BACKGROUND

Technical Field

The present disclosure relates to a method for acquiring tomographic image data by oversampling, an acquisition device, and a control program. More specifically, the present disclosure relates to an acquisition method, an acquisition device, and a control program for tomographic image data employing an efficient algebraic method of discrete inverse Radon transform based on oversampling.

Description of the Related Art

Tomography, or a tomographic imaging method using waves or particles having transmission nature such as X-rays, gamma rays, light waves, or waves or particles exhibiting any sort of permeability or transmission ability such as a seismic wave or other has been put into practical use. Typical examples include an X-ray CT scanner used in medical and industrial applications, SPECT (Single Photon Emission Computed Tomography), and Optical Tomography. In recent years, an optical CT device using visible light has been developed, and an imaging method using a gel dosimeter as an object is studied after its irradiation by radiation in order to verify three-dimensional distribution of radiation dose before performing radiation therapy of cancer. Mathematical principle assuring the tomographic image of the object by these imaging methods is Radon transform. In this context, the processing for capturing the attenuation rate of each part inside the object as imaging information from outside is described by Radon transform, and the processing for reconstructing the tomographic information of the object from the captured information is described by an inverse Radon transform. That is, the operation for detecting the flow of the wave or particles attenuated by the object at each position by changing the irradiation direction or the detection direction corresponds to the Radon transform. In contrast, an operation for reconstructing an image by estimating the attenuation rate of each part inside the object from the intensity information of each direction and each position of the detection corresponds to the inverse Radon transform. The Radon- or inverse Radon transform for continuous coordinates and continuous directions for real images is carried out using sampling points on coordinates or orientations for handling a finite number of pixels. The inverse Radon transform (discrete inverse Radon transform) for image reconstruction under sampled coordinates and orientations is reduced into solving multiple simultaneous linear equations or matrix operations. These mathematical aspects are described in standard textbooks (e.g., Non-Patent Documents 1 and 2).

Iterative reconstruction (IR), which is accompanied by iterative approximation to reduce noise and artifacts, has also attracted attention. The iteration process is repeated, for example, 100 times or more to obtain the solution. Such a technique for obtaining a reconstructed image by deriving an approximate solution of multiple simultaneous linear equations by relying on an algebraic method is called an ART (Algebraic Reconstruction Technique). The ARTs include techniques that are referred to as, for example, a Maximum Likelihood-Expectation Maximization (ML-EM) method, an Additive-Simultaneous Iterative Reconstruction Techniques (ASIRT), and a Multiplicative-Simultaneous Iterative Reconstruction Techniques (MSIRT). In conventional ARTs, a calculation scale becomes huge and a large amount of computational resources are required. In addition, repeated iterations often do not approach the solution corresponding to the true image and are captured by the local solution. On the other hand, approximation calculation efficiency in computing has been improved without resorting algebraic methods while utilizing realistic calculation resources. Such examples include FBP (Filtered Back Projection) (for example, Patent Document 1). Since the scale of the FBP calculation is smaller than ARTs, it is currently most frequently used with X-ray CT scanners.

In the course of the progress of the tomographic imaging methods from the beginning to date, there has been a continuing demand for increasing the number of imaging pixels, and therefore the possibility of the FBP has been intensively studied. The FBP is, however, generally disadvantageous in the quality of reconstructed images in which various artifacts are readily developed compared to IR. Nowadays, the ARTs, which are intrinsically advantageous in the quality of a reconstructed image, have been attracted much attention again based on continuing development of computational powers (see, for example, Patent Document 2).

CITATION LIST

Patent Documents

Patent Document 1: W02014/021349

Patent Document 2: W02013/105583

Non-Patent Documents

Non-Patent Document 1: Aviash, C Kak and Molcolm, Slaney, “Principles of Computerized Tomographic Imaging,” IEEE Press (1988)

Non-Patent Document 2: Stanley R. Deans, “The Radon Transform and Some of Its Applications,” Krieger Publications (1983)

Non-Patent Document 3: Robert L Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Medical Physics, vol. 12, no.2, pages 252-255 (1985)

Non-Patent Document 4: Sunnegardh, J. and Danielsson, P. -E. “A New Anti-Aliased Projection Operator for Iterative CT Reconstruction,” 9th International Meeting of Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, pages 125-127 (2009)

BRIEF SUMMARY

Technical Problem

There is continuing need for quality of reconstructed images in addition to the quest for increasing the number of pixels for improving the quality of images. Even when a conventional ART is adopted in a situation in which the number of imaging pixels is increased, the number of dimensions of the simultaneous linear equation to be solved, or variables to be determined, is increased, which requires a huge scale of calculation resources and takes a long time for processing. Therefore, in order to seek practicability in image data acquisition processing by way of the conventional ART, all we can do is to wait for the improvement of the calculation capability. In particular, it is not practical to obtain a large number of tomographic images, for example, to obtain a three-dimensional volume image while relying upon the conventional ARTs, which requires re-execution for each imaging for the iterative calculation accompanying iterative approximation.

The present disclosure addresses at least some of the problems mentioned above. The present disclosure provides a high degree of practicality for high quality tomographic imaging methodology by employing a new sampling technique that is supported by an algebraic exact solution of the discrete inverse Radon transform, which is responsible for image reconstruction.

Solution to Problem

The inventors realized that the problem with conventional ARTs is due to their discretization procedures, such as sampling. Conventional ARTs contain a large number of overlapping equations in a multidimensional system of linear equations, and the number of available equations is practically insufficient in comparison with the number of variables to be determined. As a result, an iterative approximation has been required and the effort has been directed to the improvement of computation performance of processors. To address the above problem, the inventor has completed a new solution by re-examining it from the discretization method in which the unconventional sampling is combined with appropriate processing.

That is, in certain aspects of the present disclosure provided is a method for acquiring tomographic image data comprising: a step for disposing an object in a detection range of a detection device having N detection elements arranged in at least one row where N is a positive integer; a step of detection in which detection operations for obtaining an intensity value for each of the detection elements by receiving transmitted waves or particles by each of the detection elements are repeated, where the waves or particles are detectable by the detection device, over (N+n) directions (n is an integer greater than or equal to 1) that are not overlapped with each other in relative detection directions for the waves or particles viewed from the object, while irradiation of the waves or particles toward the detection device by an emission device is performed, or while the waves or particles not generated by the emission device are transmitted through each part of the object; a vectorization step for obtaining a first vector with N×(N+n) elements from a detection signal by the detection device in the detection operations, wherein the elements of the first vector corresponding to those obtained by vectorizing an oversampled sinogram with (N+n) rows and N columns, each row and each column of which are associated with each detection direction and each detection element, respectively; a vector decimation step of removing n×N elements corresponding a decimation order from the first vector to obtain a second vector having the remaining N×N elements; a discrete inverse Radon transform step of operating a discrete inverse Radon transform matrix to the second vector to obtain a third vector having N×N elements; and an image data generation step for obtaining image data, by de-vectorizing the third vector, for a two-dimensional tomographic image with N pixels×N pixels having a pixel arrangement that is stationary with respect to the object.

The present disclosure can also be implemented in an aspect of a tomographic image data acquisition device. Similarly, the present disclosure can be implemented in an aspect of a control program for a tomographic image data acquisition device.

Oversampling or oversample is based on the sampling concept of the digital signal processing theory, and refers to a process of sampling at a density higher than that of basic sampling. In particular, oversampling in each aspect of the present disclosure includes sampling the necessary angular range by selecting N+n sample points in the detection direction without overlap with each other, where N is the number denoting operative detection elements in a detection direction and n is an integer greater than or equal to 1. It should be noted that the number of detection elements denotes the number of operative elements, unless otherwise noted. Decimation refers to a process in which at least one of the elements of an oversampled matrix or the elements of a corresponding vector is discarded (removed) in subsequent computational processes. In the present application, including these, we may use terminology that follows the conventions of the art to which the disclosure belongs unless it renders the description unclear. Waves may include any sort of wave that can be conceived of as attenuated through propagation, such as electromagnetic waves, sound waves, and may include light waves (infrared, visible, and ultraviolet) and oscillations (e.g., seismic waves). Particles include any sort of particles that can be conceived of as attenuated through propagation in a stream of particle rays, including neutron rays and other radiation. Anything that may have properties both of wave and particle in a quantum mechanical sense, such as electrons and photons, is also included in either or both of the waves or particles. Similarly, anything that is regarded as waves or particles in a classical mechanics point of view is also included in one or both of the waves or particles. These waves or particles can be used in the practice of the present disclosure, even they are generated by a device or an emitting device, that is an artificial source of their emission, or may be generated from natural sources, for example, cosmic rays, or generated due to a property of the object, for example, radiation emitted from various parts of the interior of the object, which are referred to as “waves or particles that are not generated by the emitting device” in this application.

Advantageous Effects of Disclosure

In certain aspects of the present disclosure, a high-definition and high-quality reconstructed image can be acquired using a relatively small computational resources at a high speed.

