Quantitative Assessment of Breast-Tumor Stiffness Using Shear-Wave Elastography Histograms.

Purpose: Shear-wave elastography (SWE) measures tissue elasticity using ultrasound waves. This study proposes a histogram-based SWE analysis to improve breast malignancy detection. Methods: N = 22/32 (patients/tumors) benign and n = 51/64 malignant breast tumors with histological ground truth. Colored SWE heatmaps were adjusted to a 0−180 kPa scale. Normalized, 250-binned RGB histograms were used as image descriptors based on skewness and area under curve (AUC). The histogram method was compared to conventional SWE metrics, such as (1) the qualitative 5-point scale classification and (2) average stiffness (SWEavg)/maximal tumor stiffness (SWEmax) within the tumor B-mode boundaries. Results: The SWEavg and SWEmax did not discriminate malignant lesions in this database, p > 0.05, rank sum test. RGB histograms, however, differed between malignant and benign tumors, p < 0.001, Kolmogorov−Smirnoff test. The AUC analysis of histograms revealed the reduction of soft-tissue components as a significant SWE biomarker (p = 0.03, rank sum). The diagnostic accuracy of the suggested method is still low (Se = 0.30 for Se = 0.90) and a subject for improvement in future studies. Conclusions: Histogram-based SWE quantitation improved the diagnostic accuracy for malignancy compared to conventional average SWE metrics. The sensitivity is a subject for improvement in future studies.

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth.

BACKGROUND: Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. AIM/OBJECTIVE: To assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. METHODS: The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. RESULTS: The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). CONCLUSION: The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis.

Non-Parametric Bayesian Registration (NParBR) of Body Tumors in DCE-MRI Data.

The identification of tumors in the internal organs of chest, abdomen, and pelvis anatomic regions can be performed with the analysis of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) data. The contrast agent is accumulated differently by pathologic and healthy tissues and that results in a temporally varying contrast in an image series. The internal organs are also subject to potentially extensive movements mainly due to breathing, heart beat, and peristalsis. This contributes to making the analysis of DCE-MRI datasets challenging as well as time consuming. To address this problem we propose a novel pairwise non-rigid registration method with a Non-Parametric Bayesian Registration (NParBR) formulation. The NParBR method uses a Bayesian formulation that assumes a model for the effect of the distortion on the joint intensity statistics, a non-parametric prior for the restored statistics, and also applies a spatial regularization for the estimated registration with Gaussian filtering. A minimally biased intra-dataset atlas is computed for each dataset and used as reference for the registration of the time series. The time series registration method has been tested with 20 datasets of liver, lungs, intestines, and prostate. It has been compared to the B-Splines and to the SyN methods with results that demonstrate that the proposed method improves both accuracy and efficiency.

Bi-modal Non-rigid Registration of Brain MRI Data with Deconvolution of Joint Statistics.

A brain MRI protocol typically includes several imaging contrasts that can provide complementary information by highlighting different tissue properties. The acquired datasets often need to be co-registered or placed in a standard anatomic space before any further processing. Current registration methods particularly for multicontrast data are computationally very intensive, their resolution is lower than that of the images, and their distance metric and its optimization can be limiting. In this work a novel and effective non-rigid registration method is proposed that is based on the restoration of the joint statistics of pairs of such images. The registration is performed with the deconvolution of the joint statistics with an adaptive Wiener filter. The deconvolved statistics are forced back to the spatial domain to estimate a preliminary registration. The spatial transformation is also regularized with Gaussian spatial smoothing. The registration method has been compared with the B-Splines method implemented in 3DSlicer and with the SyN method implemented in the ANTs toolkit. The validation has been performed with a simulated Shepp-Logan phantom, a BrainWeb phantom, the real data of the NIREP database, and real multi-contrast datasets of healthy volunteers. The proposed method has shown improved comparative accuracy as well as analytical efficiency.

Registration of abdominal tumor DCE-MRI data based on deconvolution of joint statistics.

The analysis of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) data of body tumors presents several challenges. The accumulation of contrast agent in tissues results in a temporally varying contrast in an image series. At the same time, the body regions are subject to potentially extensive motion mainly due to breathing, heart beat, and peristalsis. This complicates any further automated analysis of a DCE-MRI time series such as for tumor lesion segmentation and volumetry. To address this problem we propose a novel effective non-rigid registration method based on the restoration of the joint statistics of pairs of images in the time series. Every image in the time series is registered to a reference one from the contrast enhanced phase. The pairwise registration is performed with deconvolution of the joint statistics, forcing the results back to the spatial domain and regularizing them with Gaussian spatial smoothing. The registration method has been validated with both a simulated phantom as well as real datasets with improved results for both its accuracy and efficiency.

Joint restoration of bi-contrast MRI data for spatial intensity non-uniformities.

The reconstruction of MRI data assumes a uniform radiofrequency field. However, in practice the radio-frequency field is inhomogeneous and leads to non-biological intensity non-uniformities across an image. This artifact can complicate further automated analysis of the data. In general, an acquisition protocol provides images of the same anatomic region with multiple contrasts representing similar underlying information, but suffering from different intensity non-uniformities. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each of the two images as well as in their joint-co-occurrence statistics is modeled and used for their restoration with Wiener filtering. Several regularity constrains for the anatomy and for the non-uniformity are also imposed. Moreover, the method considers an inevitable difference between the signal regions of the two images. The joint treatment of the images can improve the accuracy and the efficiency of the restoration as well as decrease the requirements for additional calibration scans. The effectiveness of the method has been demonstrated extensively with both phantom and real brain anatomic data as well as with real DIXON pairs of fat and water abdominal data.

A comparison of different automated methods for the detection of white matter lesions in MRI data.

White matter hyperintensities (WMH) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMH labour intensive and prone to subjective bias. This study compares fully automated supervised detection methods that learn to identify WMH from manual examples against unsupervised approaches on the combination of FLAIR and T1 weighted images. Data were collected from ten subjects with mild cognitive impairment and another set of ten individuals who fulfilled diagnostic criteria for dementia. Data were split into balanced groups to create a training set used to optimize the different methods. Manual outlining served as gold standard to evaluate performance of the automated methods that identified each voxel either as intact or as part of a WMH. Otsu's approach for multiple thresholds which is based only on voxel intensities of the FLAIR image produced a high number of false positives at grey matter boundaries. Performance on an independent test set was similarly disappointing when simply applying a threshold to the FLAIR that was found from training data. Among the supervised methods, precision-recall curves of support vector machines (SVM) indicated advantages over the performance achieved by K-nearest-neighbor classifiers (KNN). The curves indicated a clear benefit from optimizing the threshold of the SVM decision value and the voting rule of the KNN. Best performance was reached by selecting training voxels according to their distance to the lesion boundary and repeated training after replacing the feature vectors from those voxels that did not form support vectors of the SVM. The study demonstrates advantages of SVM for the problem of detecting WMH at least for studies that include only FLAIR and T1 weighted images. Various optimization strategies are discussed and compared against each other.

Volumetric analysis of MRI data monitoring the treatment of polycystic kidney disease in a mouse model.

OBJECT: The human condition autosomal dominant polycystic kidney disease (ADPKD) is characterized by the growth of cysts in the kidneys that increase renal volume and lead to kidney failure. Mice studies are performed for treatment development monitored with imaging. The analysis of the imaging data is typically manual, which is costly and potentially biased. This paper presents a reliable and reproducible method for the automated segmentation of polycystic mouse kidneys. MATERIALS AND METHODS: Treated and untreated mice have been imaged longitudinally with high field anatomic MRI. The region of interest (ROI) of the kidneys in the images is identified and restored for artifacts. It is then analyzed statistically and geometric models are estimated for each kidney. The statistical and geometric information are provided to the graph cuts algorithm that delineates the kidneys. RESULTS: The accuracy of the analysis has been demonstrated by showing consistency with results obtained with previous methods as well as by comparing with manual segmentations. CONCLUSION: The method developed can accelerate and improve the accuracy of kidney volumetry in preclinical treatment trials for ADPKD.

Characterization of white matter degeneration in elderly subjects by magnetic resonance diffusion and FLAIR imaging correlation.

Fluid attenuated inversion recovery (FLAIR) and diffusion tensor imaging (DTI) techniques have been widely used to evaluate white matter (WM) alterations associated with aging, dementia and cerebral vascular disease. The relationship between FLAIR detected WM lesions (WML) and DTI detected WM integrity changes, however, remains unclear. To investigate this association, voxelwise correlations between 4 Tesla DTI and FLAIR images from elderly subjects were performed by relating WML volume and intensity in FLAIR to fractional anisotropy (FA) and mean diffusivity (MD) in DTI. Significant DTI-FLAIR correlations were found in regions overlapping with the WML of moderate intensities in FLAIR. No significant correlations were detected in periventricular regions where the FLAIR intensities are particularly high. The findings are consistent with a transitional model for WM degeneration from normal WM to cerebrospinal fluid (CSF). The results show that the correlation between DTI and FLAIR disappears when the FLAIR intensity of WML reaches its maximum at a certain lesion severity, and that the correlations may remerge with reversed signs when the lesion severity is further increased. These results suggest that the different stages of WM degeneration in elderly subjects can be better characterized by regional DTI-FLAIR correlations than single modality alone.

Restoration of MRI data for intensity non-uniformities using local high order intensity statistics.

MRI at high magnetic fields (>3.0 T) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the "bias field". These lead to non-biological intensity non-uniformities across the image. They can complicate further image analysis such as registration and tissue segmentation. Existing methods for intensity uniformity restoration have been optimized for 1.5 T, but they are less effective for 3.0 T MRI, and not at all satisfactory for higher fields. Also, many of the existing restoration algorithms require a brain template or use a prior atlas, which can restrict their practicalities. In this study an effective intensity uniformity restoration algorithm has been developed based on non-parametric statistics of high order local intensity co-occurrences. These statistics are restored with a non-stationary Wiener filter. The algorithm also assumes a smooth non-uniformity and is stable. It does not require a prior atlas and is robust to variations in anatomy. In geriatric brain imaging it is robust to variations such as enlarged ventricles and low contrast to noise ratio. The co-occurrence statistics improve robustness to whole head images with pronounced non-uniformities present in high field acquisitions. Its significantly improved performance and lower time requirements have been demonstrated by comparing it to the very commonly used N3 algorithm on BrainWeb MR simulator images as well as on real 4 T human head images.

Analysis of MR images of mice in preclinical treatment monitoring of polycystic kidney disease.

A common cause of kidney failure is autosomal dominant polycystic kidney disease (ADPKD). It is characterized by the growth of cysts in the kidneys and hence the growth of the entire kidneys with eventual failure in most cases by age 50. No preventive treatment for this condition is available. Preclinical drug treatment studies use an in vivo mouse model of the condition. The analysis of mice imaging data for such studies typically requires extensive manual interaction, which is subjective and not reproducible. In this work both untreated and treated mice have been imaged with a high field, 9.4T, MRI animal scanner and a reliable algorithm for the automated segmentation of the mouse kidneys has been developed. The algorithm first detects the region of interest (ROI) in the image surrounding the kidneys. A parameterized geometric shape for a kidney is registered to the ROI of each kidney. The registered shapes are incorporated as priors to the graph cuts algorithm used to extract the kidneys. The accuracy of the automated segmentation has been demonstrated by comparing it with a manual segmentation. The processing results are also consistent with the literature for previous techniques.

Computational atlases of severity of white matter lesions in elderly subjects with MRI.

MRI of cerebral white matter may show regions of signal abnormalities. These changes may be associated with hypertension, inflammation, or ischemia, as well as altered brain function. The goal of this work has been to construct computational atlases of white matter lesions that represent both their severity as well as the frequency of their occurrence in a population to achieve a better classification of white matter disease. An atlas is computed with a pipeline that uses 4T FLAIR and 4T T1-weighted (T1w) brain images of a group of subjects. The processing steps include intensity correction, lesion extraction, intra-subject FLAIR to T1w rigid registration, and seamless replacement of lesions in T1w images with synthetic white matter texture. Subsequently, the T1w images and lesion images of different subjects are registered non-rigidly to the same space. The decrease in T1w intensities is used to obtain severity information. Atlases were constructed for two groups of subjects, elderly normal controls or with mild cognitive impairment, and subjects with cerebrovascular disease. The lesion severities of the two groups have a significant statistical difference with the severity in the atlas of cerebrovascular disease being higher.

Motion tracking of the outer tips of microtubules.

Microtubules are tubular biopolymers of the cytoplasm. They play numerous critical roles in a cell such as providing mechanical support and structural tracks for the anchoring and transport of chromosomes, organelles, and vesicles. They also form the microtubule assembly, which is critical for the coordination of mitosis and cell migration. The most dynamic part of the assembly are the microtubule outer, plus, tips located close to the cortex of the cell. Abnormal function of the assembly has been implicated in cell pathology such as neurodegenerative diseases and cancer. To date the study of the dynamics of the microtubule assembly is often performed qualitatively by visual inspection or quantitatively by manual annotation of the locations of the tips over time in an image sequence, which is very tedious. In this work we have developed a method to automatically track microtubule tips so as to enable a more extensive and higher throughput quantitative study of the microtubule assembly. Our approach first uses the entire image sequence to estimate the region in which a tip oscillates. In that region a tip feature is computed for all time and subsequently used to form the tip trajectory. Last, we evaluate our method with phantom as well as real data. The real data show fluorescently tagged living cells imaged with epifluorescent microscopy or confocal microscopy.

Restoration of MRI Data for Field Nonuniformities using High Order Neighborhood Statistics.

MRI at high magnetic fields (> 3.0 T ) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the "bias field". These lead to nonuniformity of image intensity, greatly complicating further analysis such as registration and segmentation. Existing methods for bias field correction are effective for 1.5 T or 3.0 T MRI, but are not completely satisfactory for higher field data. This paper develops an effective bias field correction for high field MRI based on the assumption that the nonuniformity is smoothly varying in space. Also, nonuniformity is quantified and unmixed using high order neighborhood statistics of intensity cooccurrences. They are computed within spherical windows of limited size over the entire image. The restoration is iterative and makes use of a novel stable stopping criterion that depends on the scaled entropy of the cooccurrence statistics, which is a non monotonic function of the iterations; the Shannon entropy of the cooccurrence statistics normalized to the effective dynamic range of the image. The algorithm restores whole head data, is robust to intense nonuniformities present in high field acquisitions, and is robust to variations in anatomy. This algorithm significantly improves bias field correction in comparison to N3 on phantom 1.5 T head data and high field 4 T human head data.

Segmentation and 3D reconstruction of microtubules in total internal reflection fluorescence microscopy (TIRFM).

The interaction of the microtubules with the cell cortex plays numerous critical roles in a cell. For instance, it directs vesicle delivery, and modulates membrane adhesions pivotal for cell movement as well as mitosis. Abnormal function of the microtubules is involved in cancer. An effective method to observe microtubule function adjacent to the cortex is TIRFM. To date most analysis of TIRFM images has been done by visual inspection and manual tracing. In this work we have developed a method to automatically process TIRFM images of microtubules so as to enable high throughput quantitative studies. The microtubules are extracted in terms of consecutive segments. The segments are described via Hamilton-Jacobi equations. Subsequently, the algorithm performs a limited reconstruction of the microtubules in 3D. Last, we evaluate our method with phantom as well as real TIRFM images of living cells.