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Rofo 2009; 181(7): 629-636
DOI: 10.1055/s-0028-1109341
Technik und Medizinphysik

© Georg Thieme Verlag KG Stuttgart · New York

Image Data Compression in Diagnostic Imaging: International Literature Review and Workflow Recommendation

Bilddatenkompression in der bildgebenden Diagnostik: Internationale Literaturstudie und Workflow-VorschlagR. Braunschweig1 , I. Kaden1 , J. Schwarzer2 , C. Sprengel2 , K. Klose3
  • 1Klinik für Bildgebende Diagnostik und Interventionsradiologie, BG-Kliniken Bergmannstrost Halle
  • 2Departement of Management Information System and Operations Research, Martin-Luther-University Halle Wittenberg
  • 3Medizinisches Zentrum für Radiologie, Philips-Universität Marburg
Further Information

Publication History

received: 12.9.2008

accepted: 10.2.2009

Publication Date:
09 June 2009 (online)

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Zusammenfassung

Ziel: Hochvolumige Datensätze der bildgebenden Diagnostik (Direktradiografie, Multi-Slice-CT etc.) sichern die diagnostische Betreuung. Die bildgebende Diagnostik hat als Querschnittsfach Schrittmacherfunktion für effektive Workflow-Szenarien übernommen. Für ein effektives Datenmanagement sind hierfür seit Jahren Konzepte zur Datenkompression diskutiert worden. Im Februar 2008 hat eine Konsensuskonferenz der Deutschen Röntgengesellschaft stattgefunden. Es wurden einzelne Datenkompressionstechniken, Kompressionsfaktoren und deren Organbezug tabellarisch als Empfehlung zusammengestellt. Material und Methoden: Unsere Arbeit gibt eine Gesamtübersicht über den Literaturstand zur Datenkompression, Technologie (JPEG und JPEG 2000) und Organbezug und analysiert unterschiedliche Workflow-Szenarien. Dies war Grundlage der Konsensuskonferenz. Die Studien wurden in 4 Level (0 – 3) in Abhängigkeit zu ihrer Evidenz eingeteilt. Für den höchsten Level 3 konnten 51 Studien ausgewertet werden. Ergebnisse: Mit Ausnahme der Schädel-CT wird ein einheitlicher Kompressionsfaktor von 1 : 8 empfohlen. Schädel-CT können ohne diagnostischen Qualitätsverlust mit einem Kompressionswert von 1:5 komprimiert werden. Aus Workflow-Sicht empfehlen wir, Kompressionen an den Modalitäten (CT etc.) vorzunehmen. PACS-basierte Kompressionen sind jedoch derzeit üblich. In diesen Fällen werden allerdings nicht alle Workflow-Vorteile genutzt. Schlussfolgerung: Aus der Literaturübersicht hinsichtlich Technik, Organbezug und unserer Empfehlung zum Workflow ergibt sich die Forderung an die Industrie, die bildgebenden Modalitäten mit einem Kompressionsfilter auszustatten. Es gilt, dass grundsätzlich pro Bilddatensatz nur einmal komprimiert wird.

Abstract

Purpose: Today healthcare policy is based on effectiveness. Diagnostic imaging became a ”pacesetter” due to amazing technical developments (e. g. multislice CT), extensive data volumes, and especially the well defined workflow-orientated scenarios on a local and (inter)national level. To make centralized networks sufficient, image data compression has been regarded as the key to a simple and secure solution. In February 2008 specialized working groups of the DRG held a consensus conference. They designed recommended data compression techniques and ratios. Material und Methoden: The purpose of our paper is an international review of the literature of compression technologies, different imaging procedures (e. g. DR, CT etc.), and targets (abdomen, etc.) and to combine recommendations for compression ratios and techniques with different workflows. The studies were assigned to 4 different levels (0 – 3) according to the evidence. 51 studies were assigned to the highest level 3. Results: We recommend a compression factor of 1 : 8 (excluding cranial scans 1:5). For workflow reasons data compression should be based on the modalities (CT, etc.). PACS-based compression is currently possible but fails to maximize workflow benefits. Only the modality-based scenarios achieve all benefits. Conclusion: Imaging equipment manufacturers are encouraged to improve the compression technology of their imaging devices (e. g. freely selectable compression ratios in the output filter). Double compression should be strictly avoided. Lossless compression formats should be switched off.

Key words

compression - wavelet - jpeg 2000 - image data - workflow - image device

References

  • 1 Braunschweig R, Geis I, Tolksdorf D. et al . DACS – Data Archiving and Communication Services. Zentrale Archivierung von Krankenhausdaten – ein ASP-Projekt.  MedR. 2004;  5 353-359
    Google Scholar
  • 2 Braunschweig R, Geis I, Tolksdorf D. DACS – Zentrale Krankenhausdatenarchivierung und -kommunikation – ein ASP-Konzept.  Telemedizinführer Deutschland. 2005 / 2006;  192-194
    Google Scholar
  • 3 Braunschweig R, Kaden I, Schwarzer J. Activity Based Costing eines RIS/PACS.  Management und Krankenhaus. 2003;  3 12
    Google Scholar
  • 4 Braunschweig R, Kaden I, Schwarzer J. et al . Digitalisierung am Klinikum Bergmannstrost – zukunftsweisende Strategien am Beispiel eines RIS/PACS.  Krankenhaus-IT-Journal. 2003;  5 14-15
    Google Scholar
  • 5 Braunschweig R, Kaden I, Berend A. Vom RIS/PACS zum MIS/DACS – Eine Effektivitätsstrategie.  Telemedizinführer Deutschland. 2006 / 2007;  157-161
    Google Scholar
  • 6 Breeuwer M, Heusdens R, Gunnewiek R K. et al . Data compression of x-ray cardio-angiographic image series.  Int J Card Imaging. 1995;  11 179-186
    Google Scholar
  • 7 Moura L, Furuie S S, Gutierrez M A. et al . Lossy compression techniques, medical images, and the clinician.  MD Comput. 1996;  13 155-159, 172
    Google Scholar
  • 8 Okkalides D, Efremides S. Quality assessment of DSA, ultrasound and CT digital images compressed with the JPEG protocol.  Phy Med Biol. 1994;  39 1407-1421
    Google Scholar
  • 9 Tuinenburg J C, Koning G, Hekking E. et al . American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase II: the effects of varying JPEG data compression levels on the quantitative assessment of the degree of stenosis in digital coronary angiography. Joint Photographic Experts Group.  J Am Coll Cardiol. 2000;  35 1380-1387
    Google Scholar
  • 10 Loose R, Braunschweig R, Kotter E. et al . Kompression Digitaler Bilddaten in der Radiologie-Ergebnisse einer Konsensuskonferenz.  Fortschr Röntgenstr. 2009;  181 32-37
    Google Scholar
  • 11 Azpiroz-Leehan J, Leder R, Lerallut J F. Quantitative and qualitative evaluation of filter characteristics for wavelet packet compression of MR images.  Conf Proc IEEE Eng Med Biol Soc. 2004;  2 1537-1540
    Google Scholar
  • 12 Baker W A, Hearne S E, Spero L A. et al . Lossy (15:1) JPEG compression of digital coronary angiograms does not limit detection of subtle morphological features.  Circulation. 1997;  96 1157-1164
    Google Scholar
  • 13 Brennecke R, Bürgel U, Simon R. et al . American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase III: measurement of image quality differences at varying levels of data compression.  J Am Coll Cardiol. 2000;  35 1388-1397
    Google Scholar
  • 14 Cahill P T, Vullo T, Hu J H. et al . Radiologist evaluation of a multispectral image compression algorithm for magnetic resonance images.  J Digit Imaging. 1998;  11 126-136
    Google Scholar
  • 15 Chen T J, Chuang K S, Chiang Y C. et al . A statistical method for evaluation quality of medical images: a case study in bit discarding and image compression.  Comput Med Imaging Graph. 2004;  28 167-175
    Google Scholar
  • 16 Cosman P C, Davidson H C, Bergin C J. et al . Thoracic CT images: effect of lossy image compression on diagnostic accuracy.  Radiology. 1994;  190 517-524
    Google Scholar
  • 17 DeAngelis G A, Dempsey B, Berr S. et al . Diagnostic efficacy of compressed digitized real-time sonography of uterine fibroids.  Acad Radiol. 1997;  4 83-89
    Google Scholar
  • 18 Eraso F E, Analoui M, Watson A B. et al . Impact of lossy compression on diagnostic accuracy of radiographs for periapical lesions.  Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2002;  93 621-625
    Google Scholar
  • 19 Erickson B J, Manduca A, Persons K R. et al . Evaluation of irreversible compression of digitized posterior-anterior chest radiographs.  J Digit Imaging. 1997;  10 97-102
    Google Scholar
  • 20 Fidler A, Likar B, Pernus F. et al . Impact of JPEG lossy image compression on quantitative digital subtraction radiography.  Dentomaxillofac Radiol. 2002;  31 106-112
    Google Scholar
  • 21 Fidler A, Likar B, Pernus F. et al . Comparative evaluation of JPEG and JPEG2000 compression in quantitative digital subtraction radiography.  Dentomaxillofac Radiol. 2002;  31 379-384
    Google Scholar
  • 22 Frank M S, Lee H, Kim Y. et al . Evaluation of a combined two- and three-dimensional compression method using human visual characteristics to yield high-quality 10:1 compression of cranial computed tomography scans.  Invest Radiol. 1994;  29 842-847
    Google Scholar
  • 23 Goldberg M A, Pivovarov M, Mayo-Smith W W. et al . Application of wavelet compression to digitized radiographs.  Am J Roentgenol. 1994;  163 463-468
    Google Scholar
  • 24 Goldberg M A, Gazelle G S, Boland G W. et al . Focal hepatic lesions: effect of three-dimensional wavelet compression on detection at CT.  Radiology. 1997;  202 159-165
    Google Scholar
  • 25 Halpern E J, Levy H M, Newhouse J H. et al . Quadtree-based data compression of abdominal CT images.  Invest Radiol. 1990;  25 31-38
    Google Scholar
  • 26 Janhom A, Stelt P F, Sanderink G C. A comparison of two compression algorithms and the detection of caries.  Dentomaxillofac Radiol. 2002;  31 257-263
    Google Scholar
  • 27 Kalyanpur van der A, Neklesa V P, Taylor C R. et al . Evaluation of JPEG and wavelet compression of body CT images for direct digital teleradiologic transmission.  Radiology. 2000;  217 772-779
    Google Scholar
  • 28 Karson T H, Chandra S, Morehead A J. et al . JPEG compression of digital echocardiographic images: impact on image quality.  J Am Soc Echocardiogr. 1995;  8 306-318
    Google Scholar
  • 29 Kerensky R A, Cusma J T, Kubilis P. et al . American College of Cardiology/European Society of Cardiology international study of angiographic data compression phase I. The effects of lossy data compression on recognition of diagnostic features in digital coronary angiography.  Eur Heart J. 2000;  21 668-678
    Google Scholar
  • 30 Kihara Y. Evaluation of diagnostic accuracy of CRT monitor display for personal computer in the detection of small lung nodules: with particular emphasis on comparison between JPEG and wavelet compression (Article in Japanese).  Nippon Igaku Hoshasen Gakkai Zasshi. 2001;  61 231-237
    Google Scholar
  • 31 Ko J P, Chang J, Bomsztyk E. et al . Effect of CT image compression on computer-assisted lung nodule volume measurement.  Radiology. 2005;  237 83-88 (Epub 2005 Aug 26)
    Google Scholar
  • 32 Ko J P, Rusinek H, Naidich D P. et al . Wavelet compression of low-dose chest CT data: effect on lung nodule detection.  Radiology. 2003;  228 70-75 (Epub 2003 May 29)
    Google Scholar
  • 33 Kocsis O, Costaridou L, Varaki L. et al . Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms.  Eur Radiol. 2003;  13 2390-2396 (Epub 2003 Feb 15)
    Google Scholar
  • 34 Kondo Y. Medical image transfer for emergency care utilizing internet and mobile phone.  Nippon Hoshasen Gijutsu Gakkai Zasshi. 2002;  58 1393-1401
    Google Scholar
  • 35 Koning G, Béretta P, Zwart P. et al . Effect of lossy data compression on quantitative coronary measurements.  Int J Card Imaging. 1997;  13 261-270
    Google Scholar
  • 36 Kotter E, Roesner A, Torsten Winterer J. et al . Evaluation of Lossy data compression of chest X-rays: a receiver operating characteristic study.  Invest Radiol. 2003;  38 243-249
    Google Scholar
  • 37 Lee K H, Kim Y H, Kim B H. et al . Irreversible JPEG 2000 compression of a abdominal CT for primary interpretation: assessment of visually lossless threshold.  Eur Radiol. 2007;  17 1529-1534 (Epub 2006 Nov 22)
    Google Scholar
  • 38 Li F, Sone S, Takashima S. et al . Effects of JPEG and wavelet compression of spiral low-dose ct images on detection of small lung cancers.  Acta Radiol. 2001;  42 156-160
    Google Scholar
  • 39 Megibow A J, Rusinek H, Lisi V. et al . Computed tomography diagnosis utilizing compressed image data: an ROC analysis using acute appendicitis as a model.  J Digit Imaging. 2002;  15 84-90 (Epub 2002 Sep 26)
    Google Scholar
  • 40 Ohgiya Y, Gokan T, Nobusawa H. et al . Acute cerebral infarction: effect of JEPG compression on detection at CT.  Radiology. 2003;  227 124-127 (Epub 2003 Feb 19)
    Google Scholar
  • 41 Penedo M, Souto M, Tahoces P G. et al . Free-response receiver operating characteristic evaluation of lossy JPEG2000 and object-based set partitioning in hierarchical trees compression of digitized mammograms.  Radiology. 2005;  237 450-457
    Google Scholar
  • 42 Persons K R, Hangiandreou N J, Charboneau N T. et al . Evaluation of irreversible JPEG compression for a clinical ultrasound practice.  J Digit Imaging. 2002;  15 15-21 (Epub 2002 Apr 30)
    Google Scholar
  • 43 Ricke J, Maass P, Lopez Hänninen E. et al . Wavelet versus JPEG (Joint Photographic Expert Group) and fractal compression. Impact on the detection of low-contrast details in computed radiographs.  Invest Radiol. 1998;  33 456-463
    Google Scholar
  • 44 Ringl H, Schernthaner R E, Kulinna-Cosentini C. et al . Lossy Three-dimensional JPEG2000 Compression of Abdominal CT Images: Assessment of the Visually Lossless Threshold and Effect of Compression Ratio on Image Quality.  Radiology. 2007;  Epub ahead of print
    Google Scholar
  • 45 Ringl H, Schernthaner R E, Bankier A A. et al . JPEG2000 compression of thin-section CT images of the lung: effect of compression ratio on image quality.  Radiology. 2006;  240 869-877 (Epub 2006 Jul 25)
    Google Scholar
  • 46 Savcenko V, Erickson B J, Persons K R. et al . An evaluation of JPEG and JPEG 2000 irreversible compression algorithms applied to neurologic computed tomography and magnetic resonance images. Joint Photographic Experts Group.  J Digit Imaging. 2000;  13 183-185
    Google Scholar
  • 47 Savcenko V, Erickson B J, Persons K R. et al . An evaluation of JPEG and JPEG 2000 irreversible compression algorithms applied to neurologic computed tomography and magnetic resonance images. Joint Photographic Experts Group.  J Digit Imaging. 2000;  13 183-185
    Google Scholar
  • 48 Slone R M, Foos D H, Whiting B R. et al . Assessment of visually lossless irreversible image compression: comparison of three methods by using an image-comparison workstation.  Radiology. 2000;  215 543-553
    Google Scholar
  • 49 Slone R M, Muka E, Pilgram T K. Irreversible JPEG compression of digital chest radiographs for primary interpretation: assessment of visually lossless threshold.  Radiology. 2003;  228 425-429
    Google Scholar
  • 50 Sung M M, Kim H J, Yoo S K. et al . Clinical evaluation of compression ratios using JPEG2000 on computed radiography chest images.  J Digit Imaging. 2002;  15 78-83 (Epub 2002 Sep 26)
    Google Scholar
  • 51 Suryanarayanan S, Karellas A, Vedantham S. et al . A perceptual evaluation of JPEG 2000 image compression for digital mammography: contrast-detail characteristics.  J Digit Imaging. 2004;  17 64-70
    Google Scholar
  • 52 Toney M O, Dominguez R, Dao H N. et al . The effect of lossy discrete cosine transform compression on subtle bone fractures.  J Digit Imaging. 1997;  10 169-173
    Google Scholar
  • 53 Tuinenburg J C, Koning G, Hekking E. et al . American College of Cardiology/European Society of Cardiology international study of angiographic data compression phase II. The effects of varying JPEG data compression levels on the quantitative assessment of the degree of stenosis in digital coronary angiography.  Eur Heart J. 2000;  21 679-686
    Google Scholar
  • 54 Woo H S, Kim K J, Kim T J. et al . JPEG 2000 compression of abdominal CT: difference in tolerance between thin- and thick-section images.  Am J Roentgenol. 2007;  189 535-541
    Google Scholar
  • 55 Yamamoto S, Johkoh T, Mihara N. et al . Evaluation of compressed lung CT image quality using quantitative analysis.  Radiat Med. 2001;  19 321-329
    Google Scholar
  • 56 Yin F F, Gao Q. Oncolgic image compression using both waveletand maskin techniques.  Med Phys. 1997;  24 2038-2042
    Google Scholar
  • 57 Zalis M E, Hahn P F, Arellano R S. et al . CT colonography with teleradiology: effect of lossy wavelet compression on polyp detection – initial observations.  Radiology. 2001;  220 387-392
    Google Scholar
  • 58 Zheng L M, Sone S, Itani Y. et al . Effect of CT digital image compression on detection of coronary artery calcification.  Acta Radiol. 2000;  41 116-121
    Google Scholar

Dr. Ingmar Kaden

Klinik für Bildgebende Diagnostik und Interventionsradiologie, BG-Kliniken Bergmannstrost Halle

Merseburger Straße 165

06112 Halle

Germany

Phone: ++ 49/3 45/1 32 61 84

Fax: ++ 49/3 45/1 32 61 86

Email: Ingmar.Kaden@Bergmannstrost.com

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