acquired at different inflation levels and introduce a new vesselness similarity cost that improves intensity-only registration. Volumetric CT datasets from six human subjects were used in this study. The performance of four intensity-only registration algorithms was compared with and without adding the vesselness similarity cost function. Matching accuracy was evaluated using landmarks, vessel tree, and fissure planes. The Jacobian determinant of the transformation was used to reveal the deformation pattern of local parenchymal tissue. The average matching error for intensity-only registration methods was on the order of 1mm at landmarks and 1.5mm on fissure planes. After adding the vesselness preserving cost function, the landmark and fissure positioning errors decreased approximately by 25% and 30%, respectively. The vesselness cost function effectively helped improve the registration accuracy in regions near thoracic cage and near the diaphragm for all the intensity-only registration algorithms tested and also helped produce more consistent and more reliable patterns of regional tissue deformation.

Automated lobe-based airway labeling

Abstract: Regional quantitative analysis of airway morphological abnormalities is of great interest in lung disease investigation. Considering that pulmonary lobes are relatively independent functional unit, we develop and test a novel and efficient computerized scheme in this study to automatically and robustly classify the airways into different categories in terms of pulmonary lobe. Given an airway tree, which could be obtained using any available airway segmentation scheme, the developed approach consists of four basic steps: (1) airway skeletonization or centerline extraction, (2) individual airway branch identification, (3) initial rule-based airway classification/labeling, and (4) self-correction of labeling errors. In order to assess the performance of this approach, we applied it to a dataset consisting of 300 chest CT examinations in a batch manner and asked an image analyst to subjectively examine the labeled results. Our preliminary experiment showed that the labeling accuracy for the right upper lobe, the right middle lobe, the right lower lobe, the left upper lobe, and the left lower lobe is 100%, 99.3%, 99.3%, 100%, and 100%, respectively. Among these, only two cases are incorrectly labeled due to the failures in airway detection. It takes around 2 minutes to label an airway tree using this algorithm.

Compressed sensing photoacoustic imaging based on fast alternating direction algorithm

Abstract: Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications.

A new GLLD operator for mass detection in digital mammograms

Abstract: During the last decade, several works have dealt with computer automatic diagnosis (CAD) of masses in digital mammograms. Generally, the main difficulty remains the detection of masses. This work proposes an efficient methodology for mass detection based on a new local feature extraction. Local binary pattern (LBP) operator and its variants proposed by Ojala are a powerful tool for textures classification. However, it has been proved that such operators are not able to model at their own texture masses. We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM). The area under the curve of the corresponding approach has been found to be Az = 0.95 for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.

DCE-MRI and DWI integration for breast lesions assessment and heterogeneity quantification

Abstract: In order to better predict and follow treatment responses in cancer patients, there is growing interest in noninvasively characterizing tumor heterogeneity based on MR images possessing different contrast and quantitative information. This requires mechanisms for integrating such data and reducing the data dimensionality to levels amenable to interpretation by human readers. Here we propose a two-step pipeline for integrating diffusion and perfusion MRI that we demonstrate in the quantification of breast lesion heterogeneity. First, the images acquired with the two modalities are aligned using an intermodal registration. Dissimilarity-based clustering is then performed exploiting the information coming from both modalities. To this end an ad hoc distance metric is developed and tested for tuning the weighting for the two modalities. The distributions of the diffusion parameter values in subregions identified by the algorithm are extracted and compared through nonparametric testing for posterior evaluation of the tissue heterogeneity. Results show that the joint exploitation of the information brought by DCE and DWI leads to consistent results accounting for both perfusion and microstructural information yielding a greater refinement of the segmentation than the separate processing of the two modalities, consistent with that drawn manually by a radiologist with access to the same data.

Serial FEM/XFEM-based update of preoperative brain images using intraoperative MRI

Abstract: Current neuronavigation systems cannot adapt to changing intraoperative conditions over time. To overcome this limitation, we present an experimental end-to-end system capable of updating 3D preoperative images in the presence of brain shift and successive resections. The heart of our system is a nonrigid registration technique using a biomechanical model, driven by the deformations of key surfaces tracked in successive intraoperative images. The biomechanical model is deformed using FEM or XFEM, depending on the type of deformation under consideration, namely, brain shift or resection. We describe the operation of our system on two patient cases, each comprising five intraoperative MR images, and we demonstrate that our approach significantly improves the alignment of nonrigidly registered images.

Ex vivo fluorescence molecular tomography of the spine

Abstract: We investigated the potential of fluorescence molecular tomography to image ex vivo samples collected from a large animal model, in this case, a dog spine. Wide-field time-gated fluorescence tomography was employed to assess the impact of multiview acquisition, data type, and intrinsic optical properties on the localization and quantification accuracy in imaging a fluorescent inclusion in the intervertebral disk. As expected, the TG data sets, when combining early and late gates, provide significantly better performances than the CW data sets in terms of localization and quantification. Moreover, the use of multiview imaging protocols led to more accurate localization. Additionally, the incorporation of the heterogeneous nature of the tissue in the model to compute the Jacobians led to improved imaging performances. This preliminary imaging study provides a proof of concept of the feasibility of quantitatively imaging complex ex vivo samples nondestructively and with short acquisition times. This work is the first step towards employing optical molecular imaging of the spine to detect and characterize disc degeneration based on targeted fluorescent probes.

Label Fusion Strategy Selection

Abstract: Label fusion is used in medical image segmentation to combine several different labels of the same entity into a single discrete label, potentially more accurate, with respect to the exact, sought segmentation, than the best input element. Using simulated data, we compared three existing label fusion techniques—STAPLE, Voting, and Shape-Based Averaging (SBA)—and observed that none could be considered superior depending on the dissimilarity between the input elements. We thus developed an empirical, hybrid technique called SVS, which selects the most appropriate technique to apply based on this dissimilarity. We evaluated the label fusion strategies on two- and three-dimensional simulated data and showed that SVS is superior to any of the three existing methods examined. On real data, we used SVS to perform fusions of 10 segmentations of the hippocampus and amygdala in 78 subjects from the ICBM dataset. SVS selected SBA in almost all cases, which was the most appropriate method overall.

Microwave imaging and emerging applications

Abstract: Active microwave imaging for biomedical has been proposed, studied, and implemented to varying degrees by a plethora of worldwide institutions for the last 30 years. Much of the interest has been based on the fact that tissue dielectric properties embody important information about physiological state and function. Early efforts concentrated on techniques involving classic techniques such as diffraction imaging and the Born and Rytov approximations which ultimately proved too limited for the high degree of field scattering involved with electromagnetic fields. At a time of rapidly increasing computational power and speed, the electromagnetics field quickly adopted these capabilities for developing improved forward and inverse modeling tools. This was especially convenient in that hardware implementations could be rapidly simulated and validated without the considerable time and expense of fabrication and testing. The evolution of numerical capabilities has been considerable to the point where fully 3D software packages, both customized and commercial, can represent complex geometries in reasonable time frames with exquisite resolution. Microwave imaging will always be a hybrid between numerical modeling and hardware. The two principle approaches today involve some form of tomography or 3D inversion and radar-based techniques. The former primarily utilizes transmission data, in combination with an inverse algorithm, produces maps of the interrogated dielectric properties. The algorithms fall under the category of classical parameter estimation problems and stem from a basic mathematical statement to minimize the differences between the measured and computed fields. A wide range of optimization problems have been implemented including, but not limited to, Gauss-Newton iterative schemes, genetic algorithms and backprojection techniques. These have often been 2D and 3D implementations and involved single and multifrequency modes. While this discipline has been dominated by simulation experiments, several efforts have expanded to at least the level of phantom validation investigations and even more advanced clinical implementations for breast cancer imaging. As with any technology, these realizations have involved important design trade-offs with respect to signal attenuation and detection strength, operating frequency, resolution, antenna selection, mechanical versus electrical array scanning, and many others. Early results have been encouraging and in an important development it is clear that the technique can recover accurate property maps that, as earlier hypothesized, can discern objects at levels well below the λ/2, Rayleigh criteria. Radar approaches have also progressed substantially over the last decade or two. While much of the earlier work remained in the simulation realm, more recent efforts have included credible translation to experimental and even clinical implementations. The different approaches have involved either forward or backscattered measurement data and have explored air-coupled, liquid-coupled and contacting antenna techniques. This concept has been applied initially for the detection of high-contrast targets such as breast cancer with more recent efforts exploring approaches to more broadly characterize abnormalities. The classical challenges in this case involve designing broadband antennas, beam coverage of the entire target and generating sufficient measurement signal strength. More specific issues relating to breast cancer imaging involve the high degree of scattering at the skin. Early clinical efforts have produced refined and interesting breast images, especially for lower density breasts. The application of microwave imaging to the breast cancer detection problem has singularly motivated the wide-ranging worldwide efforts in this technology. Breast cancer is an important worldwide health problem afflicting primarily woman. Treatment outcomes have been shown to be progressively more positive when the cancers are detected earlier. While conventional technologies such as X-ray mammography and MR do a good job of detecting and characterizing tumors, their effectiveness is particularly limited for more dense breasts. This has spurred the drive for investigating alternative approaches. As alluded to before, tissue dielectric properties can span a wide range of permittivity and conductivity values from very low values for fattier tissue to much higher values for high-water content tissues and a range of intermediate values depending on the composition. Initially the excitement for microwave breast imaging was based on the assumption that there was a very high property contrast between tumors and normal breast tissue and that these property differences would enable detection on a sub-centimeter scale. More recent studies have shown that the contrast is not necessarily as high and that the property variations are much more complex depending on the internal fibroglandular structure. However, this more nuanced understanding of the tissue properties provides real opportunities in the breast imaging area as the technology expands to roles in both breast cancer diagnosis and therapy monitoring. In fact, this opens up the opportunity for relating this recent data back to earlier, extensive studies by Ken Foster into the relationships between free and bound water with respect to tissue properties. These complex mechanisms have been hypothesized as prognostic indicators for cancer and may pave the way for dielectric properties becoming important biological markers. As mentioned earlier, the earliest efforts in this arena were dominated by numerical modeling efforts. However, there is a realization within the community that for this technology to continue to be relevant, there needs to be concerted efforts to translate the various concepts into the clinic. This special issue is a step in that direction and demonstrates a maturing of the field. While there are some simulation studies within this collection, a good number of them deal directly with implementation challenges. Several in the radar area deal directly with acquiring in vivo measurements of a human breast. Another from the tomography techniques assesses the impact of surface waves to multipath contributions which can be an especially daunting problem in any radar, imaging, or communication system. These are healthy signs that the group understands that for it to continue to be relevant, it needs to steadily push these ideas into the clinic for real world validation. Simulations will always play an important role in this area, but the synergism with hardware implementations must be more heavily emphasized. Beyond these classical motivations, this issue also highlights important points for the field as it moves forward. For instance, there is always room to explore new imaging techniques such as holographic approaches which may be particularly well suiting for the breast imaging situation. There are also clearly ways to improve on the overall approaches, especially when these methods can be combined with existing technologies such as proposed by the paper dealing with utilizing mammography in conjunction with radar approaches. Finally, while breast cancer imaging has dominated the landscape of microwave imaging for over a decade, we should not be blinded by other important health and commercial opportunities. The studies on bone dielectric property variations with respect to mineral density set the stage for applying microwave imaging in the area of osteoporosis detection and bone health monitoring which is becoming a major health issue with our rapidly aging population. This is only one of a range of important health issues where microwave imaging could make a substantial impact. Paul M. Meaney

Detection and quantification of fluorescent cell clusters in cryo-imaging

Abstract: We developed and evaluated an algorithm for enumerating fluorescently labeled cells (e.g., stem and cancer cells) in mouse-sized, microscopic-resolution, cryo-image volumes. Fluorescent cell clusters were detected, segmented, and then fit with a model which incorporated a priori information about cell size, shape, and intensity. The robust algorithm performed well in phantomand tissue imaging tests, including accurate (<2% error) counting of cells inmouse. Preliminary experiments demonstrate that cryo-imaging and software can uniquely analyze delivery, homing to an organ and tissue distribution of stem cell therapeutics.

Modeling airflow using subject-specific 4DCT-based deformable volumetric lung models

Abstract: Lung radiotherapy is greatly benefitted when the tumor motion caused by breathing can be modeled. The aim of this paper is to present the importance of using anisotropic and subject-specific tissue elasticity for simulating the airflow inside the lungs. A computational-fluid-dynamics (CFD) based approach is presented to simulate airflow inside a subject-specific deformable lung for modeling lung tumor motion and the motion of the surrounding tissues during radiotherapy. A flow-structure interaction technique is employed that simultaneously models airflow and lung deformation. The lung is modeled as a poroelastic medium with subject-specific anisotropic poroelastic properties on a geometry, which was reconstructed from four-dimensional computed tomography (4DCT) scan datasets of humans with lung cancer. The results include the 3D anisotropic lung deformation for known airflow pattern inside the lungs. The effects of anisotropy are also presented on both the spatiotemporal volumetric lung displacement and the regional lung hysteresis.