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Mammography and DICOM

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Mammography and DICOM Adapting an Analog Modality to the Digital World Julian Marshall R2 Technology, Inc. Mammography Mammography is a film-based modality Worldwide ... – PowerPoint PPT presentation

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Title: Mammography and DICOM


1
Mammography and DICOM
  • Adapting an Analog Modality
  • to the Digital World
  • Julian Marshall
  • R2 Technology, Inc.

2
Mammography
  • Mammography is a film-based modality
  • Worldwide mammo machines
  • 25,500 film-screen
  • 500 digital

98.1 1.9
3
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

4
Digitized Film
  • Mammograms are digitized
  • Wide variation
  • Scanners vary
  • Resolution
  • Maximum O.D.
  • Noise

5
Digital Mammography
  • Mammograms are acquired digitally
  • Detectors do still vary
  • Resolution
  • Bit depth (CR)
  • Noise (CR)

6
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 cm 85
  • 24x30 cm 15

7
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!

8
Mammography and PACS
  • Images are recalled regularly
  • Scheduled pre-fetching is easy
  • But each image is accessed each year!

9
Computer-Aided Detection
  • Use a computer to look for regions-of-interest
    that might be overlooked by a radiologist
  • Simple example Count the Fs

10
Computer-Aided Detection
  • Simple example Count the Fs

FINISHED FILES ARE THE RE- SULT OF YEARS OF
SCIENTIF- IC STUDY COMBINED WITH THE EXPERIENCE
OF YEARS
11
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
12
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
13
Computer-Aided Detection
  • Mammography CAD first became available
  • 1998 Film-screen mammography
  • 2000 Digital mammography
  • At that time
  • DICOM support for images
  • No DICOM support for CAD output

14
DICOM WG 15
  • Standards development
  • Digital X-ray (includes mammo) 1998
  • Mammography CAD SR 2001

15
Mammography CAD SR
  • Allows encoding of ACRs BI-RADSTM 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

16
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

17
Inference Tree
  • Individual Calcification
  • Location of center
  • Outline of individual calcification
  • Size

Three individual calcifications are detected in a
single image
18
Inference Tree
  • Calcification cluster
  • Location of center
  • Outline of cluster
  • Size
  • No. of individual calcifications

The three are grouped together as a cluster of
calcifications
19
Inference Tree
  • Density
  • Center of density
  • Outline
  • Size
  • Description of margin

Densities and other clusters are detected, some
from priors
20
Inference Tree
Densities become masses if spatially related
21
Inference Tree
Other findings may also be spatially related
22
Inference Tree
Calcs within a mass are related spatially
23
Inference Tree
Objects found in priors are temporally related to
currents
24
Inference Tree
Objects can also be related contra-laterally (not
shown here)
25
Inference Tree
Individual Impressions and Recommendations are
formed
26
Inference Tree
Overall Impression and Recommendation is formed
27
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.

28
Other Information
  • Breast outline (border)
  • Pectoral muscle outline
  • Nipple location
  • Other findings
  • BBs
  • J-wires

29
Other Information
  • Image quality findings
  • Motion blur
  • Artifacts

30
Coming Soon
  • Breast Imaging Report SR
  • Relevant Patient History Query

31
Summary
  • Mammography is almost entirely a film-based
    modality
  • Slowly this is changing
  • And with that change comes DICOM!
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