Mammography and DICOM Adapting an Analog Modality to the Digital World Julian Marshall R2 Technology, Inc.

Slides:



Advertisements
Similar presentations
IHE Workshop – June 2006What IHE Delivers 1 Carolyn Reynolds Hologic, Inc. Vendor co-chair, IHE Mammography Committee Mammography Image Integration Profile.
Advertisements

Image Reconstruction.
Assessment of Radiologists’ Performance with CADe for Digital Mammography Elodia B. Cole, MS Medical University of South Carolina Department of Radiology.
The Field of Digital Radiography
Copyright © 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins CHAPTER 15 Creating the Digital Image.
Digital Radiography.
MAMMO QC – covered in week 8
Renate Höcker, Antje Schroeder, Siemens Healthcare IHE Radiology – DBT Supplement Supplement Development Kick-Off.
ACR and SBI Statement Margarita Zuley, MD Associate Professor, Radiology Medical Director, Breast Imaging University of Pittsburgh.
Real-Time Human Pose Recognition in Parts from Single Depth Images Presented by: Mohammad A. Gowayyed.
Computer Aided Diagnosis: CAD overview
Breast Histopathology : Mammography
· Information gathering · Data analysis · Decision making · “ Human life is too important to be left to a computer “ Patients receive the best treatment.
Influence of Monitor Luminance & Tone Scale on Observers’ Search & Dwell Patterns.
For internal use only / Copyright © Siemens AG All rights reserved. Multiple-instance learning improves CAD detection of masses in digital mammography.
tomos = slice, graphein = to write
February 13, 1997CWU B.Kovalerchuk1 DESIGN OF CONSISTENT SYSTEM FOR RADIOLOGISTS TO SUPPORT BREAST CANCER DIAGNOSIS.
Automatic Detection And Classification Of Microcalcifications In Digital Mammograms Institute for Brain and Neural Systems Brown University Providence.
Picture Archiving And Communication System (PACS)
Ulrich Bick, MD Maryellen L. Giger PhD Robert A. Schmidt, MD Robert M. Nishikawa, PhD Kunio Doi, PhD 1 報告者:劉治元.
Volumetric Breast Density understandbreastdensity.or g.
1 History and Lessons from FDA Regulation of Digital Radiology Kyle J. Myers, Ph.D. Division of Imaging and Applied Mathematics OSEL/CDRH/FDA October 22,
Digital Image Characteristic
BI-RADS By Nina Zahedi MD.
1 Bitmap Graphics It is represented by a dot pattern in which each dot is called a pixel. Each pixel can be in any one of the colors available and the.
Why Digital Mammography. Why Cogdell Memorial Hospital?  Cogdell Memorial Hospital delivers the highest quality patient care available –Dedicated staff.
Kunal Mitra Professor, Mechanical and Aerospace Engineering Department Director- Biomedical Engineering Program Florida Institute of Technology, Melbourne,
Future Of Diagnostic Imaging A Look Into The Next Decade ? (Part 3)
DICOM INTERNATIONAL DICOM INTERNATIONAL CONFERENCE & SEMINAR April 8-10, 2008 Chengdu, China Applications of DICOM SR Andrei Leontiev Dynamic Imaging Solutions,
ACRIN Breast Committee Fall Meeting : Comparison of Full-Field Digital Mammography with Digital Breast Tomosynthesis Image Acquisition in Relation.
What’s Next After an Abnormal Screening Mammogram? James A Stewart M.D. Elizabeth Burnside M.D.
DR (MRS) AUGUSTINA BADU-PEPRAH MB Ch B, FWACS RADIOLOGIST KATH.
Introduction to Breast Imaging BREAST RAD LAB Directions: Please answer all the questions prior to interactive conference. 1.
Seeram Chapter #3: Digital Imaging
Introduction to Clinical Radiology: The Breast
Portal (Win2000 +Linux) Digital Mammography Unit Acquisition Workstation Dry Laser Film Printer Diagnostic Workstation.
Managed by UT-Battelle for the Department of Energy Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm Robert M. Patton, Ph.D.
infinity-project.org Engineering education for today’s classroom 2 Outline How Can We Use Digital Images? A Digital Image is a Matrix Manipulating Images.
3D Mammography Ernesto Coto Sören Grimm Stefan Bruckner M. Eduard Gröller Institute of Computer Graphics and Algorithms Vienna University of Technology.
1 INTRODUCTION TO THE PHYSICS OF DIAGNOSTIC IMAGING Outline of Course Brief History Common Terminology Imaging Modalities.
Feb 7-8, 2007IHE Participant's Workshop 1 Integrating the Healthcare Enterprise Mammography Image – MAMMO Chris Lindop, GE Healthcare Co-Chair Radiology.
Renate Höcker, Antje Schroeder, Siemens Healthcare IHE Radiology – DBT Supplement Supplement Development Kick-Off.
40% of women have dense breasts. RESULT: Current 2D mammography makes it difficult to detect cancers in dense breast tissue because both appear white.
ENDA MOLLOY, ELECTRONIC ENG. INITIAL PRESENTATION, 7/10/08. Automated Image Analysis Techniques for Screening of Mammography Images.
Mammography Information System
More digital reading explaining LUT RT 244 Perry Sprawls, Ph.D. Professor Emeritus Department of Radiology Emory University School of.
Visual Computing Computer Vision 2 INFO410 & INFO350 S2 2015
Biometrics % Biostatistics
Philadelphia University Faculty of Nursing. Mammogram By :- Yasmin Ali Musleh Num : Dr :- Aida Abd- ALrazeq.
Mammography Linda Haun Bryan Medical Center Mammography Coordinator.
Some Difficult Decisions are Easier without Computer Support / TA Mammography, RT Diversity / Andrey A. Povyakalo (work together with E Alberdi, L Strigini.
IHE Workshop – June Note to IHE Committees: This is my draft of the IHE Mammography Image Integration Profile talk for the workshop. If I have the.
What IHE Delivers IHE Mammography: Workflow, Display, and Dose Monitoring Christoph Dickmann, Siemens Healthcare RSNA 2008 Informatics Courses.
More digital 244 wk 12 Perry Sprawls, Ph.D. Professor Emeritus Department of Radiology Emory University School of Medicine Atlanta, GA,
Effects of Grayscale Window/Level on Breast Lesion Detectability Jeffrey Johnson, PhD a John Nafziger, PhD a Elizabeth Krupinski, PhD b Hans Roehrig, PhD.
ACRIN Breast Committee Fall Meeting CADe study Etta Pisano, MD Martin Yaffe PhD Elodia Cole, MS Zheng Zhang, PhD ACRIN Breast Committee.
What IHE Delivers 1 Mammography Image Integration Profile Carolyn Reynolds Connectivity Manager/Hologic, Inc.
國立雲林科技大學 National Yunlin University of Science and Technology Intelligent Database Systems Lab 1 Self-organizing map for cluster analysis of a breast cancer.
By Prof. Stelmark. Digital Imaging In digital imaging, the latent image is stored as digital data and must be processed by the computer for viewing on.
Dr. Julia Flukinger Breast Radiologist, Director Breast MRI, Advanced Radiology May 21, 2106.
Introduction to Medical Imaging Week 2: X-ray and CT
Sunday Case of the Day History : Patient presented for screening mammogram. The radiologist noted that there “increased motion on both CC views” limiting.
Computed tomography. Formation of a CT image Data acquisitionImage reconstruction Image display, manipulation Storage, communication And recording.
Understanding Analogue and Digital Video Lesson 1
How good are you at making observations?
DICOM 11/21/2018.
Sensitivity of a Direct Computer-aided Detection System in Full-field Digital Mammography for Detection of Microcalcifications Not Associated with Mass.
The Radiology Information System (RIS)
Presentation transcript:

Mammography and DICOM Adapting an Analog Modality to the Digital World Julian Marshall R2 Technology, Inc.

Mammography Mammography is a film-based modality –Worldwide mammo machines: 25,500 film-screen 500 digital 98.1 % 1.9 %

Reading Mammograms ACR position: –Radiologist must read original image US clinical practice: –Read film-screen mammograms on film Do not digitize films and read softcopy –Priors can be read softcopy

Digitized Film Mammograms are digitized –Wide variation –Scanners vary: Resolution Maximum O.D. Noise

Digital Mammography Mammograms are acquired digitally –Detectors do still vary: Resolution Bit depth (CR) Noise (CR)

Mammography Imaging demands are extreme: –Typical resolutions: Film:43 to 50 microns x 12 bits Digital:50 to 100 microns x 14 bits –Typical image sizes: 18x24 cm85% 24x30 cm15%

Mammography Imaging demands are extreme: –Typical data volume: 4 film case:180 MB avg 100 case/day:18.0 GB/day 250 days/yr:4.5 TB/year –Film scanner will generate: 45 MB per minute, all day long!

Mammography and PACS Images are recalled regularly Scheduled pre-fetching is easy But … each image is accessed each year!

Computer-Aided Detection Use a computer to look for regions-of- interest that might be overlooked by a radiologist Simple example: Count the ‘F’s

Computer-Aided Detection Simple example: Count the ‘F’s FINISHED FILES ARE THE RE- SULT OF YEARS OF SCIENTIF- IC STUDY COMBINED WITH THE EXPERIENCE OF YEARS

Computer-Aided Detection Most people find these three FINISHED FILES ARE THE RE- SULT OF YEARS OF SCIENTIF- IC STUDY COMBINED WITH THE EXPERIENCE OF YEARS

Computer-Aided Detection Many people do not find all six! FINISHED FILES ARE THE RE- SULT OF YEARS OF SCIENTIF- IC STUDY COMBINED WITH THE EXPERIENCE OF YEARS

Computer-Aided Detection Mammography CAD first became available: –1998Film-screen mammography –2000Digital mammography At that time: –DICOM support for images –No DICOM support for CAD output

DICOM WG 15 Standards development: –Digital X-ray (includes mammo)1998 –Mammography CAD SR2001

Mammography CAD SR Allows encoding of ACR’s BI-RADS TM reporting structure via an inference tree “Simple” CAD devices can create “simple” Mammo CAD objects “Complex” CAD devices can create full mammography report inference tree

Mammography CAD SR Single image finding – found in one image Composite object – findings correlated in one or more images: –Temporal – comparison over time –Spatial – e.g. mass behind the nipple, or mammo/ultrasound correlation –Contra-laterally – e.g. left/right comparison

Inference Tree Three individual calcifications are detected in a single image Individual Calcification: –Location of center –Outline of individual calcification –Size

Inference Tree The three are grouped together as a cluster of calcifications Calcification cluster: –Location of center –Outline of cluster –Size –No. of individual calcifications

Inference Tree Densities and other clusters are detected, some from priors Density –Center of density –Outline –Size –Description of margin

Inference Tree Densities become masses if spatially related

Inference Tree Other findings may also be spatially related

Inference Tree Calcs within a mass are related spatially

Inference Tree Objects found in priors are temporally related to currents

Inference Tree Objects can also be related contra-laterally (not shown here)

Inference Tree Individual Impressions and Recommendations are formed

Inference Tree Overall Impression and Recommendation is formed

A Vast Array of Adjectives Every Single Image Finding and Composite Object has a set of common descriptors: –Rendering intent –Certainty of finding –Probability of cancer Plus a variety of context-specific descriptors: –Calcs: rod-like, pleomorphic, etc.

Other Information Breast outline (border) Pectoral muscle outline Nipple location Other findings: –BBs –J-wires

Other Information Image quality findings –Motion blur –Artifacts

Coming Soon Breast Imaging ReportSR Relevant Patient HistoryQuery

Summary Mammography is almost entirely a film- based modality Slowly this is changing And with that change comes DICOM!