KEYWORDS: Computed tomography, Image registration, Magnetic resonance imaging, 3D image processing, Image segmentation, Tissues, In vivo imaging, Optical coherence tomography, Positron emission tomography, Medical imaging
Histopathology is the accepted gold standard for identifying cancerous tissues. Validation of in vivo imaging signals with precisely correlated histopathology can potentially improve the delineation of tumors in medical images for focal therapy planning, guidance, and assessment. Registration of histopathology with other imaging modalities is challenging due to soft tissue deformations that occur between imaging and histological processing of tissue. In this paper, a framework for precise registration of medical images and pathology using white-light images (photographs) is presented. A euthanized normal mouse was imaged using four imaging modalities: CBCT, PET-CT, MRI and micro CT. The mouse was then fixed in an embedding medium, optical cutting temperature (OCT) compound, with co-registration markers and sliced at 50 m intervals in a cryostatmicrotome. The device automatically photographed each slice with a mounted camera and reconstructed the 3D white-light image of the mouse through co-registering of consecutive slices. The white-light image was registered to the four imaging modalities based on the external contours of the mouse. Six organs (brain, liver, stomach, pancreas, kidneys and bladder) were contoured on the MR image while the skeletal structure and lungs were segmented on the CBCT image. The contours of these structures were propagated to the additional imaging modalities based on the registrations to the white-light image and were analyzed qualitatively by developing an anatomical atlas of normal mouse defined using three imaging modalities. This work will serve as the foundation to include histopathology through the transfer of the imaged slice onto tape for histological processing.
Model observers intended to predict the diagnostic performance of human observers should account for the effects of both quantum and anatomical noise. We compared the abilities of several visual-search (VS) and scanning Hotelling-type models to account for anatomical noise in a localization receiver operating characteristic (LROC) study involving simulated nuclear medicine images. Our VS observer invoked a two-stage process of search and analysis. The images featured lesions in the prostate and pelvic lymph nodes. Lesion contrast and the geometric resolution and sensitivity of the imaging collimator were the study variables. A set of anthropomorphic mathematical phantoms was imaged with an analytic projector based on eight parallel-hole collimators with different sensitivity and resolution properties. The LROC study was conducted with human observers and the channelized nonprewhitening, channelized Hotelling (CH) and VS model observers. The CH observer was applied in a “background-known-statistically” protocol while the VS observer performed a quasi-background-known-exactly task. Both of these models were applied with and without internal noise in the decision variables. A perceptual search threshold was also tested with the VS observer. The model observers without inefficiencies failed to mimic the average performance trend for the humans. The CH and VS observers with internal noise matched the humans primarily at low collimator sensitivities. With both internal noise and the search threshold, the VS observer attained quantitative agreement with the human observers. Computational efficiency is an important advantage of the VS observer.
Many search-capable model observers follow task paradigms that specify clinically unrealistic prior knowledge about the anatomical backgrounds in study images. Visual-search (VS) observers, which implement distinct, feature-based candidate search and analysis stages, may provide a means of avoiding such paradigms. However, VS observers that conduct single-feature analysis have not been reliable in the absence of any background information. We investigated whether a VS observer based on multifeature analysis can overcome this background dependence. The testbed was a localization ROC (LROC) study with simulated whole-body PET images. Four target-dependent morphological features were defined in terms of 2D cross-correlations involving a known tumor profile and the test image. The feature values at the candidate locations in a set of training images were fed to a support-vector machine (SVM) to compute a linear discriminant that classified locations as tumor-present or tumor-absent. The LROC performance of this SVM-based VS observer was compared against the performances of human observers and a pair of existing model observers.
Model observers have frequently been used for hardware optimization of imaging systems. For model observers to reliably mimic human performance it is important to account for the sources of variations in the images. Detection-localization tasks are complicated by anatomical noise present in the images. Several scanning observers have been proposed for such tasks. The most popular of these, the channelized Hotelling observer (CHO) incorporates anatomical variations through covariance matrices. We propose the visual-search (VS) observer as an alternative to the CHO to account for anatomical noise. The VS observer is a two-step process which first identifies suspicious tumor candidates and then performs a detailed analysis on them. The identification of suspicious candidates (search) implicitly accounts for anatomical noise. In this study we present a comparison of these two observers with human observers. The application considered is collimator optimization for planar nuclear imaging. Both observers show similar trends in performance with the VS observer slightly closer to human performance.
SPECT imaging using In-111 ProstaScint is an FDA-approved method for diagnosing prostate cancer metastases within the pelvis. However, conventional medium-energy parallel-hole (MEPAR) collimators produce poor image quality and we are investigating the use of multipinhole (MPH) imaging as an alternative. This paper presents a method for evaluating MPH designs that makes use of sampling-sensitive (SS) mathematical model observers for tumor detectionlocalization tasks. Key to our approach is the redefinition of a normal (or background) reference image that is used with scanning model observers. We used this approach to compare different MPH configurations for the task of small-tumor detection in the prostate and surrounding lymph nodes. Four configurations used 10, 20, 30, and 60 pinholes evenly spaced over a complete circular orbit. A fixed-count acquisition protocol was assumed. Spherical tumors were placed within a digital anthropomorphic phantom having a realistic Prostascint biodistribution. Imaging data sets were generated with an analytical projector and reconstructed volumes were obtained with the OSEM algorithm. The MPH configurations were compared in a localization ROC (LROC) study with 2D pelvic images and both human and model observers. Regular and SS versions of the scanning channelized nonprewhitening (CNPW) and visual-search (VS) model observers were applied. The SS models demonstrated the highest correlations with the average human-observer results
Early staging of prostate cancer (PC) is a significant challenge, in part because of the small tumor sizes in- volved. Our long-term goal is to determine realistic diagnostic task performance benchmarks for standard PC imaging with single photon emission computed tomography (SPECT). This paper reports on a localization receiver operator characteristic (LROC) validation study comparing human and model observers. The study made use of a digital anthropomorphic phantom and one-cm tumors within the prostate and pelvic lymph nodes. Uptake values were consistent with data obtained from clinical In-111 ProstaScint scans. The SPECT simulation modeled a parallel-hole imaging geometry with medium-energy collimators. Nonuniform attenua- tion and distance-dependent detector response were accounted for both in the imaging and the ordered-subset expectation-maximization (OSEM) iterative reconstruction. The observer study made use of 2D slices extracted from reconstructed volumes. All observers were informed about the prostate and nodal locations in an image. Iteration number and the level of postreconstruction smoothing were study parameters. The results show that a visual-search (VS) model observer correlates better with the average detection performance of human observers than does a scanning channelized nonprewhitening (CNPW) model observer.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.