1. 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
cperez.val@quiron.es
2
apomar.val@quiron.es
3
ggarcia.val@quiron.es
4
aalberich.val@quiron.es
5
rsanz.val@quiron.es
+
Department of Medicine, University of Valencia, Valencia, Spain.
6
luis.marti@uv.es
Abstract Results: The developments have provided an innovative service
that follows an organized process, as a proper technological
Purpose: To include imaging biomarkers in the radiological support to leverage the usability and ease the development and
workflow, providing additional quantitative information to implementation of quantitative imaging. In addition, the
radiologists 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 objective
characteristics that are related to normal biological processes, Conclusion: Imaging biomarkers help establishing the presence
diseases, or the response to treatment. Their implementation is of a lesion before it becomes evident, assess the predisposition to
changing the concept and workflow of radiology today. By suffer it, measure its biological situation, define its progress and
applying new modeling techniques and computational evaluate treatment effects. The platform quickly incorporates all
procedures to medical images, a set of quantitative parameters is these advantages into the radiological workflow.
obtained. This quantitative information provides accurate and
reproducible measures of various processes in individual
patients. Their potential to display and measure a wide range of I. INTRODUCTION
biological and physiological situations, and their non invasive
The workspace of radiologists and medical imaging
nature, makes imaging biomarkers one of the most active
research fields. specialists has changed with the development and
An automated post-processing platform was developed in order implementation of digital imaging. The viewing, processing
to implement imaging biomarkers in the radiological workflow. and properties extraction from medical images are some of the
The 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 resonance
different conditions and diseases. For instance: indicators of imaging (MRI) scanners has exponentially grown. Clinicians
neovascularization 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 a
bone structure studies on osteoporosis, studies of cartilage
few. An MRI scan is one of the best methods for the in-vivo
degeneration in osteoarthritis, studies of connectivity, volume
and morphometry in neurodegenerative diseases and studies of examination of the human body without opening it. Modern
morphology and function of the cardiovascular system. MRI equipments provide non invasive, highly accurate
The platform stores the results in a database and generates anatomic images and have an excellent spatial resolution that
structured reports that are sent to the PACS. These post- allows to visualize internal structures in detail and to define
processing reports provide very useful quantitative information their main properties. The high quality of MRI images help
to 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 computers
algorithms are programmed in Matlab and results are stored in a
can be exploited to improve the quality and to extract
MySQL database. The only hardware requirement is a
workstation 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

2. biomarkers are being developed to provide quantitative Biomedical Engineering Knowledge, is defining new
information that cannot be a priori detected or measured by radiological workflows.
the visualization of the original medical images [1]. The adequate technological support required to integrate
Imaging biomarkers are objective characteristics extracted the use of imaging biomarkers in a radiology service is
from medical images that act as indicators of normal described in this work. The presented Biomarkers Workflow
biological processes, diseases or responses to therapeutic implements the entire methodology, from the image
interventions [2]. This quantitative information is obtained acquisition to the generation and storage of post-processing
before a lesion or biological process becomes evident in the reports, making the whole process much more efficient and
radiological observation, by analyzing properties and simple.
multivariate combination of medical images and data. This
process requires careful monitoring of acquisition,
normalization of data and image preparation, data extraction, II. MATERIALS AND METHODS
analysis and visualization of results. Their enormous potential The radiological workflow and the image-based clinical
to display and measure a wide range of biological and practice are tightly related to the PACS (Picture Achiving and
physiological situations, and their non invasive nature, makes Communication System) and the DICOM (Digital Imaging
imaging 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 manages
Fig. 1. acquisition, transmission, storage, distribution, display and
interpretation of medical images.
Medical digital images format is defined by DICOM
standard [3], 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 image
flow, 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 data
imaging 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.

3. • 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 by
workflow, a post-processing platform [4] has been completely query/retrieve service via the network, or directly from
integrated in the hospital network. It receives DICOM images imaging devices (MRI, Computerized Tomography).
from the hospital PACS or any other storage media, and sends
complete reports containing the post-processing results to the B. Medical Images Storage
PACS. 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 [6] and volumetry analysis, functional studies,
Fig. 3 [5]. finite element mechanical simulations [7], spectroscopy
The IB-ASAP data pipeline is shown in Fig. 4: profile, diffusion [8, 9], perfusion, quantification of water-fat-
iron, flow dynamics quantification [10], fiber tracking studies,
pharmacokinetic models [11], 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 [12] 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-processing
process: data reception, medical images storage, notifications,
workflow. After being filled with the required data, the
post-processing algorithms execution and post-processing
HTML templates are transformed to JPG format and
results management.
embedded in a file with a DICOM header that has been

4. previously extracted out from one of the patient’s study III. RESULTS
images. By this procedure, it can be ensured that the post- The developments have provided an innovative service that
processing report will be appended to the correct patient and follows an organized process, implemented in the proper
study in the PACS. Then the platform sends the dicomized technological support. The post-processing platform leverages
report to the PACS [13, 14] by using the Dicom Store service, the usability and eases the development and implementation
and waits for a Dicom Storage Commitment to confirm that it of quantitative imaging in the radiological workflow. As a
has 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 for
patterns 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 do
allows regenerating the report anytime in the future by using not allow in many cases to detect the disease, so there is a
different 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 majority
biomarkers workflow: The post-processing platform software of tumors (> 50%) are isoechoic and central gland lesions are
is implemented in Java programming language using the not seen, showing a low sensitivity (39-52%). On MRI, the
Netbeans IDE 7.0.1. The use of Java ensures compatibility image shows the tumor with sensitivity that does not exceed
with several operative systems by installing the Java Virtual 67-72% (see Fig. 6). Although widely used for the study of
Machine. A screenshot of the post-processing platform the prostate, MRI conventional sequences have a low
interface 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 of
However, any other programming language or post-processing
malignant tumors, new MRI acquisition techniques have been
suite for the biomarkers quantification can be used, due to the
developed and added to the standard protocol for prostate
modularity of the biomarkers workflow (i.e. input and output
MRI. Three examples of these new techniques are the
for each workflow is centralized in the database). HTML
dynamic pharmacokinetic modeling, the study of molecular
templates 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 these
MySQL and PhpMyAdmin. DICOM protocol services and
techniques cannot be processed directly, but requires the
communications 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 lesion
to 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

5. diffusion of the blood of arterial vessels through the approaches radiological workflow to the new personalized
capillaries of the prostate tissue. The diffusion report medicine paradigm, as it allows extracting physical, chemical
quantifies intracellular and extracellular mobility and and biological properties from individual patients. The
diffusion of the protons of water molecules within the resulting quantification reports contain additional information
prostatic tissue, showing the areas with increased cellularity that complements traditional radiological diagnosis, while
[17]. The spectroscopy report gives a biochemical and improving its accuracy and the evaluation of the effectiveness
metabolic profile of the gland, highlighting increased choline of treatments.
and regional reduction in the levels of citrate, which are
indicators of tumor presence [18]. An example of perfusion ACKNOWLEDGMENT
quantification 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.
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