Paper Carlos Pérez - Imaging Biomarkers Automated Structured Document Transcript
Imaging Biomarkers Automated Structured Assembly Pipeline (IB-ASAP)C. Pérez-Castillo1*#, A. Pomar-Nadal, 2*#, G. García-Martí3*#, A. Alberich-Bayarri4*#, R. Sanz-Requena5*#, L. Martí- Bonmatí6*#+. * Department of Radiology, Hospital Quirón Valencia, Avda. Blasco Ibáñez, 14, 46010 Valencia, Spain. # Consorcio cvREMOD, Programa Cenit-e 2009-2012, Ministerio de Ciencia e Innovación, Madrid, Spain 1 email@example.com 2 firstname.lastname@example.org 3 email@example.com 4 firstname.lastname@example.org 5 email@example.com + Department of Medicine, University of Valencia, Valencia, Spain. 6 firstname.lastname@example.orgAbstract Results: The developments have provided an innovative service that follows an organized process, as a proper technologicalPurpose: To include imaging biomarkers in the radiological support to leverage the usability and ease the development andworkflow, providing additional quantitative information to implementation of quantitative imaging. In addition, theradiologists in order to friendly obtain more accurate diagnosis. software is fully automated, vendor independent and compatible with DICOM standards.Materials and Methods: Imaging biomarkers define objectivecharacteristics that are related to normal biological processes, Conclusion: Imaging biomarkers help establishing the presencediseases, or the response to treatment. Their implementation is of a lesion before it becomes evident, assess the predisposition tochanging the concept and workflow of radiology today. By suffer it, measure its biological situation, define its progress andapplying new modeling techniques and computational evaluate treatment effects. The platform quickly incorporates allprocedures to medical images, a set of quantitative parameters is these advantages into the radiological workflow.obtained. This quantitative information provides accurate andreproducible measures of various processes in individualpatients. Their potential to display and measure a wide range of I. INTRODUCTIONbiological and physiological situations, and their non invasive The workspace of radiologists and medical imagingnature, makes imaging biomarkers one of the most activeresearch fields. specialists has changed with the development andAn automated post-processing platform was developed in order implementation of digital imaging. The viewing, processingto implement imaging biomarkers in the radiological workflow. and properties extraction from medical images are some of theThe post-processing algorithms quantify biochemical, cellular parcels of medicine where innovation is most visible.and structural levels that indicate the presence and magnitude of In just a few decades, the use of magnetic resonancedifferent conditions and diseases. For instance: indicators of imaging (MRI) scanners has exponentially grown. Cliniciansneovascularization in cancer processes (such as prostate cancer, can demand MRI scans to help diagnosing multiple sclerosis,hepatic focal lesions, breast cancer, brain tumors), trabecular brain tumours, tendonitis, cancer and strokes, to name just abone structure studies on osteoporosis, studies of cartilage few. An MRI scan is one of the best methods for the in-vivodegeneration in osteoarthritis, studies of connectivity, volumeand morphometry in neurodegenerative diseases and studies of examination of the human body without opening it. Modernmorphology and function of the cardiovascular system. MRI equipments provide non invasive, highly accurateThe platform stores the results in a database and generates anatomic images and have an excellent spatial resolution thatstructured reports that are sent to the PACS. These post- allows to visualize internal structures in detail and to defineprocessing reports provide very useful quantitative information their main properties. The high quality of MRI images helpto the radiologist for the diagnosis. radiologists to classify diseases by analyzing morphological,The platform software is implemented in Java programming structural and physical properties.language using the open-source NetBeans IDE. Post-processing On the other hand, the current high capacity of computersalgorithms are programmed in Matlab and results are stored in a can be exploited to improve the quality and to extractMySQL database. The only hardware requirement is aworkstation connected to the hospital network. information from medical images by means of advanced post- processing algorithms. As a result of the synergy between digital imaging and computer processing, new imaging
biomarkers are being developed to provide quantitative Biomedical Engineering Knowledge, is defining newinformation that cannot be a priori detected or measured by radiological workflows.the visualization of the original medical images . The adequate technological support required to integrate Imaging biomarkers are objective characteristics extracted the use of imaging biomarkers in a radiology service isfrom medical images that act as indicators of normal described in this work. The presented Biomarkers Workflowbiological processes, diseases or responses to therapeutic implements the entire methodology, from the imageinterventions . This quantitative information is obtained acquisition to the generation and storage of post-processingbefore a lesion or biological process becomes evident in the reports, making the whole process much more efficient andradiological observation, by analyzing properties and simple.multivariate combination of medical images and data. Thisprocess requires careful monitoring of acquisition,normalization of data and image preparation, data extraction, II. MATERIALS AND METHODSanalysis and visualization of results. Their enormous potential The radiological workflow and the image-based clinicalto display and measure a wide range of biological and practice are tightly related to the PACS (Picture Achiving andphysiological situations, and their non invasive nature, makes Communication System) and the DICOM (Digital Imagingimaging biomarkers one of the most active research fields. and Communications in Medicine) standard.Some examples of imaging biomarkers pictures are shown in A PACS is a hospital computer system that managesFig. 1. acquisition, transmission, storage, distribution, display and interpretation of medical images. Medical digital images format is defined by DICOM standard , which facilitates the exchange of clinical cases and studies between different organizations. A DICOM file encapsulates the image within a structure that includes a data header, which contains relevant information such as patient data and parameters of image acquisition. This information is indexed by pairs of numbers called tags, to be managed and operated by hospital information systems (see Fig. 2). Fig. 2 Dicom header example Besides defining the file format, DICOM includes a network communication protocol that uses TCP/IP (Transfer Control Protocol / Internet Protocol). Thus, DICOM files can be exchanged between two DICOM-compatible entities. This data exchange is managed by several DICOM services: • Dicom Store: It is used to send images and structured Fig. 1 Examples of pictures extracted from imaging biomarkers reports to a PACS or workstation. quantification. From left to right: tractography, morphometry analysis, aortic • Storage Commitment: It is used to confirm that an imageflow, cardiac study, knee pharmacokinetics and jaw mechanical quantification. has been permanently stored by a device. The user (modality, workstation, etc.) uses the confirmation of the The use of imaging biomarkers opens the field of medical storage station (service provider) to ensure that dataimaging to other disciplines such as engineering and physics. exchange was properly done.This multidisciplinary interaction, included in the area of • Query/Retrieve: It allows a workstation to search for images in a PACS and retrieve them.
• Other services: Dicom Worklist, Modality Performed A. Data Reception Procedure Step, Dicom Print, etc. Firstly, input DICOM images reach the platform DICOM node in three ways: from external devices (CD, DVD, USB In order to include imaging biomarkers in the radiological storage), from the PACS or other DICOM storage stations byworkflow, a post-processing platform  has been completely query/retrieve service via the network, or directly fromintegrated in the hospital network. It receives DICOM images imaging devices (MRI, Computerized Tomography).from the hospital PACS or any other storage media, and sendscomplete reports containing the post-processing results to the B. Medical Images StoragePACS. While being received by the platform DICOM node, medical images are automatically stored in a directory tree structure (according to information extracted from their DICOM headers) and optionally transformed from DICOM to other formats that facilitate computer processing (ANALYZE or NIFTI). For each image sequence, a .txt file is created inside the series folder containing the following information: number of images, patient position, number of temporal positions, echo times, diffusion B-Values and further header information. C. Notifications The platform sends an e-mail alert to the users to indicate that a new dataset has arrived to the pipeline, and then allows selecting and launching the proper post-processing algorithm depending on the study type extracted from DICOM headers. D. Post-processing Algorithms Execution Post-processing algorithms quantify biochemical, cellular Fig. 3 Radiological workflow and biomarkers improvement and structural levels of patients and aid early diagnosis, assessment of prognosis, definition of therapeutic options and The radiological workflow and the way the imaging evaluation of treatment effectiveness.biomarkers complement it, providing additional quantitative Some examples of post-processing studies are:information to the radiologist for the diagnosis is observed in Morphometry  and volumetry analysis, functional studies,Fig. 3 . finite element mechanical simulations , spectroscopy The IB-ASAP data pipeline is shown in Fig. 4: profile, diffusion [8, 9], perfusion, quantification of water-fat- iron, flow dynamics quantification , fiber tracking studies, pharmacokinetic models , image correlation with genetic profile, studies of texture and physical properties and multimodal studies. After start running, post-processing algorithms prompt messages that ask for user interaction when required (processes are automated to require minimal user interaction). The interaction is centralized and managed in the post- processing platform interface. E. Post-Processing Results Management Final post-processing results (i.e. imaging biomarkers quantification), including multi-parametric images and data, follow two paths: 1) DICOM Structured Reports: Data is embodied in structured reports  that are sent to the PACS, providing Fig. 4 IB-ASAP data pipeline very useful complementary quantitative information to the radiologist for the diagnosis. These reports are automatically created by using predesigned HTML (HyperText Markup IB-ASAP consists on several steps that follow an organized Language) templates, customized for every post-processingprocess: data reception, medical images storage, notifications, workflow. After being filled with the required data, thepost-processing algorithms execution and post-processing HTML templates are transformed to JPG format andresults management. embedded in a file with a DICOM header that has been
previously extracted out from one of the patient’s study III. RESULTSimages. By this procedure, it can be ensured that the post- The developments have provided an innovative service thatprocessing report will be appended to the correct patient and follows an organized process, implemented in the properstudy in the PACS. Then the platform sends the dicomized technological support. The post-processing platform leveragesreport to the PACS [13, 14] by using the Dicom Store service, the usability and eases the development and implementationand waits for a Dicom Storage Commitment to confirm that it of quantitative imaging in the radiological workflow. As ahas been permanently stored. result, IB-ASAP exploits the possibilities offered by 2) Post-Processing Database: Data is also stored in a technological advances and multidisciplinary collaboration.MySQL database for further statistical analysis and normality An example of the resulting new workflow, in this case forpatterns calculation, as well as automatic knowledge the study of prostate carcinoma, is described below.extraction by data mining procedures. This process also So far the techniques used in the study of prostate cancer doallows regenerating the report anytime in the future by using not allow in many cases to detect the disease, so there is adifferent HTML templates. need of more accurate diagnostic tools. Useful imaging techniques for the study of patients with prostate cancer, such as ultrasound and conventional MRI, usually fail to detect the The following software has been used to implement the disease in its early stages [15, 16]. On ultrasound, the majoritybiomarkers workflow: The post-processing platform software of tumors (> 50%) are isoechoic and central gland lesions areis implemented in Java programming language using the not seen, showing a low sensitivity (39-52%). On MRI, theNetbeans IDE 7.0.1. The use of Java ensures compatibility image shows the tumor with sensitivity that does not exceedwith several operative systems by installing the Java Virtual 67-72% (see Fig. 6). Although widely used for the study ofMachine. A screenshot of the post-processing platform the prostate, MRI conventional sequences have a lowinterface is shown in Fig. 5. accuracy in detecting malignant tumors, since the findings on MRI may mimic or be similar to those of benign prostatic hyperplasia, prostatitis or post-biopsy changes. Therefore MRI conventional sequences have a limited usefulness of as a technique to diagnose cancer. Fig. 5 Screenshot of the post-processing platform interface Fig. 6 58 years old patient prostate adenocarcinoma Post-processing algorithms are programmed using Matlab(The MathWorks Inc., Natick, Massachusetts, USA). In order to improve the diagnosis and monitoring ofHowever, any other programming language or post-processing malignant tumors, new MRI acquisition techniques have beensuite for the biomarkers quantification can be used, due to the developed and added to the standard protocol for prostatemodularity of the biomarkers workflow (i.e. input and output MRI. Three examples of these new techniques are thefor each workflow is centralized in the database). HTML dynamic pharmacokinetic modeling, the study of moleculartemplates were created with Dreamweaver CS5 suite. The diffusion of water and the clinical evaluation by spectroscopy.database for storing all the information is programmed using The vast amount of images and data generated by theseMySQL and PhpMyAdmin. DICOM protocol services and techniques cannot be processed directly, but requires thecommunications are managed with dcm4che2 libraries. application of medical image post-processing algorithms to The only hardware requirement is a workstation connected draw relevant conclusions by the quantification of lesionto the hospital network. characteristics (imaging biomarkers). The output of the post-processing algorithms is included in three reports: perfusion, diffusion and spectroscopy. The perfusion report overlays a vascular permeability parametric map on anatomical slices, highlighting differences on the
diffusion of the blood of arterial vessels through the approaches radiological workflow to the new personalizedcapillaries of the prostate tissue. The diffusion report medicine paradigm, as it allows extracting physical, chemicalquantifies intracellular and extracellular mobility and and biological properties from individual patients. Thediffusion of the protons of water molecules within the resulting quantification reports contain additional informationprostatic tissue, showing the areas with increased cellularity that complements traditional radiological diagnosis, while. The spectroscopy report gives a biochemical and improving its accuracy and the evaluation of the effectivenessmetabolic profile of the gland, highlighting increased choline of treatments.and regional reduction in the levels of citrate, which areindicators of tumor presence . An example of perfusion ACKNOWLEDGMENTquantification image is shown in Fig. 7. Supported by grants from SERAM (Sociedad Española de Radiología Médica). The authors also thank the Radiology Department of Hospital Quirón Valencia for their help and continuous support with image acquisition and for the clinical validation. REFERENCES  Martí Bonmatí L, Alberich-Bayarri A, García-Martí G, Sanz Requena R, Pérez Castillo C, Carot Sierra JM, Manjón Herrera JV. “Imaging biomarkers, quantitative imaging, and bioengineering.” Radiologia. 2011 Jul 4.  Van Beers B, Cuenod CA, Martí-Bonmatí L, Matos C, Niessen W, Padhani A. European Society of Radiology Working Group on Imaging Biomarkers. “White paper on Imaging Biomarkers”. Insights Imaging. 2010;1:42-5.  (2011) Digital Imaging and Communications in Medicine (DICOM): Available: http://medical.nema.org.Fig. 7 Parametric map of perfusion quantification. Red colour indicates more  Perez-Castillo C, Martí-Bonmatí L, Sanz-Requena R, Alberich-Bayarri vascular permeability in prostate tissue A, Garcia-Marti G, Pomar-Nadal A, Lederhandler S. “Post-processing and reporting platform”. European Society for Magnetic Resonance in Medicine and Biology. Octubre 2011. Multivariate combination of the results of these three  Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwigtechniques (diffusion MRI, perfusion MRI and MR LM, et al. “Standards for reporting of diagnostic accuracy. Towardsspectroscopy) (see Fig. 8) on a common platform represents a complete and accurate reporting of studies of diagnostic accuracy: The STARD initiative”. AJR Am J Roentgenol. 2003;181:51-5.significant increase in sensitivity and specificity for the  García-Martí G, Aguilar EJ, Lull JJ, Martí-Bonmatí L, Escartí MJ,diagnosis and monitoring of prostate cancer, thereby Manjón JV, et al. “Schizophrenia with auditory hallucinations: a voxel-improving the diagnosis and monitoring of the lesion. based morphometry study”. Prog Neuropsychopharmacol Biol Psychiatry. 2008;32:72—80.  A. Alberich-Bayarri1, L. Marti-Bonmati1, R. Sanz-Requena1, E. Belloch1 and D. Moratal “In Vivo Trabecular Bone Morphologic and Mechanical Relationship Using High-Resolution 3-T MRI”. M. Eng thesis, Polytechnic University of Valencia, Valencia, Spain. April 2011  Alberich-Bayarri A, Uribe S, Sanchez-Gonzalez J, Pomar A, Lederhandler S, Costa S, Sanz-Requena R, Marti-Bonmati L. “Feasibility of Clinical Cardiac MR Diffusion at 3 Tesla”. Radiological Society of North America. Noviembre 2011.  Alberich-Bayarri A, Uribe S, Sanchez-Gonzalez J, Pomar A, Lederhandler S, Costa S, Sanz-Requena R, Marti-Bonmati L. “Optimization of cardiac MR-diffusion studies at 3 Tesla”. European Society for Magnetic Resonance in Medicine and Biology. Octubre 2011  Lederhandler S.B, Pomar-Nadal A, Alberich-Bayarri A,Uribe S, Sanz- Fig. 8 Examples of perfusion, diffusion and spectroscopy reports Requena R, Perez-Castillo C, Martí-Bonmatí L. “Feasibility of free- breathing 4D phase contrast-MRI at 3T as a routine integration into the diagnosis of cardiac disease”. European Society for Magnetic Resonance in Medicine and Biology. Octubre 2011. IV. CONCLUSIONS  Sanz R, Martí-Bonmatí L, Rodrigo JL, Moratal D. “MR Imaging biomarkers are powerful tools that quantify bio- pharmacokinetic modeling of the patellar cartilage differentiates normal from pathological conditions”. J Magn Reson Imaging.physiological properties not yet perceptible in the medical 2008;27:171—7.image, provide high sensitivity and specificity, and are non  Clunie DA. DICOM Structured Reporting, Bangor, PA, PixelMedinvasive to the patient. Publishing, 2000. The proposed Assembly Pipeline workflow facilitates the  Rada Hussein MSc, Uwe Engelmann PhD, Andre Schroeter MSc, Hans-Peter Meinzer PhD. “DICOM Structured Reporting. Part 1.fast inclusion of imaging biomarkers in clinical practice, Overview and Characteristics”. RadioGraphics 2004; 24:891-896.exploiting the possibilities offered by technological advances  Rada Hussein MSc, Uwe Engelmann PhD, Andre Schroeter MSc,and multidisciplinary collaboration. This new process Hans-Peter Meinzer PhD. “DICOM Structured Reporting. Part 2.
Problems and Challenges in Implementation for PACS Workstations”. RadioGraphics 2004; 24:897-909. (2011) FEFOC (Fundación para la Educación Pública y la Formación en Cáncer) y cáncer ce próstata. Available: www.cancerdeprostata.org (2011) American Cancer Society. Available: http://www.cancer.org/docroot/CRI/CRI_2_3x.asp?rnav=cridg&dt=36 Charles-Edwards EM, de Souza NM. “Diffusion-weighted magnetic resonance imaging and its application to cancer”. Cancer Imaging 2006; 6:135-143 Swindle P, McCredie S, Russell P, et al. “Pathologic Characterization of Human Prostate Tissue with Proton MR Spectroscopy”. Radiology 2003; 228:144-151.