Abstract:
A medical imaging system ( 10 ) includes at least one radiation detection head ( 16 ) disposed adjacent a subject receiving aperture ( 18 ) to detect radiation from a subject. The detected radiation is reconstructed into at least one initial 2D projection image (μ). Resolution in each initial 2D image (μ) is restored by using the extended iterative constrained deconvolution algorithm by incorporating different estimates of the system response function which estimates correspond to different distances between the detection head and the origins of the detected radiation. Measured response functions are used to restore a series of images. The optimal image is determined by automatic searching with the figure of merit, by user&#39;s observation, or by using blind deconvolution for a concurrent estimating of the system response function and updating the original image.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. provisional application Ser. No. 60/628,638 filed Nov. 17, 2004, which is incorporated herein by reference. 
    
    
     The present invention relates to the nuclear medicine diagnostic imaging systems and methods. It finds particular application in conjunction with the Single Photon Emission Tomography (SPECT) systems and will be described with particular reference thereto. It will be appreciated that the invention is also applicable to other imaging systems such as Positron Emission Tomography systems (PET), Computed Tomography systems (CT), x-ray imaging, and the like. 
     Nuclear imaging employs a source of radioactivity to image the anatomy of a patient. Typically, a radiopharmaceutical is injected into the patient. Radiopharmaceutical compounds contain a radioisotope that undergoes gamma-ray decay at a predictable rate and characteristic energy. One or more radiation detectors are placed adjacent to the patient to monitor and record emitted radiation. Sometimes, the detector is rotated or indexed around the patient to monitor the emitted radiation from a plurality of directions. Based on information such as detected position and energy, the radiopharmaceutical distribution in the body is determined and an image of the distribution is reconstructed to study the circulatory system, radiopharmaceutical uptake in selected organs or tissue, and the like. 
     In a traditional scintillation detector, the detector has a scintillator made up of a large scintillation crystal or matrix of smaller scintillation crystals. In either case, the scintillator is viewed by a matrix of sensors. A commonly employed sensor is a photomultiplier tube (“PMT”). A collimator, which includes a grid- or honeycomb-like array of radiation absorbent material, is located between the scintillator and the subject being examined to limit the angle of acceptance of radiation which impinges on the scintillator. Each radiation event impinging on the scintillator generates a corresponding flash of light (scintillation) that is seen by the PMTs. Based on the outputs from the PMTs, the gamma camera maps radiation events, i.e., it determines the energy and position of radiation rays impinging the scintillator. 
     Image quality of the SPECT images is determined by a count sensitivity of the detector and geometry of the collimator. Generally, it is difficult to obtain the high quality SPECT images because of the limited spatial resolution due to various factors including system parameters, such as collimator geometry, non-linear PMT response, quantum mechanical probabilities, and the like. The image blurring or degradation is commonly expressed as a point spread function (PSF). 
     Restoration techniques, which have been proposed to improve the planar (2D) image resolution, employ classical inverse filters such as Wiener filter, count-dependent Metz filter, maximum entropy-based filter, power spectrum equalization filter and the like. However, the proposed inverse filtering techniques assume that the point spread function is depth independent, e.g., PSF is known and defined at certain depth. One of the restoration methods, an iterative constrained deconvolution, has been extended to employ an unknown point spread function or so called blind deconvolution to reconstruct the 3D SPECT images by using the projection information to get a correlation between projections. However, for the 2D nuclear medicine planar images, such correlation information does not exist. 
     The restoration of the nuclear medicine images is complex primarily due to the fact that the image blurring or a point spread function depends on the distance between the radiation decay event and the camera, e.g. it is depth dependent. The exact point spread function of the resulting image is typically unknown because the image is a compilation of information from different depths. Additionally, the depth of the organ of interest may vary from one study to another, and, therefore, it is difficult to predict the point spread function for a given study in the case of planar image. 
     The present invention provides a new and improved imaging apparatus and method which overcomes the above-referenced problems and others. 
     In accordance with one aspect of the present invention, a medical imaging system is disclosed. At least one radiation detection head is disposed adjacent a subject receiving aperture to detect radiation from a subject. A means reconstructs the detected radiation into at least one initial 2D projection image. An iterative constrained deconvolution means iteratively restores resolution of each initial 2D image with a plurality of system response functions which is each representative of a corresponding distance between the detection head and an origin of the detected radiation. 
     In accordance with another aspect of the present invention, a method of medical imaging is disclosed. Radiation data is detected from a subject. The detected radiation is reconstructed into an initial 2D image. Resolution in the initial 2D image is iteratively restored by a use of an iterative constrained deconvolution which applies a plurality of system response functions which functions correspond to distances between the detection head and an origin of the detected radiation. 
     One advantage of the present invention resides in increased spatial resolution. 
     Another advantage resides in reduction of noise in the reconstructed 2D images. 
     Another advantage of the present invention resides in increased accuracy of the reconstructed image. 
     Still further advantages and benefits of the present invention will become apparent to those of ordinary skill in the art upon reading and understanding the following detailed description of the preferred embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention may take form in various components and arrangements of components, and in various steps and arrangements of steps. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. 
         FIG. 1  is a diagrammatic illustration of an imaging system; 
         FIG. 2  diagrammatically illustrates point spread functions for three points; 
         FIG. 3  is a diagrammatic illustration of a portion of an imaging system; and 
         FIG. 4  is a diagrammatic illustration of another portion of an imaging system. 
     
    
    
     DETAILED DESCRIPTION 
     With reference to  FIG. 1 , a nuclear imaging system  10  typically includes a stationary gantry  12  that supports a rotatable gantry  14 . One or more detector heads  16  are carried by the rotatable gantry  14  to detect radiation events emanating from a region of interest or examination region  18 . Each detector head includes two-dimensional arrays of detector elements or a scintillator  20 . Each head  16  includes circuitry  22  for converting each radiation response into a digital signal indicative of its location (x, y) on the detector face and its energy (z). The location of an event on the scintillator  20  is resolved and/or determined in a two dimensional (2D) Cartesian coordinate system with nominally termed x and y coordinates. However, other coordinate systems are contemplated. A collimator  24  controls the direction and angular spread, from which each detector element of the scintillator  20  can receive radiation, i.e., the scintillator  20  can receive radiation only along known rays. Thus, the determined location on the scintillator  20  at which radiation is detected and the angular position of the camera  16  define the nominal ray along which each radiation event occurred. Due to the limited height of the collimator, the physical size of its aperture, physics of detection, and other system factors, there is uncertainty of the actual ray that traveled from the radiation event to the detector. The potential rays spread with distance from the detector with the positional uncertainty at any given distance being given by a point spread function (PSF). 
     Typically, an object to be imaged is injected with one or more radiopharmaceuticals or radioisotopes and placed in the examination region  18  supported by a couch  26 . Few examples of such isotopes are Tc-99m, Ga-67, and In-131. The presence of the radiopharmaceuticals within the object produces emission radiation from the object. Radiation is detected by the detector heads  16  which are able to be angularly indexed or rotated around the examination region  18  to collect the projection emission data at one or more selected projection directions. The projection emission data, e.g. the location (x, y) and/or energy (z), and an angular position (θ) of each detector head  16  around the examination region  18  (e.g., obtained from an angular position resolver  28 ) are stored in a data memory  30 . The projection data from each selected projection direction is conveyed to an image processor  40  which reconstructs a projection image, which is stored in a projection image memory  42 . 
     A 2D resolution restoration processor  44  restores 2D images, such as produced by total body bone scan, tumor scan, and other scans. As described in a greater detail below, the image is de-blurred or refocused by deconvolution. The blurring or a point spread function depends on the distance between the radiation event and the detector  20 . In  FIG. 2 , three point sources a, b, c are illustrated at different depths d 1 , d 2 , d 3 . The radiation data from the points b and c are collected in the same detector area. However, a point&#39;s b point spread function h b  is wider than a point&#39;s c point spread function h c . This illustrates that a point spread function is depth dependent, e.g. a distance d 2  from point source b to the detector  20  is larger than a distance d 3  from the point source c to the detector  20 . 
     With reference again to  FIG. 1 , the resolution restored 2D images are stored in a 2D image memory  48 . A video processor  50  processes the optimized 2D images for a display on an image display  52 . 
     In one embodiment, a 3D reconstruction processor  60  processes the 2D images from the 2D image memory  48  into volumetric image representations. The image representations are stored in a 3D image memory  62  for manipulation by the video processor  50  and displayed on the image display  52  such as a video monitor, printer, or the like. 
     With continuing reference to  FIG. 1  and further reference to  FIG. 3 , an iterative processor or process  70  applies an iterative constrained deconvolution algorithm or process to an original image μ by a use of point spread functions with a known depth. More specifically, a function means  72  determines a series of point spread functions h 1 , h 2 , . . . , h n  for different depths based on an input from an information database  74 . Preferably, the point spread functions h 1 , h 2 , . . . , h n  are determined based on at least one of the isotope administered to the object and the geometry of the collimator  24  which is used for imaging. Of course, it is also contemplated that a user can provide the information for determining the point spread functions h 1 , h 2 , . . . , h n  on the fly by using an input means  76 , such as keyboard, mouse and the like, of an operator interface station  78 . A fixed means or processor  80  applies an iterative constrained deconvolution algorithm or process (ICD) to the original image Ft for each individual point spread function (h 1 , h 2 , . . . , h n ) to generate a series of restored images λ 1 , λ 2 , . . . , λ μ . 
     Preferably, the fixed processor  80  implements a maximum-likelihood (ML) algorithm: 
                 λ     (     k   +   1     )       ⁡     (   i   )       =         λ     (   k   )       ⁡     (   i   )       ⁢       ∑   l     ⁢         h   ⁡     (     l   -   i     )       ⁢     μ   ⁡     (   l   )             ∑   z     ⁢       h   ⁡     (     l   -   z     )       ⁢       λ     (   k   )       ⁡     (   z   )                       
where h is a known point spread function,
     z, l represent the pixel indices for the image,   μ denotes the original image,   λ (k)  is the restored image at k th  iteration, and   i represents the i th  pixel in the image.   
     A terminating means  82  terminates the iterative process  70  based on a prespecified criteria such as a preselected number of iterations, a predefined average contrast in at least a selected region of the image, a predefined standard pixel deviation in at least a selected region of the image, and the like. 
     The image optimizing means  84 , preferably automatically, optimizes or selects the best image from the series of the restored images λ 1 , λ 2 , . . . , λ μ  using a predefined optimization resolution criteria, e.g., the images are optimized by maximizing the contrast-to-noise ratio, signal-to-noise ratio, or certain frequency components. The restored 2D image is stored in the 2D image memory  48 . 
     Of course, it is also contemplated that the optimizing is visually performed by the user. After the initial image is restored or refocused for each point spread function h, each of the restored images is stored in the 2D image memory  48 . The series of restored images, each corresponding to a different depth of optimal refocusing is displayed on the display  52 . Preferably, the user scrolls through the series to select his/her viewing preference. The display during this process is similar to focusing a microscope based on the depth of the part of the body it is desirable to observe. E.g., if the organs of interest are located close to the camera  16 , a point spread function with a small depth results in the image of a better quality; while in other situations, when the organs of interest are further away from the camera  16 , a point spread function with a large depth results in a better quality image. 
     With continuing reference to  FIG. 1  and further reference to  FIG. 4 , the iterative processor  70  applies the iterative constrained deconvolution algorithm to the original image μ by a use of an assumed or unknown point spread function h 1 . More specifically, the function means  72  estimates an approximation of the point spread function h 1 . Since the complete knowledge of the point spread function h 1  is unknown, a blind deconvolution means or process or processor  86  applies so called blind deconvolution to the original image μ, starting with the approximated point spread function h 1 . A redefine function processor or means  88  estimates the point spread finction while a redefine image processor or means  90  simultaneously restores the image. The terminating means  82  determines whether the restored image λ is of an acceptable quality by monitoring the blind deconvolution process  86 . In the preferred embodiment, the terminating means  82  determines one of whether the average contrast of the image is above a pre-defined threshold, a standard pixel deviation is above a pre-defined standard pixel deviation, and a preselected number of iterations have been performed. A resultant, optimally restored image λ is stored in the 2D image memory  48 . Preferably, the blind deconvolution means  86  enhances the contrast of the images without amplifying too much noise and/or without creating false artificial features. Such blind deconvolution process  86  is advantageous since there is no need to know the exact information of the point spread function. The blindly restored point spread function is more accurate than empirically measured one because it is free from noise contamination, and is based on the distortion actually present within the dataset, rather than when the point spread function was measured. As a result, the image restored using the blindly restored point spread function is more robust and statistically more accurate. 
     Preferably, the blind deconvolution process  86  implements a maximum-likelihood (ML) algorithm, in which the image λ and the point spread function h are estimated concurrently in each iteration: 
                 λ     (     k   +   1     )       ⁡     (   i   )       =         λ     (   k   )       ⁡     (   i   )       ⁢       ∑   l     ⁢           λ     (   k   )       ⁡     (     l   -   i     )       ⁢     μ   ⁡     (   l   )             ∑   z     ⁢         h     (   k   )       ⁡     (     l   -   z     )       ⁢       λ     (   k   )       ⁡     (   z   )                                   h     (     k   +   1     )       ⁡     (   b   )       =         h     (   k   )       ⁡     (   b   )       ⁢       ∑   l     ⁢           λ     (   k   )       ⁡     (     l   -   b     )       ⁢     μ   ⁡     (   l   )             ∑   z     ⁢         λ     (   k   )       ⁡     (     l   -   z     )       ⁢       h     (   k   )       ⁡     (   z   )                   ,         
where h (k)  is the estimated point spread function at k th  iteration;
     z, l represent the pixel indices for the image,   μ denotes the original image,   λ (k)  is the restored image at k th  iteration, and   i represents the i th  pixel in the image.   
     The deconvolution process has been described with reference to the entire projection image for simplicity of illustration. Of course, a single image can be optimized on a regional basis with each of plural regions being optimized to different point spread functions. In this manner a region with a shallow organ of interest and another region of the same image with a deep organ of interest can both be optimally and resolutionally restored. 
     The iterative constrained deconvolution technique can be used to restore the resolution in 3D images. In one embodiment, the iterative constrained deconvolution is applied to 3D images after 3D image reconstruction by using the 3D volumetric response function. In another embodiment, the iterative deconvolution is applied to each 2D projection before SPECT reconstruction. In yet another embodiment, the iterative deconvolution is incorporated into the 3D SPECT reconstruction process. 
     The invention has been described with reference to the preferred embodiments. Obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.