Guideline for the Use of Image Compression in Diagnostic Imaging Guideline for the Use of Image Compression in Diagnostic Imaging Clinical Radiology Guideline Name of document and version: Guideline for the Use of Image Compression in Diagnostic Imaging, Version 2 Approved by: Faculty of Clinical Radiology Council Date of approval: 13 August 2020 ABN 37 000 029 863 Copyright for this publication rests with The Royal Australian and New Zealand College of Radiologists ® The Royal Australian and New Zealand College of Radiologists Level 9, 51 Druitt Street Sydney NSW 2000 Australia New Zealand Office: Floor 6, 142 Lambton Quay, Wellington 6011, New Zealand Email: [email protected] Website: www.ranzcr.com Telephone: +61 2 9268 9777 Disclaimer: The information provided in this document is of a general nature only and is not intended as a substitute for medical or legal advice. It is designed to support, not replace, the relationship that exists between a patient and his/her doctor. TABLE OF CONTENTS 1. Introduction 3 2. Data Compression in Medical Imaging 4 3. Appropriate Application of Data Compression 4 4. Recommendations 6 August 2020 5. Related documents 7 6. Acknowledgements 7 7. References 7 lege of Radiologists® | | © | The Royal Australian and New Zealand Col Guideline for the Use of Image Compression in Diagnostic Imaging, Version 2 Page 3 of 8 About the College The Royal Australian and New Zealand College of Radiologists (RANZCR) is a not-for-profit association of members who deliver skills, knowledge, insight, time and commit to promoting the science and practice of the medical specialties of clinical radiology (diagnostic and interventional) and radiation oncology in Australia and New Zealand. The Faculty of Clinical Radiology, RANZCR, is the peak bi-national body for setting, promoting and continuously improving the standards of training and practice in diagnostic and interventional August 2020 radiology for the betterment of the people of Australia and New Zealand. The work of the College is scrutinised and externally accredited against industry standard by the Australian Medical Council and the Medical Council of New Zealand. Our Vision RANZCR as the peak group driving best practice in clinical radiology and radiation oncology for the benefit of our patients. Our Mission To drive the appropriate, proper and safe use of radiological and radiation oncological medical services for optimum health outcomes by leading, training and sustaining our professionals. Our Values Commitment to Best Practice Exemplified through an evidence-based culture, a focus on patient outcomes and equity of access to | © | The Royal Australian and New Zealand College of Radiologists® | high quality care; an attitude of compassion and empathy. Acting with Integrity Exemplified through an ethical approach: doing what is right, not what is expedient; a forward thinking and collaborative attitude and patient-centric focus. Accountability Exemplified through strong leadership that is accountable to members; patient engagement at professional and organisational levels. Code of Ethics The Code defines the values and principles that underpin the best practice of clinical radiology and radiation oncology and makes explicit the standards of ethical conduct the College expects of its members. f Image Compression in Diagnostic Imaging, Version 2 1. INTRODUCTION Medical imaging is integral to the provision of modern medical care; it provides a wealth of information that is increasingly relied upon in the clinical management of patients and treatment planning. Medical imaging data is acquired at some cost to the community in terms of radiation exposure, and the human resources, infrastructure and time involved in the acquisition of medical images. Once Guideline for the Use o acquired, such information should not be discarded lightly, and indeed should be preserved in such a way, and for such a period of time, as to maximise the value gained from the initial imaging procedure, and minimise the risk of unnecessary repeat tests. Advances in technology have created the opportunity for radiology systems to use complex compression algorithms to reduce the file size of each image in an effort to partially offset the Page 4 of 8 increase in data volume created by new or more complex modalities. The purpose of this document is to provide guidance to radiologists about the appropriate levels of compression medical imaging practices may use. 2. DATA COMPRESSION IN MEDICAL IMAGING Considerable effort has been expended in the wider community to produce destructive (irreversible or lossy) methods of image compression that still allow for the convenient transmission of large datasets with minimal appreciable loss of data fidelity. Examples include MP3 music files, JPEG photos and MPEG movies. August 2020 Broadly speaking, there are currently two main groups of image compression options available in medical imaging: lossless and lossy compression. Lossless, or reversible compression is intended to reduce the size of the original image data set and so speed up image transmission and reduce required data storage space1,2. The image obtained after compression and then decompression is identical to the original image1,2. Typical compression ratios lege of Radiologists® | achieved range from 1.5:1 to 3.6:1. Lossy, or irreversible compression techniques use algorithms which can compress images at much higher compression ratios than are achievable using lossless compression, resulting in faster image transmission speeds and smaller image storage space requirements1. With these techniques, the regenerated image is not guaranteed to be identical to the original image, as certain elements may have been removed when reducing the image size1,2; that is, some data are lost during the compression process, and some distortion may occur when the image is decompressed. Typical compression ratios achieved range from 5:1 to 50:1. In the wider community, lossy data compression is often acceptable. Data from medical imaging examinations, however, may be put to many different uses, with potentially different requirements for fidelity: | © | The Royal Australian and New Zealand Col • Extensive post-processing of large datasets (both from CT & MRI) is commonplace and will become more so with the wider deployment of ‘artificial intelligence’ techniques. • Semi-automated analysis of large datasets is commonplace and often necessary (e.g. breast MRI) • Automated follow-up for lung nodules and other pathology requires repeat access to the complete 3D dataset • Accurate, serial studies are often key to appropriate clinical management decisions A loss of clinical data either compromises or has the potential to compromise the value of an imaging examination to a patient. 3. APPROPRIATE APPLICATION OF DATA COMPRESSION 3.1 Inconsistency in approaches internationally A number of studies overseas1,2,3,4 have examined the issue of lossy compression in medical imaging. There are numerous examples of studies testing the acceptable limits of image compression ratios, for many different modalities. While a general conclusion could be drawn that some levels of lossy compression are suitable for some purposes and some modalities (“Diagnostically Acceptable Image Compression”1), there remains considerable uncertainty as to exactly what level and type of compression is enough, or too much, for any particular examination or modality. For example, while compression of digital mammograms is not permitted in the USA by the Food and Drug Administration, the Royal College of Radiologists, Guideline for the Use of Image Compression in Diagnostic Imaging, Version 2 and the German Radiology Society in Europe, have published acceptable lossy compression ratios of 20:1 and 15:1, respectively. Page 5 of 8 The European Society for Radiology collated guidelines for “Diagnostically Acceptable Image Compression” in its position paper of 20111. The RCR has subsequently withdrawn, and not replaced, its guidance on image compression; its recommendations were very similar to those of the German Roentgen Society4. The Canadian2 and German guidance remains current, as per an RCR guideline of 2008, (quoted in references (2) and (7), but subsequently withdrawn in May 2018); In general, the Canadian guidance permits higher levels of compression. There remains considerable uncertainty as to the best metric for describing the quality of lossy compression. The compression ratio is widely used because it is readily available and can be August 2020 used to directly assess the effect of compression on storage requirements. However, it correlates poorly with measures of image quality. Other measures, such as ‘Peak Signal to Noise Ratio’ and ‘Structural Similarity’ have been tested, but there is no consensus on the most useful tool (see discussion in section 1). A European review1 has noted that multiple cycles of lossy compression and compression have the potential to cause cumulative degradation of the image, especially if different algorithms are used, and therefore recommends against this practice. 3.2 Emerging technology The majority of studies to date have concentrated on 2D image compression, since the commonly available (and commercially implemented) algorithms are thus focused. It has, however, been shown that new algorithms are required to best deal with the particular needs of 3D datasets5. It has also been shown that lossy compression produces visible difference in 3D images at relatively low compression ratios, though it is as