RSNA RCA22 Quantitative Imaging: Structured Reporting in 3D Slicer and beyond

Transcription

1 RSNA RCA22 Quantitative Imaging: Structured Reporting in 3D Slicer and beyond Andrey Fedorov, PhD November 29, 2015 Brigham and Women s Hospital / Harvard Medical School Boston, MA, USA fedorov@bwh.harvard.edu

2 Disclosures Co-PI of U24 CA Quantitative Image Informatics for Cancer Research (QIICR) (NIH NCI) 3D Slicer contributor and user The content is solely the responsibility of the author and does not necessarily represent the official views of the NCI. 2

3 Outline Structured reporting in Quantitative Imaging (QI) research Background about the projects and tools QI Structured reporting in 3D Slicer using DICOM motivation context examples Focus on what DICOM can do for you, not how 3

4 What is Structured Reporting? Excerpt from (Clunie 2000) Clunie D. DICOM Structured Reporting. PixelMed Publishing;

5 Scope of this talk Structured Reporting as in Structured Data Reporting * for Quantitative Image Analysis research automated measurements automated image segmentation automated image registration lesion tracking data aggregation and mining secondary analysis * not limited to the use of DICOM Structured Reporting IOD (if you know what that means but if you don t - it s Ok!) 5

6 DICOM Structured Reporting features Common to various applications of SR (Clunie 2000): the presence of lists and hierarchical relationships the use of coded or numeric content in addition to plain text the use of relationships between concepts the presence of embedded references to images and similar objects (Clunie 2000) Clunie D. DICOM Structured Reporting. PixelMed Publishing;

7 Use case: Positron Emission Tomography (PET) qualitative detection & localization quantitative characterization treatment response disease assessment treatment response research questions region of interest definition (manual vs auto) SUV normalization (SUVbw, SUVlbm, ) quantitation approach (mean, peak, ) 7

8 Imaging research informatics: Common practice domain-specific formats image analysis comparison visualization 8

9 Imaging research informatics: Common practice domain-specific formats image analysis comparison visualization lab notes, README files 9

10 Imaging research informatics: Common practice domain-specific formats image analysis comparison visualization lab notes, README files spreadsheets, delimited text files 10

11 What is the problem? limited integration of patient information acquisition details missing processing details missing semantics are limited data relationships are implicit or in free text 11

12 QIICR: Quantitative Imaging Informatics for Cancer Research NIH National Cancer Institute funded project U24 CA In the context of quantitative imaging biomarker development: help share tools open science reduce waste translation to clinic translation to commercial products 12

13 QIICR: Quantitative Imaging Informatics for Cancer Research NIH National Cancer Institute funded project U24 CA In the context of quantitative imaging biomarker development: help share tools help share data validation and reproducibility secondary analysis aggregate repositories of the analysis results 13

14 QIICR: Quantitative Imaging Informatics for Cancer Research NIH National Cancer Institute funded project U24 CA In the context of quantitative imaging biomarker development: help share tools help share data improve interoperability commercial products and clinical systems research community repositories of data research platforms 14

15 Application: NCI Quantitative Imaging Network (QIN) The network is designed to promote research and development of quantitative imaging methods for the measurement of tumor response to therapies in clinical trial settings, with the overall goal of facilitating clinical decision making. QIN as of March QIICR QIN partners University of Iowa: Head and neck cancer Brigham and Women s Hospital: Prostate cancer Mass General Hospital: Glioblastoma 15

16 Platform: 3D Slicer Multi-modality visualization, segmentation and registration Free open source Cross-platform Support and training Industry-led engineering Extensible Not FDA approved! Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30: doi: /j.mri

17 Standard: DICOM Digital Imaging and Communication in Medicine (DICOM) is the standard for communication of medical imaging information and related data compatibility with acquisition and archival tools store images and results of analysis harmonized with other standards (HL7, JSON, XML, REST, WADO) history of development and adoption since 1983 adopted by virtually all manufacturers of medical imaging equipment open international community of stakeholders continuously evolving standard 17

18 PET/CT quantitative imaging research Processing steps: 1. Normalization (SUV-correction) 2. Segmentation 3. Region of interest quantification + Clinical information 18

19 PET SUV factors in DICOM In addition to encoding the actual SUV normalization factor: Semantics Cross-references to the source data Composite context normalization method units Patient Study DICOM Real World Value Mapping (RWVM) object (G-C036, SRT, Measurement method ) = (126410,DCM, SUV body weight calculation method ) (G-C1C6, SRT, Quantity ) = (126400, DCM, Standardized uptake value ) Measurement Units = ({SUVbw}g/ml, UCUM, Standardized Uptake Value body weight ) ReferencedImageSequence PatientName, PatientID, PatientSex, PatientBirthDate StudyDate, StudyTime, Unique Identifiers 19

20 PET SUV support in 3D Slicer automatic calculation of SUVbw RWVM object generation semantics automatically populated composite context and references automatically populated SUV-customized image display standalone converters RWVM object from DICOM PET 20

21 Image segmentation in DICOM In addition to the spatial labeling of the voxels of interest: pathology and reference regions semantics segmentation process Segmented Property Category = (M-01000, SRT, "Morphologically Altered Structure") Segmented Property Type = (M-80003, SRT, "Neoplasm, Primary") Anatomic Region = (T-53131, SRT, "base of tongue") algorithm reader/session single file, efficient storage, multiple occupancy voxels presentation DICOM Segmentation image (SEG) 21

22 DICOM SEG support in 3D Slicer DICOM SEG import DICOM SEG export auto load referenced images initialization of region semantics initialization of color for presentation user-initialized region semantics from pre-populated options auto initialization of DICOM attributes standalone converters research formats to/from DICOM SEG 22

23 PET quantitative measurements in DICOM In place (or in addition to!) of an Excel spreadsheet: template-defined content structure hierarchical organization concepts and their relationships references to the PET, SEG and RWVM objects used composite context DICOM Structured Reporting (SR) object 23

24 PET quantitative measurements in 3D Slicer variety of supported measurements SUV-specific: maximum, mean and peak, Total Lesion Glycolysis (TLG), glycolysis quartiles, Standardized Added Metabolic Activity (SAM) generic: median, variance and Root Mean Square (RMS) of SUV, segmentation volume standalone conversion tools from CSV-like to DICOM SR 24

25 Encoding clinical information In place of the Excel spreadsheets (at best!): hierarchy, semantics, composite context clinical history outcomes demographics diagnosis pathology therapy regimen follow-up date and status survival DICOM Structured Reporting object 25

26 DICOM SR: Presentation and interaction 26

27 Ecosystem of DICOM objects applied to PET QI research explicit references to the source objects commonality of identifiers (common composite context at patient and study level) 27

28 The take-home points unlike in radiology reading applications, most of the content can be populated automatically workstations supporting SR for QI are becoming available and are evolving developer toolkits are available to reduce the barriers for adoption adoption of SR has a tremendous and tangible potential to reduce obstacles for technology translation support reproducible science enable data aggregation and mining help with tool comparison 28

29 Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. (2015) DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ PrePrints 3:e

30 Related activities at RSNA 2015 Quantitative Imaging Reading Room (Lakeside) 3D Slicer booth (QRR004) DICOM for Segmentation interoperability booth (QRR005) 3D Slicer courses google RSNA 2015 NAMIC 30

31 Acknowledgments QIICR team Ron Kikinis David Clunie Steve Pieper Reinhard Beichel Christian Bauer Ethan Ulrich Andreas Wahle John Buatti John Sunderland Michael Onken Joerg Riesmeier Nicole Aucoin Christian Herz 3D Slicer community National Cancer Institute funding ITCR community QIN community 31