Guy Nir

HU Protein Induces Incoherent DNA Persistence Length

HU is a highly conserved protein that is believed to play an important role in the architecture and dynamic compaction of bacterial DNA. Its ability to control DNA bending is crucial for functions such as transcription and replication. The effects of HU on the DNA structure have been studied so far mainly by single molecule methods that require us to apply stretching forces on the DNA and therefore may perturb the DNA-protein interaction. To overcome this hurdle, we study the effect of HU on the DNA structure without applying external forces by using an improved tethered particle motion method. By combining the results with DNA curvature analysis from atomic force microscopy measurements we find that the DNA consists of two different curvature distributions and the measured persistence length is determined by their interplay. As a result, the effective persistence length adopts a bimodal property that depends primarily on the HU concentration. The results can be explained according to a recently suggested model that distinguishes single protein binding from cooperative protein binding.

Registration of Whole-Mount Histology and Volumetric Imaging of the Prostate Using Particle Filtering

Registration of histological slices to volumetric imaging of the prostate is an important task that can be used to optimize imaging for cancer detection. Such registration is challenging due to physical changes of the specimen during excision and fixation, and misalignment of the histological slices during preparation and digital scanning. In this work, we consider a multi-slice to volume registration method in which a stack of sparse, unaligned 2-D whole-mount histological slices is registered to a 3-D volumetric imaging of the prostate. We propose a particle filtering framework to contend with the high dimensionality of the search space and multimodal nature of the optimization. Such framework allows modeling of the uncertainty in the pose of the slices and in the imaged information, in order to derive optimal registration parameters in a Bayesian approach. Intensity-, region-, and point-based similarity metrics were incorporated into the registration algorithm to account for different imaging modalities. We demonstrate and evaluate our method on a diverse set of data that includes a synthetic volume, ex vivo and in vivo magnetic resonance imaging, and in vivo ultrasound.

Ultrasound RF time series for tissue typing: First in vivo clinical results

The low diagnostic value of ultrasound in prostate cancer imaging has resulted in an effort to enhance the tumor contrast using ultrasound-based technologies that go beyond traditional B-mode imaging. Ultrasound RF time series, formed by echo samples originating from the same location over a few seconds of imaging, has been proposed and experimentally used for tissue typing with the goal of cancer detection. In this work, for the first time we report the preliminary results of in vivo clinical use of spectral parameters extracted from RF time series in prostate cancer detection. An image processing pipeline is designed to register the ultrasound data to wholemount histopathology references acquired from prostate specimens that are removed in radical prostatectomy after imaging. Support vector machine classification is used to detect cancer in 524 regions of interest of size 5×5 mm, each forming a feature vector of spectral RF time series parameters. Preliminary ROC curves acquired based on RF time series analysis for individual cases, with leave-one-patient-out cross validation, are presented and compared with B-mode texture analysis.

Registration of whole-mount histology and tomography of the prostate using particle filtering

Registration of histological slices to volumetric imaging of the prostate is an important task that can be used to optimize imaging for cancer detection. Such registration is challenging due to change in volume of the specimen during fixation, and misalignment of the histological slices during preparation and digital scanning. In this work we propose a multiple-slice to volume registration method in which a stack of equispaced, uniaxial but unaligned 2D contours, extracted from digitally scanned whole-mount histological slices, is registered to a 3D surface, extracted from a volumetric image of the prostate. Initially, the stack of unaligned contours is coarsely aligned to the surface as a whole. Then, each contour is finely registered to the surface while being confined to its plane along the sectioning axis. We incorporate the method in a particle filtering framework to compensate for the high dimensionality of the search space and multi-modal nature of the problem. Moreover, such framework allows modeling the uncertainty in the segmentation of the contours and surface, in order to derive optimal registration parameters in a Bayesian approach. The proposed algorithm is demonstrated and evaluated on both synthetic and clinical data. The mean area overlap of the registered gland and the segmented histology was found to be 90.2%, with a mean registration error of 1.8mm between visible landmarks.

Model-based registration of ex vivo and in vivo MRI of the prostate using elastography

Registration of histopathology to in vivo magnetic resonance imaging (MRI) of the prostate is an important task that can be used to optimize in vivo imaging for cancer detection. Such registration is challenging due to the change in volume and deformation of the prostate during excision and fixation. One approach towards this problem involves the use of an ex vivo MRI of the excised prostate specimen, followed by in vivo to ex vivo MRI registration of the prostate. We propose a novel registration method that uses a patient-specific biomechanical model acquired using magnetic resonance elastography to deform the in vivo volume and match it to the surface of the ex vivo specimen. The forces that drive the deformations are derived from a region-based energy, with the elastic potential used for regularization. The incorporation of elastography data into the registration framework allows inhomogeneous elasticity to be assigned to the in vivo volume. We show that such inhomogeneity improves the registration results by providing a physical regularization of the deformation map. The method is demonstrated and evaluated on six clinical cases.

Biomechanical Modeling of the Prostate for Procedure Guidance and Simulation

Biomechanical models of the prostate have a number of potential applications in the diagnosis and management of prostate cancer. Most importantly, it has been shown in several studies that cancerous prostate tissue has different viscoelastic properties than normal prostate tissue: it is typically stiffer (higher storage modulus) and more viscous (higher loss modulus). If a strong correlation can be obtained between malignant tissue and its viscoelastic properties, then all commonly practiced prostate cancer procedures—biopsies, surgery and radiation treatment—can be improved by elasticity imaging. The elastic properties of the prostate and peri-prostatic tissue can also be used in procedure planning, even if such elastic properties do not show strong correlation to cancer. This chapter starts with an introduction to the prostate anatomy, prostate cancer, and a description of the most common procedures and their clinical needs. It continues by presenting the potential impact of elasticity imaging on these procedures. A brief survey of elastography techniques is presented next, with a sampling of some prostate elastography results to date. We describe two of the systems that we developed for the acquisition of prostate ultrasound and magnetic resonance elastography images and summarize our results to date. We show that these elasticity images can be used for prostate segmentation and cross-modality image registration. Furthermore, we show how prostate region deformation models can be used in the development of a prostate brachytherapy simulator which can also be used in the planning of needle insertions that account for deformation.

MR elastography and diffusion‐weighted imaging of ex vivo prostate cancer: quantitative comparison to histopathology

The purpose of this work was (1) to develop a magnetic resonance elastography (MRE) system for imaging of the ex vivo human prostate and (2) to assess the diagnostic power of mono‐frequency and multi‐frequency MRE and diffusion weighted imaging (DWI) alone and combined as correlated with histopathology in a patient study.

A framework for optimization-based design of motion encoding in magnetic resonance elastography

In conventional three‐dimensional magnetic resonance elastography, motion encoding gradients (MEGs) synchronized to a mechanical excitation are applied separately in each direction to encode tissue displacement generated by the corresponding waves. This requires long acquisition times that introduce errors due to patient motion and may hinder clinical deployment of magnetic resonance elastography. In this article, a framework for MEGs sequence design is proposed to reduce scanning time and increase signal‐to‐noise ratio.

Multi-parametric 3D quantitative ultrasound vibro-elastography imaging for detecting palpable prostate tumors.

In this article, we describe a system for detecting dominant prostate tumors, based on a combination of features extracted from a novel multi-parametric quantitative ultrasound elastography technique. The performance of the system was validated on a data-set acquired from n = 10 patients undergoing radical prostatectomy. Multi-frequency steady-state mechanical excitations were applied to each patients prostate through the perineum and prostate tissue displacements were captured by a transrectal ultrasound system. 3D volumetric data including absolute value of tissue elasticity, strain and frequency-response were computed for each patient. Based on the combination of all extracted features, a random forest classification algorithm was used to separate cancerous regions from normal tissue, and to compute a measure of cancer probability. Registered whole mount histopathology images of the excised prostate gland were used as a ground truth of cancer distribution for classifier training. An area under receiver operating characteristic curve of 0.82 +/- 0.01 was achieved in a leave-one-patient-out cross validation. Our results show the potential of multi-parametric quantitative elastography for prostate cancer detection for the first time in a clinical setting, and justify further studies to establish whether the approach can have clinical use.

MR elastography of prostate cancer: quantitative comparison with histopathology and repeatability of methods: TRANSPERINEAL PROSTATE MRE: INITIAL PATIENT STUDY

The purpose of this work was to assess trans‐perineal prostate magnetic resonance elastography (MRE) for (1) repeatability in phantoms/volunteers and (2) diagnostic power as correlated with histopathology in prostate cancer patients.