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ABSTRACT:The correlation function of the echo signal intensities at detectability via spatial and frequency compounding [2][3][4][5]. These a fixed region on a series of B-mode images is directly related to the speckle reduction methods involve the averaging of images in change of speckle patterns between these images. An indication is which the speckle pattern has been changed. Recently, assessment given here of how the rate of the change of that correlation function of blood flow and, potentially, perfusion has been investigated can be used to estimate the scan-plane motion in any direction relaby use of decorrelation of the Doppler signal with time at one tive to the imaged tissue or other material. In this first implementation location in the image [6]. In this article we use the speckle correlait is assumed that the statistical properties of the echo signals follow tion between corresponding points in a series of scan planes those of a complex circular Gaussian, and the case is considered of for an alternative purpose: to determine the relative scan-plane We analyze the second-order statistical properties of the echo taken at differing locations within a volume.

Objective. The goal is to increase the precision of radiation delivery during radiotherapy by tracking the movements of the tumor and other surrounding normal tissues due to respiratory and other body motions. Introduction. This work presents the recent advancement of X-ray-induced radiation acoustic imaging (xRAI) technology and the evaluation of its feasibility for real-time monitoring of geometric and morphological misalignments of the X-ray field with respect to the target tissue by combining xRAI with established ultrasound (US) imaging, thereby improving radiotherapy tumor eradication and limiting treatment side effects. Methods. An integrated xRAI and B-mode US dual-modality system was established based on a clinic-ready research US platform. The performance of this dual-modality imaging system was evaluated via experiments on phantoms and ex vivo and in vivo rabbit liver models. Results. This system can alternatively switch between the xRAI and the US modes, with spatial resolutions of 1.1 mm and 0.37 mm, respectively. 300 times signal averaging was required for xRAI to reach a satisfactory signal-to-noise ratio, and a frame rate of 1.1 Hz was achieved with a clinical linear accelerator. The US imaging frame rate was 22 Hz, which is sufficient for real-time monitoring of the displacement of the target due to internal body motion. Conclusion. Our developed xRAI, in combination with US imaging, allows for mapping of the dose deposition in biological samples in vivo, in real-time, during radiotherapy. Impact Statement. The US-based image-guided radiotherapy system presented in this work holds great potential for personalized cancer treatment and better outcomes.

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Ionizing radiation acoustic imaging (iRAI) allows online monitoring of radiation’s interactions with tissues during radiation therapy, providing real-time, adaptive feedback for cancer treatments. We describe an iRAI volumetric imaging system that enables mapping of the three-dimensional (3D) radiation dose distribution in a complex clinical radiotherapy treatment. The method relies on a two-dimensional matrix array transducer and a matching multi-channel preamplifier board. The feasibility of imaging temporal 3D dose accumulation was first validated in a tissue-mimicking phantom. Next, semiquantitative iRAI relative dose measurements were verified in vivo in a rabbit model. Finally, real-time visualization of the 3D radiation dose delivered to a patient with liver metastases was accomplished with a clinical linear accelerator. These studies demonstrate the potential of iRAI to monitor and quantify the 3D radiation dose deposition during treatment, potentially improving radiotherapy treatment efficacy using real-time adaptive treatment.

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Abstract-Registration of an image, the query or reference, to a database of rotated and translated exemplars constitutes an important image retrieval and indexing application which arises in biomedical imaging, digital libraries, georegistration, and other areas. Two important issues are the specification of a class of discriminatory and generalizable image features and determination of an appropriate image-dissimilarity measure to rank the closenes of the query image with respect to images in the database. The theoretically best set of features and dissimilarity measure are those which can be implemented with the lowest misregistration error rate-In this paper we study a method based on feature discrimination using feature coincidence tree and mutnal a-information measures of feature correlation. Feature coincidence trees represent the commonality between pairs of images using joint histograms of many simple features, or tags, which are organized in a data s t~c t n~ similar to that Amit and Geman's randomized trees for shape recognition. The mutual alpha-information meamre is a ranking discriminant applied to the joint histograms which is motivated by a large deviations framework for detection error rates. We illustrate the methodology in the context of registering ultrasound scans of homan breast images.

Image registration requires the specification of a class of discriminatory image features and an appropriate imagedissimilarity measure. Entropic spanning graphs produce a consistent estimator of feature entropy and divergence. We compare direct estimators with non-parametric "plugin" density estimators, on single pixels and independent image component feature vectors. We have also investigated a technique for minimum spanning tree construction with significantly lower memory and time complexity. On the basis of misregistration errors with decreasing SNR, the minimal graph entropy estimator can have better performance than indirect estimators. In general, misregistration errors are lower with higher dimensional ICA feature vectors as compared to single pixels.