Jovan G. Brankov

Bio: Jovan G. Brankov is an academic researcher from Illinois Institute of Technology. The author has contributed to research in topics: Iterative reconstruction & Image quality. The author has an hindex of 22, co-authored 157 publications receiving 2269 citations.

Machine Learning in Medical Imaging

Abstract: This article will discuss very different ways of using machine learning that may be less familiar, and we will demonstrate through examples the role of these concepts in medical imaging. Although the term machine learning is relatively recent, the ideas of machine learning have been applied to medical imaging for decades, perhaps most notably in the areas of computer-aided diagnosis (CAD) and functional brain mapping. We will not attempt in this brief article to survey the rich literature of this field. Instead our goals will be 1) to acquaint the reader with some modern techniques that are now staples of the machine-learning field and 2) to illustrate how these techniques can be employed in various ways in medical imaging.

Digital watermarking robust to geometric distortions

Abstract: In this paper, we present two watermarking approaches that are robust to geometric distortions. The first approach is based on image normalization, in which both watermark embedding and extraction are carried out with respect to an image normalized to meet a set of predefined moment criteria. We propose a new normalization procedure, which is invariant to affine transform attacks. The resulting watermarking scheme is suitable for public watermarking applications, where the original image is not available for watermark extraction. The second approach is based on a watermark resynchronization scheme aimed to alleviate the effects of random bending attacks. In this scheme, a deformable mesh is used to correct the distortion caused by the attack. The watermark is then extracted from the corrected image. In contrast to the first scheme, the latter is suitable for private watermarking applications, where the original image is necessary for watermark detection. In both schemes, we employ a direct-sequence code division multiple access approach to embed a multibit watermark in the discrete cosine transform domain of the image. Numerical experiments demonstrate that the proposed watermarking schemes are robust to a wide range of geometric attacks.

Multiple-image radiography

Abstract: Conventional radiography produces a single image of an object by measuring the attenuation of an x-ray beam passing through it When imaging weakly absorbing tissues, x-ray attenuation may be a suboptimal signature of diseaserelated information In this paper we describe a new phase-sensitive imaging method, called multiple-image radiography (MIR), which is an improvement on a prior technique called diffraction-enhanced imaging (DEI) This paper elaborates on our initial presentation of the idea in Wernick et al (2002 Proc Int SympBiomedImaging pp 129–32) MIR simultaneously produces several images from a set of measurements made with a single x-ray beam Specifically, MIR yields three images depicting separately the effects of refraction, ultrasmall-angle scatter and attenuation by the object All three images have good contrast, in part because they are virtually immune from degradation due to scatter at higher angles MIR also yields a very comprehensive object description, consisting of the angular intensity spectrum of a transmitted x-ray beam at every image pixel, within a narrow angular range Our experiments are based on data acquired using a synchrotron light source; however, in preparation for more practical implementations using conventional x-ray sources, we develop and evaluate algorithms designed for Poisson noise, which is characteristic of photon-limited imaging The results suggest that MIR is capable of operating at low photon count levels, therefore the method

A physical model of multiple-image radiography

Abstract: We recently proposed a phase-sensitive x-ray imaging method called multiple-image radiography (MIR), which is an improvement on the diffraction-enhanced imaging technique. MIR simultaneously produces three images, depicting separately the effects of absorption, refraction and ultra-small-angle scattering of x-rays, and all three MIR images are virtually immune to degradation caused by scattering at higher angles. Although good results have been obtained using MIR, no quantitative model of the imaging process has yet been developed. In this paper, we present a theoretical prediction of the MIR image values in terms of fundamental physical properties of the object being imaged. We use radiative transport theory to model the beam propagation, and we model the object as a stratified medium containing discrete scattering particles. An important finding of our analysis is that the image values in all three MIR images are line integrals of various object parameters, which is an essential property for computed tomography to be achieved with conventional reconstruction methods. Our analysis also shows that MIR truly separates the effects of absorption, refraction and ultra-small-angle scattering for the case considered. We validate our analytical model using real and simulated imaging data.

Tomographic image reconstruction based on a content-adaptive mesh model

Abstract: In this paper, we explore the use of a content-adaptive mesh model (CAMM) for tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first represented by a mesh model, an efficient image description based on nonuniform sampling. In the CAMM, image samples (represented as mesh nodes) are placed most densely in image regions having fine detail. Tomographic image reconstruction in the mesh domain is performed by maximum-likelihood (ML) or maximum a posteriori (MAP) estimation of the nodal values from the measured data. A CAMM greatly reduces the number of unknown parameters to be determined, leading to improved image quality and reduced computation time. We demonstrated the method in our experiments using simulated gated single photon emission computed tomography (SPECT) cardiac-perfusion images. A channelized Hotelling observer (CHO) was used to evaluate the detectability of perfusion defects in the reconstructed images, a task-based measure of image quality. A minimum description length (MDL) criterion was also used to evaluate the effect of the representation size. In our application, both MDL and CHO suggested that the optimal number of mesh nodes is roughly five to seven times smaller than the number of projection bins. When compared to several commonly used methods for image reconstruction, the proposed approach achieved the best performance, in terms of defect detection and computation time. The research described in this paper establishes a foundation for future development of a (four-dimensional) space-time reconstruction framework for image sequences in which a built-in deformable mesh model is used to track the image motion.

X-ray phase-contrast imaging: from pre-clinical applications towards clinics

Abstract: Phase-contrast x-ray imaging (PCI) is an innovative method that is sensitive to the refraction of the x-rays in matter. PCI is particularly adapted to visualize weakly absorbing details like those often encountered in biology and medicine. In past years, PCI has become one of the most used imaging methods in laboratory and preclinical studies: its unique characteristics allow high contrast 3D visualization of thick and complex samples even at high spatial resolution. Applications have covered a wide range of pathologies and organs, and are more and more often performed in vivo. Several techniques are now available to exploit and visualize the phase-contrast: propagation- and analyzer-based, crystal and grating interferometry and non-interferometric methods like the coded aperture. In this review, covering the last five years, we will give an overview of the main theoretical and experimental developments and of the important steps performed towards the clinical implementation of PCI.

How many trees in a random forest

Abstract: Random Forest is a computationally efficient technique that can operate quickly over large datasets. It has been used in many recent research projects and real-world applications in diverse domains. However, the associated literature provides almost no directions about how many trees should be used to compose a Random Forest. The research reported here analyzes whether there is an optimal number of trees within a Random Forest, i.e., a threshold from which increasing the number of trees would bring no significant performance gain, and would only increase the computational cost. Our main conclusions are: as the number of trees grows, it does not always mean the performance of the forest is significantly better than previous forests (fewer trees), and doubling the number of trees is worthless. It is also possible to state there is a threshold beyond which there is no significant gain, unless a huge computational environment is available. In addition, it was found an experimental relationship for the AUC gain when doubling the number of trees in any forest. Furthermore, as the number of trees grows, the full set of attributes tend to be used within a Random Forest, which may not be interesting in the biomedical domain. Additionally, datasets' density-based metrics proposed here probably capture some aspects of the VC dimension on decision trees and low-density datasets may require large capacity machines whilst the opposite also seems to be true.

Ratiometric optical nanoprobes enable accurate molecular detection and imaging

Abstract: Exploring and understanding biological and pathological changes are of great significance for early diagnosis and therapy of diseases. Optical sensing and imaging approaches have experienced major progress in this field. Particularly, an emergence of various functional optical nanoprobes has provided enhanced sensitivity, specificity, targeting ability, as well as multiplexing and multimodal capabilities due to improvements in their intrinsic physicochemical and optical properties. However, one of the biggest challenges of conventional optical nanoprobes is their absolute intensity-dependent signal readout, which causes inaccurate sensing and imaging results due to the presence of various analyte-independent factors that can cause fluctuations in their absolute signal intensity. Ratiometric measurements provide built-in self-calibration for signal correction, enabling more sensitive and reliable detection. Optimizing nanoprobe designs with ratiometric strategies can surmount many of the limitations encountered by traditional optical nanoprobes. This review first elaborates upon existing optical nanoprobes that exploit ratiometric measurements for improved sensing and imaging, including fluorescence, surface enhanced Raman scattering (SERS), and photoacoustic nanoprobes. Next, a thorough discussion is provided on design strategies for these nanoprobes, and their potential biomedical applications for targeting specific biomolecule populations (e.g. cancer biomarkers and small molecules with physiological relevance), for imaging the tumor microenvironment (e.g. pH, reactive oxygen species, hypoxia, enzyme and metal ions), as well as for intraoperative image guidance of tumor-resection procedures.

Current Applications and Future Impact of Machine Learning in Radiology.

Abstract: Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed. © RSNA, 2018