Proc. SPIE. 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography
KEYWORDS: Cancer, Breast cancer, Tissues, Ultrasonography, Image segmentation, Pathology, Physics, Computer simulations, Data acquisition, Finite element methods, Prostate, Prostate cancer, In vivo imaging
Temporal enhanced ultrasound (TeUS) is an imaging approach where a sequence of temporal ultrasound data is acquired and analyzed for tissue typing. Previously, in a series of in vivo and ex vivo studies we have demonstrated that, this approach is effective for detecting prostate and breast cancers. Evidences derived from our experiments suggest that both ultrasound-signal related factors such as induced heat and tissue-related factors such as the distribution and micro-vibration of scatterers lead to tissue typing information in TeUS. In this work, we simulate mechanical micro-vibrations of scatterers in tissue-mimicking phantoms that have various scatterer densities reflecting benign and cancerous tissue structures. Finite element modeling (FEM) is used for this purpose where the vertexes are scatterers representing cell nuclei. The initial positions of scatterers are determined by the distribution of nuclei segmented from actual digital histology scans of prostate cancer patients. Subsequently, we generate ultrasound images of the simulated tissue structure using the Field II package resulting in a temporal enhanced ultrasound. We demonstrate that the micro-vibrations of scatterers are captured by temporal ultrasound data and this information can be exploited for tissue typing.
The imaging biomarkers <i>EmphysemaPresence</i> and <i>NoduleSpiculation</i> are crucial inputs for most models aiming to predict the risk of indeterminate pulmonary nodules detected at CT screening. To increase reproducibility and to accelerate screening workflow it is desirable to assess these biomarkers automatically. Validation on NLST images indicates that standard histogram measures are not sufficient to assess <i>EmphysemaPresence</i> in screenees. However, automatic scoring of bulla-resembling low attenuation areas can achieve agreement with experts with close to 80% sensitivity and specificity. <i>NoduleSpiculation</i> can be automatically assessed with similar accuracy. We find a dedicated spiculi tracing score to slightly outperform generic combinations of texture features with classifiers.
Ultrasound imaging is an attractive modality for real-time image-guided interventions. Fusion of US imaging with a diagnostic imaging modality such as CT shows great potential in minimally invasive applications such as liver biopsy and ablation. However, significantly different representation of liver in US and CT turns this image fusion into a challenging task, in particular if some of the CT scans may be obtained without contrast agents. The liver surface, including the diaphragm immediately adjacent to it, typically appears as a hyper-echoic region in the ultrasound image if the proper imaging window and depth setting are used. The liver surface is also well visualized in both contrast and non-contrast CT scans, thus making the diaphragm or liver surface one of the few attractive common features for registration of US and non-contrast CT. We propose a fusion method based on point-to-volume registration of liver surface segmented in CT to a processed electromagnetically (EM) tracked US volume. In this approach, first, the US image is pre-processed in order to enhance the liver surface features. In addition, non-imaging information from the EM-tracking system is used to initialize and constrain the registration process. We tested our algorithm in comparison with a manually corrected vessel-based registration method using 8 pairs of tracked US and contrast CT volumes. The registration method was able to achieve an average deviation of 12.8mm from the ground truth measured as the root mean square Euclidean distance for control points distributed throughout the US volume. Our results show that if the US image acquisition is optimized for imaging of the diaphragm, high registration success rates are achievable.
We present a variational approach for segmenting bone structures in Computed Tomography (CT) images. We
introduce a novel functional on the space of image segmentations, and subsequently minimize this functional
through a gradient descent partial differential equation. The functional we propose provides a measure of
similarity of the intensity characteristics of the bone and tissue regions through a comparison of their cumulative
distribution functions; minimizing this similarity measure therefore yields the maximal separation between the two regions. We perform the minimization of our proposed functional using level set partial differential equations; in addition to numerical stability, this yields topology independence, which is especially useful in the context of CT bone segmentation where a bone region may consist of several disjoint pieces. Finally, we present an extensive validation of our method against expert manual segmentation on CT images of the wrist, ankle, foot, and pelvis.
Probabilistic maps are useful in functional neuroimaging research for anatomical labeling and for data analysis.
The degree to which a probability map can accurately estimate the location of the structure of interest in
a new individual depends on many factors, including the variability in the morphology of the structure of
interest over subjects, the registration (normalization procedure and template) applied to align the brains among
individuals and the registration used to map a new subject's dataset to the frame of the probabilistic map.
Here, we take Heschl's gyrus (HG) as our structure of interest, and explore the impact of different registration
methods on the accuracy with which a probabilistic map of HG can approximate HG in a new individual. We
compare three registration procedures; high-dimensional (HAMMER); template-free B-spline-based groupwise;
and segmentation-based (SPM5); to each other and to a previously published (affine) probabilistic map of HG.1
We quantitatively evaluate the accuracy of the resulting maps using evidence-based diagnostic measures within
a leave-one-out cross-validation structure, to demonstrate that maps created using either HAMMER or SPM5
have relatively high sensitivity, specificity and positive predictive value, compared to a map created using the
groupwise algorithm or compared to the published map.