Abstract:
Systems, apparatus and methods for collecting, storing, processing, reconstructing, and interpreting raw scan data from a medical diagnostic imaging scan. Raw data after a scan such as a Computed Tomography (CT) scan is sent to a raw scan database system and image reconstruction system, where image volumes are reconstructed using software from the software bank and sent to a data management system. Raw scan data generated once by a scanner is continuously used at later times to reconstruct images of the patient without having to perform additional patient scans.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This invention is a divisional of and claims the benefit of priority to U.S. Non-Provisional patent application Ser. No. 13/797,457, filed Mar. 12, 2013, which claims priority to U.S. Provisional Application Ser. No. 61/658,406 filed Jun. 11, 2012, both of which are incorporated therein by reference in their entirety. 
    
    
     FIELD OF INVENTION 
     This invention relates to diagnostic imaging, and in particular to systems, apparatus and methods for collecting, storing, and analyzing raw scan data as well as raw scan data processing and image reconstruction algorithms and software used for medical diagnostic imaging, non-destructive material analysis, security and other imaging applications. 
     BACKGROUND OF THE INVENTION 
     In the medical industry it is well known to use technologies such as computer tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), ultrasound, conventional X-rays and various other technologies to diagnose the health of a patient. The equipment used to provide such imaging is generally very complex and expensive. For CT imaging, the equipment generally includes a CT scanner that collects raw CT data and proprietary software that reconstructs 3D images out of raw data utilizing scanner&#39;s various hardware components, including computers. A computed tomography (CT) method uses X-ray scan data to reconstruct detailed images of a body&#39;s interior structure. 
     Despite the fact that only raw CT data preserves all the medical information acquired during a scan, in practice, only the image data (results of reconstruction) are saved. Once raw data is deleted there is no way back to perform additional image reconstructions without a repeat scan. But, if raw data were saved, radiologists will be able to request retrospective reconstructions to more precisely zero in on specific regions of interest (ROI) without a repeat scan. 
     In addition, raw data from previous scans can be used for better planning of new scans and much more accurate monitoring of the treatment/disease progress. Availability of raw scan data to a wider group of image reconstruction professionals will also stimulate faster development and adoption of the next generation of reconstruction technologies. The same data sets can be used for testing and improving other new image reconstruction methods. 
     The saved raw CT data can also be used to more accurately determine the source of a medical problem, optimize treatment or disease monitoring and lead to a paradigm shift in medical imaging, improving medical care not just on the individual patient level but on the entire patient population level. 
       FIG. 1  shows a conventional prior art workflow of CT data acquisition and image reconstruction using software pre-installed on a scanner. 
     Referring to  FIG. 1 , the Scan Order  10  can be generated by a physician to assist with diagnosing or treating a medical issue or related issues. The scanner technician can perform a scan of a patient in accordance with the Scan Order  10  and certain scanner-specific pre-determined scan protocol. The protocol takes into account numerous patient-related variables to optimize diagnostic quality and minimize the amount of radiation exposure by the patient. 
     During the scan, the scanner  20  collects raw CT scan data, sufficient to perform a computed tomography (CT)-based 3D (three-dimensional) image reconstruction, and stores raw scan data temporarily on the scanner  20  which includes a computer with pre-installed data processing and reconstruction software and temporary CT data storage. 
     After the scan is complete, the processing and image reconstruction software preinstalled on the scanner  20  performs raw data processing followed by 3D image reconstruction. 
     The reconstructed 3D image volumes are temporarily stored on the scanner  20  and are also sent to the image repository called Picture Archiving and Communication System (PACS)  30  which also includes a computer for medium-term storage. The PACS  30  system as well as image visualization workstations connected to the PACS  30  can be accessed by physicians/radiologists to read and interpret the reconstructed images of a patient. Long-term storage (e.g., multiple years) of images can also be done. 
     Prior art exists in the area of image manipulation (or enhancement), which takes place after images have been reconstructed by the scanner system and raw data has been deleted. Such image-based image enhancement has inherent weakness over the raw data-based image reconstruction or raw data-based image enhancement. Many prior art patents in the area of imaging focus on specific algorithms for image manipulation rather than dealing with accumulation and re-inverting (re-imaging) of raw data acquired by a CT scanner/sensors to reconstruct a more accurate image of the body. 
     Various patents exist that touch on the idea of using previously generated images or downloading CT image data, but only in terms of using the data for their specific algorithm. See for example, U.S. Pat. No. 7,145,984 to Nishide et al.; 7,684I 589 to Nilsen et al.; U.S. Pat. Nos. 7,599,534 and 7,672,491 to Krishnan et al.; 7,7561 314 to Karau et al.; U.S. Pat. No. 7,860,286 to Wang and Jackson; U.S. Pat. No. 8,195,481 to Backhaus; which are each incorporated by reference in their entirety. 
     Nishide et al. &#39;984 describes a method to plan a scan in consideration of past patient exposures. The method includes a step of sampling information on a patient exposure the subject has received during a scan performed for reconstructing tomographic images, which is appended to each of the reconstructed tomographic images, on the basis of identification information with which the subject is identified; a step of creating a distribution of patient exposures calculated relative to an axis orthogonal to the scanning directions on the basis of the sampled information on the patient exposure (an estimated patient exposure, which is estimated in planning a scan, and an exposure limit); and a step of displaying the created exposure distribution. 
     The components  10 ,  20  and  30  of  FIG. 1  are generally covered in the above identified patents. Nilsen et al. &#39;589 describes a technique to accelerate the image reconstruction process by dividing one set into subsets, where “The raw image data is decomposed into N subsets of raw image data. N is based on a number of available image generation computer processors. The N subsets of raw image data are processed to create processed image data. The image generation processors perform image processing on the image data in parallel with respect to each other.” This patent describes “a method for increasing the performance of a system for processing raw image data via dividing it into smaller subsets of raw image data.” This patent would also be potentially obsolete in the future based on continuing advances in efficiency of algorithms and computing power would make this technology unnecessary. 
     Krishnan et al. &#39;534 and &#39;491 are described as being used for “processing a medical image to automatically identify the anatomy and view from the medical image and automatically assess the diagnostic quality of the medical image. In one aspect a method for automated decision support for medical imaging includes obtaining image data, extracting feature data from the image data, and automatically performing anatomy identification, view identification and/or determining a diagnostic quality of the image data, using the extracted feature data.” The methods described here are generally using an Image Database for performing feature analysis, anatomy/view identification and quality assessment of the imaged data (images) but not with raw CT data. 
     Karau et al. &#39;314 is designed for dealing with imaged data for acquiring images on an imaging system and performing accessing image data with a Computer-Aided Design (CAD) algorithm. 
     Wang et al. &#39;286 describes a medical image acquisition error detection technique which uses special characteristics of medical images to detect possible errors. In general, the presented technique categorizes medical images based on the type of images. When a medical image is to be examined for possible acquisition errors, it is categorized and a measure of difference between the image and the standard image for the category is computed. If the measure of difference falls outside the acceptable difference for the category, the image is deemed to contain an acquisition error and an alert is issued. 
     Backhaus &#39;481 describes a teleradiology image processing system to receive, process, and transmit radiology read requests and digital radiology image data. Here, a radiology processing system can include a series of processing components configured to receive digital radiology data from a medical provider, extract relevant information and radiology scan images from the digital radiology data, and initiate and control a workflow with a qualified remote radiologist who ultimately performs a read of the radiology scan images. Other techniques in this patent facilitate data processing within the image processing system in response to medical facility rules and preferences; translation or conversion of digital images to other formats; compilation of patient and medical facility data obtained from the digital radiology data into medical records or data stores; assignment of radiology studies within a teleradiology workflow in response to licensing and credentialing rules; and billing functions in response to completed reads by the remote radiologist. 
     Thus, the first key problem in the prior art workflows, including solutions currently utilized by the medical industry, is that the raw data are only temporarily stored on the scanner {i.e., a few days) or even deleted right after the image reconstruction process is finished. 
     Another problem of the current workflow is that the scanner operates like a “black box”, where no one except the scanner manufacturer has access to the raw scan data. As such, CT scanners are designed in such a way that it is impossible for anyone except the scanner manufacturer to take the raw data from the scanner to perform an additional image reconstruction with another, potentially superior or more customized, image reconstruction algorithm (software). 
     It is also impossible for third parties to independently install, potentially superior or more customized, image reconstruction software onto the scanner. 
     When there is a medical diagnosis needed to further scrutinize a particular target using computed tomography, because raw CT data has been deleted, there is no known reliable alternative which is not based on manipulation with images, but to repeat a CT scan, thereby exposing the patient to additional high doses of X-ray radiation. Not only is this re-image requirement detrimental to the health of the patient, but it requires additional costly system resources, medical personnel time, etc. 
     Moreover, a repeated scan is often performed on a different scanner, and the image reconstruction is often performed by different software than the one used after the first scan, and the reconstructed images are with different skills, experience, etc. 
     All these factors can accumulate and result in an inconsistency with the first result and even in an incorrect medical diagnosis. It would be much safer for a patient and more reliable from the medical point of view to resolve a possible issue by returning back to the saved raw data and perform CT reconstruction avoiding all or the majority of the factors mentioned above. On top of that, it would be greatly beneficial to the patients to have an option to perform a repeat reconstruction (of course, without a repeat scan) using the best available reconstruction software. This option is not available in the current art. 
     These problems not only result in radiation over-exposure of the patient population, but on a global level limit the full potential of medical imaging diagnostic quality and the pace of making available for doctors and patients novel imaging algorithms developed outside the walls of scanner manufacturer technology centers. 
     Solutions to the above problems are not contemplated by the scanner manufacturers or in the prior art. 
     Thus, the need exists for a new workflow and system that solve the above problems with the prior art. 
     SUMMARY OF INVENTION 
     A primary objective is to provide methods, systems, and apparatus for collecting, storing, and analyzing raw scan data for medical diagnostic imaging, non-destructive material analysis and security applications that can perform image reconstruction of an object even after raw scan data acquired during scanning of this object are deleted from the scanner after the image reconstruction step. 
     A secondary objective is to provide methods, systems, and apparatus for collecting, testing and storing raw scan data, processing and image reconstruction, and image interpretation algorithms and software for medical diagnostic imaging, non-destructive material analysis and security applications that can be use independently from the software preinstalled on a scanner by the manufacturer of the scanner. 
     A third objective is to provide systems, apparatus and methods for collecting, storing, and analyzing raw scan data for medical diagnostic imaging and other applications, where the scanner does not operate as a “black box”, where only the scanner manufacturer has access to the raw data. 
     A fourth objective is to provide systems, apparatus and methods for collecting, storing, and analyzing raw scan data for medical diagnostic imaging and other applications, which allows raw scan data from the scanner to be used for performing additional data processing, image reconstruction and interpretation steps with another, potentially superior or more customized, data processing, image reconstruction, and interpretation algorithms (software) developed by a third party. 
     A fifth objective is to provide systems, apparatus and methods for collecting, storing, and analyzing raw scan data for medical diagnostic imaging and other applications, which allows third parties to independently install, potentially superior or more customized data processing, image reconstruction and image interpretation software onto the scanner or connect a scanner with a third party server capable of performing data processing, image reconstruction, and image interpretation using raw CT data acquired by the scanner but using a third party software. 
     A sixth objective is to provide systems, apparatus and methods for collecting, storing, and analyzing raw scan data for medical diagnostic imaging and other applications, which allows for additional reconstructions needed to further scrutinize a particular target without exposing the patient to additional high doses of radiation by repeating a CT scan of a patient. 
     A seventh objective is to provide systems, apparatus and methods for collecting, storing, and analyzing raw scan data for medical diagnostic imaging and other applications, which allows for additional reconstructions needed to further scrutinize a particular target without requiring additional costly system resources such as additional CT scans. 
     A scanner-independent system for collecting and storing raw scan data of an object or objects and performing raw scan data processing, image reconstruction and reconstructed image interpretation can include a raw scan data transfer module located adjacent to at least one scanner, the raw scan data transfer module to transfer raw data from the at least one scanner onto a raw scan database system after scans are completed and a database for storing raw scan data in compressed or uncompressed formats sufficient for performing image reconstructions at any time after storing raw scan data, an uploading protocol for transferring raw scan data to the raw scan database from any scanner via data networks using a digital information transfer medium; and a data processing and image reconstruction and image interpretation system for providing image processing, image reconstruction and image interpretation from the raw scan data on any stored scan data set at any time. 
     The at least one scanner can be selected from at least one of: computed tomography (CT) scanner, magnetic resonance imaging (MRI) scanner, positron emission tomography (PET) scanner, an ultrasound scanner, and other types of scanners. 
     The system can further include the capability to receive via a digital transfer medium any number of additional image reconstruction orders to be performed on the raw scan data stored in the database, which allows the raw scan data to be processed and image reconstructed by the data processing and image reconstruction system to provide additional image reconstructed volumes therefrom, and results of image reconstruction are transferred to one or more external picture archiving and communication system (PACS) data management systems (PACS). 
     The scan order can be generated based on analysis performed on stored raw data sets on database system, further comprising performing a scan according to the recommended-by-the-system modified scan protocol, with images resulting from the image reconstruction transferred to an external picture archiving and communication system (PACS) data management system. 
     The raw scan database system can include software providing raw scan data compression and decompression, and computer and software providing capabilities for raw scan and image data storage locally, in a centralized global database, or using cloud computing on the Internet. 
     The data processing, image reconstruction, and image interpretation system can include software providing raw scan data processing and image reconstruction, software providing interpretation of reconstructed volumes from raw scan data and updating the global raw scan database system with such reconstructed volumes, and an artificial Intelligence software system generating a new scan protocol and optimized data interpretation workflow based on a submitted scan order and the available information on the subject of scan study in the raw scan database system. An example of application of an Artificial Intelligence-based system for improved healthcare is described in U.S. Pat. No. 8,396,804 to Dala et. al., which is incorporated by reference in its entirety. 
     The digital information transfer medium can be selected from at least one of: a WEB portal, DVD (digital video disc), CD (compact disc), portable drives, and other software carrier transfer medium. 
     A Software Bank system used with scan data processing, image reconstruction and image interpretation applications, can include a software development and trial system for any raw scan data processing, image reconstruction and interpretation software created by any developers, raw CT (computer tomography) data sets from the raw scan database system and testing procedures for performing trials of any scale of software candidates for the Software Bank, and a Web-enabled portal to apply for a software trial and transfer software modules to satisfy the qualification requirements of the Software Bank. 
     The software and trial system can include input and output data format standards required to be satisfied by any software applied for a trial, phantom data sets for initial test runs performed by an applicant, software generating a set of real raw scan data from the raw scan database to perform a trial, a queue trial system to manage a number of applicants, required computer power, and data storage volume, and software for an automatic assessment of quality and reliability of software tested during the trial. 
     Input and output data format standards includes but is not limited to a binary and DICOM formats. DICOM or Digital Imaging and Communications in Medicine is a standard for handling, storing, printing, and transmitting information in medical imaging. It includes a file format definition and a network communications protocol. 
     Phantom data sets for initial test runs includes but is not limited to only raw scan data of a selected medical phantom or phantoms of simple or human-body-type structures (scan objects). 
     Queue trial system, which is designed to optimize the use of CPU power and data storage of the trial system, includes but is not limited to a set of scripts automatically managing the trial system. 
     Software for automatic assessment of quality and reliability of imaging results includes but is not limited to software programs to compare images generated by a tested software program with the phantom structure, if it is tested on a phantom, or with the results of imaging generated by one or a number of previously tested and approved for medical use image software programs. 
     The software successfully passing through the software development and trial system can be uploaded to the Software Bank and then information about this software becomes available to any user worldwide via a Web-based system. 
     A method for collecting, storing, processing of raw scan data, and image reconstruction and interpretation used for medical diagnostic imaging for more than one scan order, can include the steps of ordering a scan of a patient with a scanner adapted for diagnosing or treating a medical issue, generating raw scan data collected by the scanner, generating a computer based three-dimensional (3D) reconstructed image using the raw collected data, transferring the reconstructed image to an external picture archiving and communication system (PACS), data management system for providing the reconstructed image, separately storing the raw scanned data generated by the scanner in a raw database storage system, transferring the raw scanned data from the raw database storage system to a data Processing, Image Reconstruction and Interpretation System (PIRIS) and reconstructing additional images at any time with the PIRIS, wherein the raw scanned data is able to be continuously used at later times without having to perform another patient scan. 
     The scanner type can be selected from at least one of: computed tomography (CT) scanner, magnetic resonance imaging (MRI) scanner, positron emission tomography (PET) scanner, an ultrasound scanner, and other imaging modalities allowing to retrieve raw scan data. 
     The method can include the steps of ordering a second scan for the same patient, transferring the second scan order to the raw CT (computer tomography) database system with a digital information transfer medium, transferring the raw scan data from the first scan order to the PIRIS, and generating a second image reconstruction order based on the raw scan data generated by the scanner from the first scan order, the second image reconstruction based on reconstructed volumes from the raw scan data, so that the second image reconstruction is sent to the external picture archiving and communication system (PACS) data management system. 
     The transfer medium being can be selected from at least one of: a WEB portal, DVD (digital video disc)1 CD (compact disc)1 portable drives, and other software carrier transfer medium, 
     The method can include the steps of providing software to the PIRIS from a software bank, for the image volumes to be reconstructed. 
     In a preferred embodiment that can use a CT scanner, the detector data are subjected to various preprocessing steps such as calibration, scatter correction, beam hardening correction, and the like before it can be used for image reconstruction. For the purposes of this patent application, the raw data (also referred to raw scan data or raw CT data) can mean data at any stage, i.e. before any preprocessing done and all the way up to the stage when all preprocessing is performed and the preprocessed data is ready to be used for image reconstruction. 
     Further objects and advantages of this invention will be apparent from the following detailed description of the presently preferred embodiments which are illustrated schematically in the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a fuller understanding of the invention, reference should be made to the following detailed description, taken in connection with the accompanying drawings, in which: 
         FIG. 1  is a prior art workflow of CT data acquisition and image reconstruction with scanner pre-installed software. 
         FIG. 2  shows a scanner-independent, stand-alone workflow system providing raw CT scan data-based image reconstruction performed automatically in parallel with the scanner&#39;s system, without impacting the operational workflow of the scanner or scan protocol. Raw scan data are stored in the raw CT database system (ROBS) and processes are performed using software from the data Processing, Image Reconstruction and Interpretation System (PIRIS). 
         FIG. 3  shows a modified scanner-independent, stand-alone workflow system used in  FIG. 2  that also allows a user to select software pre-installed on the data Processing, Image Reconstruction, and Interpretation System (PIRIS) to be ran in parallel with the scanner&#39;s system and without changing the conventional operational workflow of the scanner or scan protocol. 
         FIG. 4  shows a modified scanner-independent, stand-alone workflow system used in  FIG. 3  that also allows a user to select software pre-installed on the data Processing, Image Reconstruction, and Interpretation System with a new scan protocol guiding scan performance. 
         FIG. 5  shows a modified scanner-independent, stand-alone workflow system used in  FIG. 4  with an additional web-based option permitting the system administrator controlling the global raw CT scan database system and the Software Bank to test and then install new software onto the software bank. Selected software modules from the software bank can then also be installed onto the scanner. The word global throughout this patent application references that the corresponding database is connected to the Internet. 
         FIG. 6  shows a modified scanner-independent, stand-alone workflow system used in  FIG. 5  additional web-based option permitting external users to consult with the global raw CT database system. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Before explaining the disclosed embodiments of the present invention in detail it is to be understood that the invention is not limited in its applications to the details of the particular arrangements shown since the invention is capable of other embodiments. Also, the terminology used herein is for the purpose of description and not of limitation. 
     A list of the components in the figures will now be described. 
     1. Workflow steps of prior art scan order ( 10 ), scanner ( 20 ) and data management ( 30 ). 
     
         
           10 . Scan order. 
           20 . Scanner with pre-installed data processing and image reconstruction software and a temporary CT data storage. 
           30 . Data Management—Picture Archiving and Communication System (PACS) or any other image visualization and/or storage platform. 
           100 . Automatic scanner-independent, stand-alone workflow system. 
           21 . Raw CT scan Data Transfer Module (DTM). 
           105 . Raw CT Data. 
           106 . Raw CT data compression software module. 
           110 . Raw CT Database System (RDBS). 
           111 . ROBS request and decompressed raw CT data. 
           120 . Data Processing, Image Reconstruction, and Image Interpretation System (PIRIS). 
           121 . Request to consult with database systems (RDBS) and/or upgrade them. 
           130 . Reconstructed Image Volumes. 
           200 . Workflow of Embodiment  100  with Additional Reconstruction Order without changing scan protocol. 
           210 . Reconstruction Order. 
           115 . Raw CT data decompression. 
           300 . Workflow of Embodiment  200  with Additional reconstruction order and/or changing scan protocol. 
           310 . Reconstruction order and/or protocol change. 
           400 . Workflow of Embodiment  300  Web upload steps. 
           112 . Global Raw CT Database System (GRDBS). 
           410 . CT system Web portal to upload data and software. 
           415 . Uploading of new raw CT data sets onto GRDBS. 
           417 . External special requests to consult with GRDBS. 
           420 . Software Trial and QC systems. 
           430 . Software Bank. 
           440 . Installation of a software module from the software bank onto a scanner. 
           500 . Workflow of Embodiment  400  permitting an external user to request consulting with the GRDBS (i.e., with the global database of raw CT data, processed CT data, and CT imaged data/images) created and constantly expanded using raw CT data from any number of CT scanners. 
       
    
     First Embodiment 
       FIG. 2  shows a scanner-independent, stand-alone workflow system  100  providing raw CT data image reconstruction in parallel and/or simultaneously with image reconstruction performed by the software preinstalled on the scanner, without impacting the operational workflow of the scanner or scan protocol. 
     Here, the workflow for raw CT Database System (RDBS) and Data Processing, Image Reconstruction, and Image Interpretation System (PIRIS) use alternative data processing and image reconstruction software modules installed on an external server. This workflow does not require changing a scan protocol and therefore does not require an additional order from a physician generally required in the prior art workflow system of  FIG. 1 . 
     Raw CT data are transferred from the scanner  20  using the Data Transfer Module  21 , which provides wireless/wire-based data transfer from the CT scanner  20  to the ROBS  110 . During this step raw CT data can be compressed  106  to reduce storage space on the RDBS. 
     Referring to  FIG. 2 , there can be the following steps required as described below. 
     In a first step (1a), Scan Order  10 , a physician orders a scan, which includes at least one of: computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, and the like, and can be used to assist with diagnosing or treating a medical issue or related issues in a similar manner as referenced in the prior art. 
     The second step, (2a), includes Setup and Scan  20 . The Scan can be performed in accordance with the Scan Order (1a) and a scanner and scan-specific protocol. The scan-specific protocol is based on the initial Scan Order from the physician. 
     A third step, (3a), can occur in the Raw CT Database System  110 . Immediately after the scan is performed or at a later time, the raw CT data are transferred from the scanner  20  using the Data Transfer Module  21 . The raw data  105 , which includes scanner information, protocol of the scan, and other related patient and scan data acquired before and during the scan and sufficient to perform data processing and image reconstruction, can be automatically compressed ( 106 ) to reduce storage space on the RDBS  110  and transmitted to and stored on the Raw CT Database System (ROBS)  110 , which includes hardware and database management software for local and/or cloud-based storage and organization of Raw Data. 
     An example of a system that is partially similar to the “RAW CT DATABASE SYSTEM”  110  that includes the hardware and software for this computer system can be found in at least U.S. Pat. No. 7,436,924 B2 by Takahashi et al., which is incorporated by reference. The U.S. Pat. No. 7,436,924 B2 by Takahashi et al. does not contemplate using the data base for the purposes of giving access to the data to third parties, using the data for later reconstruction by other algorithms, for improving the quality of healthcare by means including but not limited by the monitoring of the treatment of the patient, reducing the number of repeat scans, and the like. Another difference between the present invention and patent U.S. Pat. No. 7,436,924 B2 by Takahashi et al. is connecting the database to the Software Bank that can be used for image reconstruction. 
     The RDBS (Raw CT Database System)  110  can include, but is not limited to, raw CT data sets, scanner information, de-identified patient information, unique RDBS patient ID to be used to retrieve past CT scans of the patient, other medical information and/or data required to perform image reconstruction at any time without needing any additional information from other sources of information. Raw scan data can be stored in the original uncompressed form or in compressed form. RDBS  110  can also store the original reconstructed volumes generated by pre-installed on the scanner image reconstruction software. 
     The above set of data and scan information can be fully sufficient and can be stored for an indefinite amount of time. It is assumed that RDBS  110  is being backed-up on a regular basis and access to the system is not allowed to any unauthorized personnel to preserve RDBS integrity and confidentiality. 
     For the purposes of subsequent use, the database  110  should provide a way to establish a connection between patients&#39; data (for example: raw CT data, reconstructed images, and the like) stored in the database and the patients&#39; personal identifying information. For example, each data set in the database can be given a unique identifying number, and a separate database may contain the collection of these identifying numbers and the corresponding patient personal data such as social security numbers. 
     The Raw Data  105  can be stored for any period of time, such as but not limited to 7 years or longer, if patients and/or hospitals made special requests and/or paid for an extended storing time. 
     A fourth step (4a) occurs in the Data Processing, Image Reconstruction, and Image Interpretation System (PIRIS)  120 . Raw CT Data  105  can be first compressed ( 106 ) and then stored or stored in uncompressed form in the RDBS ( 110 ) and then automatically uploaded to the PIRIS System  120 , and the required steps as per RDBS request  111 , including image reconstruction are performed, utilizing the software pre-installed on the PIRIS System  120 . As many data processing, image reconstruction, and data analysis and interpretation runs can be performed as needed, utilizing, for example, various image reconstruction algorithms and other relevant software and input data. If necessary, these runs can all be performed simultaneously. The software on the PIRIS System  120  is regularly updated, tested, and new software modules are added as needed and/or when become available. 
     An example of a system having some elements partially similar to “DATA PROCESSING, IMAGE RECON AND INTERPRETATION SYSTEM (PIRIS)”  120  can be found in at least U.S. Pat. No. 8,134,571 B2 to Krishnan et al., which is incorporated by reference in its entirety. The significant difference between the present invention and the U.S. Pat. No. 8,134,571 B2 to Krishnan et al. is that the latter is dealing with image processing only, whereas the present invention deals with image reconstruction from raw data. By image processing we mean the manipulation of images that are of diagnostic value by themselves. 
     Additional step (4b) is a continuation of step (4a) from  FIG. 2 . A Data Interpretation component is added to the Image Reconstruction System  120  (collectively, “Data Processing, Image Reconstruction and Interpretation System, PIRIS”,  120 ), which can be consulted by the physician in step (1c) to provide data helpful for determining the type of software and protocol to be utilized for the particular patient as well as additional information that can be obtained by utilizing the Raw CT Database and the software on the Image Reconstruction and Interpretation System. 
     An example of a system that is partially similar to the PIRIS system  120  can be found in U.S. Patent: U.S. Pat. No. 7,436,924 B2 by Takahashi et al. which is incorporated by reference in its entirety. The U.S. Patent U.S. Pat. No. 7,436,924 B2 by Takahashi et al. does not contemplate using the data base for the purposes of giving access to the data to third parties, using the data for later reconstruction by other algorithms, for improving the quality of healthcare by means including but not limited by the monitoring of the treatment of the patient, reducing the number of repeat scans, and the like. Another difference between the present invention and U.S. Pat. No. 7,436,924 B2 by Takahashi et al. is connecting the database to the Software Bank that can be used for image reconstruction. 
     Raw CT Data Processing, Image Reconstruction, and Interpretation System (PIRIS)  120  can include but are not limited to various algorithms that can be utilized individually or according to a predetermined workflow. In order to be used in the course of medical care, and utilized by the PIRIS system, the individual software modules and workflows are assumed to be tested and passed the FDA and other relevant regulatory approval processes that might be required in the future. Some algorithms, which can be part of PIRIS, are described in the above referenced U.S. patents to at least U.S. Pat. No. 7,436,924 B2 by Takahashi et al. and U.S. Pat. No. 7,684,589 B2 by Nelsen et al., which are incorporated by reference in their entirety. 
     Other examples of algorithms which can be part of PIRIS are described in U.S. Pat. Nos. 5,539,800; 5,550,892; 5,717,211; 7,590,216 all to Katsevich which cover local tomography, which in their entirety are all incorporated by reference, and U.S. Published Patent Application 2012/0128265 to Silver et al. entitled: Method and System Utilizing Iterative Reconstruction with Adaptive Parameters for Computer Tomography (CT), which is also incorporated by reference in its entirety. 
     A fifth step (5a) is the storage and transmission of reconstructed images. The image volumes reconstructed in step (4a) are stored on the Raw CT Database System (RDBS)  110 , previously described, and are also transmitted to an image repository called Picture Archiving and Communication System (PACS)  30  and/or other visualization workstations or image storage media. 
     Reconstructed image volumes mean reconstructed 3D structure of an object under study for which a CT scan has been ordered and then raw CT scan data have been acquired and processed. Reconstructed image volumes allow evaluating the whole patient&#39;s body or a certain Region of Interest (ROI) of a patient. 
     The physician is notified immediately (via all possible electronic notifiers) that the images have been reconstructed and are ready to be viewed and analyzed. Electronic notifiers can include but are not limited to automatic email message, text message, telephone call, and the like. 
     Second Embodiment 
       FIG. 3  shows the scanner-independent, stand-alone workflow system  200  of  FIG. 2  which allows a system user to select the software from the list of pre-installed on the PIRIS System  120  modules, previously described. Here, workflow for RDBS  110  and PIRIS System  120  can use data processing, image reconstruction and interpretation software modules installed on an external server computer. 
     This workflow does allow applying an alternative scan protocol simultaneously with the protocol utilizing a pre-installed on the scanner image reconstruction software and therefore it requires an additional order from a physician. 
     Referring to  FIG. 3 , an additional step (1b) can occur in the Scan Order  10 . As a continuation of (1a) from  FIG. 2 , a physician orders a scan (for example, CT, MRI, PET, ultrasound, and the like) to assist with diagnosing or treating a medical issue or related issues and orders specific software module(s) to be utilized in performing image reconstruction (without impacting the scanner&#39;s workflow). This additional Reconstruction Order (see left arrow  210  in  FIG. 3 ) can be ordered at the time of the original scan or at any later date if the Raw Data are saved in the computer system of the Raw CT Database System. 
     Currently, doctors have to use the software that comes with the scanner, but by using the ROBS and PIRIS systems, physicians will have freedom to choose the best image reconstruction algorithm (software) available at the time of the scan. 
     Specific software modules can include but are not limited to for example, local tomography to one of the inventors of the subject invention. See for example, U.S. Pat. Nos. 5,539,800; 5,550,892; 5,717,211; 7,590,216 all to Katsevich which cover local tomography, which in their entirety are all incorporated by reference. See for example, U.S. Published Patent Application 2012/0128265 to Silver et al. entitled: Method and System Utilizing Iterative Reconstruction with Adaptive Parameters for Computer Tomography (CT), which is also incorporated by reference in its entirety. 
     Also, doctors will be able to request retrospective reconstructions to more precisely zero in on specific areas of interest without a repeat scan. Further, raw data from previous scans can be used for better planning of new scans and much more accurate monitoring of the treatment progress or disease development. 
     Third Embodiment 
       FIG. 4  shows a modified scanner-independent, stand-alone workflow system  300  used in  FIG. 3  that allows a user to select software pre-installed on the computer of the PIRIS System  120  with a new scan protocol guiding scan performance. 
     Referring to  FIG. 4 , workflow  300  for the raw CT Database System (RDBS)  110  and PIRIS system  120  uses alternative software installed on an external server and alters the scan protocol and consults with the data analysis expert system of PIRIS. 
     Additional step (1c) occurs in the Scan Order  10  as a continuation of step (1b) from  FIG. 3 . Rather than selecting the software pre-installed on the scanner  20  and the related protocol (as shown and described in  FIG. 2  and  FIG. 3 ), the physician can elect to modify the scan protocol in accordance with medical requirements to assess patient&#39;s medical condition or a treatment and using the additional capability provided by the software in the Software Bank that is not provided by the software on the scanner. 
     The result can be that the scanner  20  does not perform image reconstruction or that the images reconstructed on the scanner  20  are not of diagnostic quality to investigate a particular medical condition or perform a detailed treatment analysis. The additional step outlined in  FIG. 3  which include step (1b) can be applicable here as well. 
     An example of application of an alternative algorithm can be based on Local Tomography. See for example, U.S. Pat. Nos. 5,539,800; 5,550,892; 5,717,211 7,590,216 all to Katsevich which cover local tomography, which in their entirety are all incorporated by reference. 
     With superior edge detection, the alternative algorithm provides the potential for enhanced diagnostic accuracy and be applicable to many areas in the body including heart, brain, spine, liver, and lung. Disease detection should be enhanced because of the edge enhancement characteristics of the local tomography algorithm. Moreover, with the full implementation of collimator assisted local scanning, radiation dose can be significantly reduced. This is an example of additional capability that could be provided by software in the software bank. In other words, the physician can elect to use collimators knowing that there is a code in the software bank that can reconstruct a diagnostic-quality image from such a lower radiation CT data set. 
     Image reconstruction software can be run using CPU (central processing unit) or GPU (graphics processing unit) types of computer processors that are referenced in for example in U.S. Pat. No. 7,684,589 B2 to Nilsen et al.; U.S. Pat. No. 7,145,984 B2 to Nishide; and U.S. Pat. No. 8,314,796 to Pratx, et al; which are all incorporated by reference in their entirety. 
     Fourth Embodiment 
       FIG. 5  shows a modified scanner-independent, stand-alone workflow system  400  used in  FIG. 4  with an additional web-based option  410  permitting the user to request a software trial  420  of new software and, if approved, it can be loaded onto the Software Bank  430 . Another additional web-based option  415  permitting an external user to upload raw CT data sets via a web portal  410  connected to the Global Raw CT Database System (GRDBS)  112 . 
     Here, the new workflow for global raw CT Database System and Image reconstruction systems  400  uses an alternative software installed on an external server computer. This workflow allows the alteration of a scan protocol via consulting with PIRIS and uses software from the Software Bank  430 . Referring to  FIG. 5 , step 4c allows for the Data Processing, Image Reconstruction and Interpretation System  120  to be connected to a repository of software (“Software Bank”)  430 . 
     Software from the Software Bank  430  can be installed on the Data Processing, Image Reconstruction and Interpretation System (PIRIS)  120  as part of previously described step (1b) and step (1c) utilizing a Web portal  410  or by other electronic technique. Other electronic techniques can include but are not limited to software carrier media, e.g., DVD, portable drives, and the like. 
     Under additional step (6a) prior to being uploaded into the Software Bank  430 , software passes through trials utilizing the Raw Data  105  from the GRDBS  112  (collectively, “Software Trial System”)  420 . 
     Software from the Software Bank  430  can include but is not limited to local tomography, iterative reconstruction, and other data processing, 3D image reconstruction algorithms. Examples of Local Tomography-based and Iterative Reconstruction algorithms are referenced in, for example, in U.S. Pat. No. 7,590,216 B2, to Katsevich, and U.S. Published Patent Application 2012/0128265 to Silver et al. entitled: Method and System Utilizing Iterative Reconstruction With Adaptive Parameters For Computer Tomography (CT), respectively, which are all incorporated by reference in their entirety. 
     The software should be required to pass regulatory approval (for example, FDA U.S. Food and Drug Administration) as well as other quality control steps to ensure compliance with system&#39;s performance standards. Access to the Software Trial System can be obtained via a Web portal  410  or by other electronic techniques, such as but not limited to software carrier transfer medium, such as but not limited to, DVD (digital video disc), CD (compact disc), portable drives, and the like. 
     Quality control steps include but are not limited to software testing procedures using predetermined phantom and raw CT data sets from the GRDBS to satisfy pre-determined performance standards that can be based on but not limited to tests for speed and accuracy of data processing, image reconstruction and analysis software. 
     Alternatively, new software modules can be uploaded from the Software Bank and installed onto the scanner  440 . 
     Regulatory government approval can include but is not limited to FDA (U.S. Food and Drug Administration) approval, and other government approval and the like. The described above part of this embodiment, in which additional software is installed on the scanner, has the advantage of eliminating potential time delays associated with transferring data from the scanner to the database and then to the reconstruction engine. 
     An Artificial Intelligence (AI) system can also be a part of the computer GRDBS  112 , previously described. The AI system preselects the best algorithm based on the type of scan, patient data, and the like, and suggests to the radiologist what additional software might be needed in every particular case. For example, to minimize delay, the reconstruction engine might reconstruct different volumes prior to the physician ordering these reconstructions. Thus, if the physician wants to see several images, they will be instantaneously available. For example, if the AI system knows that the patient is overweight, this means that the CT data are noisier, and therefore the system will run an iterative reconstruction algorithm, which is more stable with respect to noise in the data. Another example of application of an Artificial Intelligence-based system for improved healthcare is described in U.S. Pat. No. 8,396,804 to Dala et. al., which is incorporated by reference in its entirety. 
     Fifth Embodiment 
       FIG. 6  shows a modified scanner-independent, stand-alone workflow system  500  used in  FIG. 5  with an additional web-based option  417  permitting an external user to request consulting  112  with the GRDBS (i.e., with the global database of raw CT data, processed CT data, and CT imaged data/images) created and constantly expanded using raw CT data from any number of CT scanners. 
     Such a consulting with the GRDBS can be used, for example, in the regular course of medical care for improving of treatment, early detecting or predicting of diseases, improving and testing of new reconstruction and/or interpretation methods, selecting of the best method for a specific disease, and the like. To maintain image reconstruction consistency, and therefore providing a more reliable reply on a request from an external user, image reconstructions of a number of raw CT data sets from the 
     GRDBS, which are required to be performed to generate such a reply, can be based on the same (unique) image reconstruction algorithm. Imaging process can be based on a single-type of reconstruction algorithm (For example, a Filtered-Backprojection-type algorithm (FBP)). Examples of these algorithms can be found in at least U.S. U.S. Pat. Nos. 6,574,299; 6,771,733; 6,804,321; 6,898,264; 7,010,079; 7,197,105; 7,242,749 to Hsieh et al; U.S. Pat. Nos. 7,280,632; and 7,305,061 all to Dr. Katsevich and U.S. Pat. No. 7,242,749 to Hsieh et al, that are all incorporated by reference in their entirety. 
     Additionally image processing can be based on a joint/sequential application of reconstruction algorithms (e.g., Local Tomography and Iterative Reconstruction). See for example, U.S. Pat. Nos. 5,539,800; 5,550,892; 5,717,211; 7,590,216 all to Katsevich which cover local tomography, which are all incorporated by reference in their entirety. See for example, U.S. Published Patent Application 2012/0128265 to Silver et al. entitled: Method and System Utilizing Iterative Reconstruction With Adaptive Parameters For Computer Tomography (CT), which incorporated by reference in its&#39; entirety. 
     To assess quality of reconstruction, a comparison of two or more reconstructed 3D images of an arbitrary property (not necessarily density, the other one is gradient-like density computed by local tomography, etc.) can be done using but not limited to digital manipulation with images, e.g., subtraction, division, calculation of derivative, integration, and the like. 
     The invention can include software for automatically assessing quality and performance of software tested during the trial. 
     Image quality can be defined as the absence of significant image artifacts, high spatial resolution, low noise level, and the like. 
     Performance can include but is not limited to computational efficiency (minimum computational operation steps to run through the software), CPU power, and the like. The fewer iterations, the more efficient is the software. 
     Quality of software is characterized by producing images with good quality, for example, good resolution and contrast, and the like, described above. 
     Performing a fast clinical trial of a new image reconstruction algorithm can be performed using the GRDBS and PIRIS systems. 
     Other embodiments of the invention are possible. For instance, the raw CT database can contain a module that converts raw data collected by scanners from different manufacturers in different formats into a universal format that can be easily read by all third-party software developers. The advantages of this module are manifold. The company that develops a conversion module can execute non-disclosure agreements with the CT manufacturers and get information about their raw data formats under the agreement. Thus, third-party developers will only be able to see the standard uniform format and the proprietary data formats of the CT manufacturers will not be compromised. Another advantage is that if the standard format is published and well-advertised, this will spur innovation since third-party developers will not have to worry about reading data in multiple formats from different CT manufacturers. 
     While most of image reconstruction is currently done in 3D, there can be some applications in which two-dimensional (2D) images are of interest. Thus, there can exist embodiments that deal with 2D and/or 3D image reconstruction. Similarly, imaging can be performed in (four dimensional) with time being the additional dimension, and so the database should also have capabilities for storing and processing of 4D data. 
     While normally one can expect a raw scan data transfer module to be located adjacent to at least one scanner, by utilizing an intranet of a hospital, one can get access to the raw data even if the transfer module is not located adjacent to any scanner. 
     While the invention has been described, disclosed, illustrated and shown in various terms of certain embodiments or modifications which it has presumed in practice, the scope of the invention is not intended to be, nor should it be deemed to be, limited thereby and such other modifications or embodiments as may be suggested by the teachings herein are particularly reserved especially as they fall within the breadth and scope of the claims here appended.