The quest for "diagnostically lossless" medical image compression using objective image quality measures

Given the explosive growth of digital image data being generated, medical communities worldwide have recognized the need for increasingly efficient methods of storage, display and transmission of medical images. For this reason lossy image compression is inevitable. Furthermore, it is absolutely essential to be able to determine the degree to which a medical image can be compressed before its “diagnostic quality” is compromised. This work aims to achieve “diagnostically lossless compression”, i.e., compression with no loss in visual quality nor diagnostic accuracy. Recent research by Koff et al. has shown that at higher compression levels lossy JPEG is more effective than JPEG2000 in some cases of brain and abdominal CT images. We have investigated the effects of the sharp skull edges in CT neuro images on JPEG and JPEG 2000 lossy compression. We provide an explanation why JPEG performs better than JPEG2000 for certain types of CT images. Another aspect of this study is primarily concerned with improved methods of assessing the diagnostic quality of compressed medical images. In this study, we have compared the performances of structural similarity (SSIM) index, mean squared error (MSE), compression ratio and JPEG quality factor, based on the data collected in a subjective experiment involving radiologists. An receiver operating characteristic (ROC) curve and Kolmogorov-Smirnov analyses indicate that compression ratio is not always a good indicator of visual quality. Moreover, SSIM demonstrates the best performance. We have also shown that a weighted Youden index can provide SSIM and MSE thresholds for acceptable compression. We have also proposed two approaches of modifying L2-based approximations so that they conform to Weber’s model of perception. We show that the imposition of a condition of perceptual invariance in greyscale space according to Weber’s model leads to the unique (unnormalized) measure with density function ρ(t) = 1/t. This result implies that the logarithmic L1 distance is the most natural “Weberized” image metric. We provide numerical implementations of the intensity-weighted approximation methods for natural and medical images

OPTIMAX 2014 - Radiation dose and image quality optimisation in medical imaging

Medical imaging is a powerful diagnostic tool. Consequently, the number of medical images taken has increased vastly over the past few decades. The most common medical imaging techniques use X-radiation as the primary investigative tool. The main limitation of using X-radiation is associated with the risk of developing cancers. Alongside this, technology has advanced and more centres now use CT scanners; these can incur significant radiation burdens compared with traditional X-ray imaging systems. The net effect is that the population radiation burden is rising steadily. Risk arising from X-radiation for diagnostic medical purposes needs minimising and one way to achieve this is through reducing radiation dose whilst optimising image quality. All ages are affected by risk from X-radiation however the increasing population age highlights the elderly as a new group that may require consideration. Of greatest concern are paediatric patients: firstly they are more sensitive to radiation; secondly their younger age means that the potential detriment to this group is greater. Containment of radiation exposure falls to a number of professionals within medical fields, from those who request imaging to those who produce the image. These staff are supported in their radiation protection role by engineers, physicists and technicians. It is important to realise that radiation protection is currently a major European focus of interest and minimum competence levels in radiation protection for radiographers have been defined through the integrated activities of the EU consortium called MEDRAPET. The outcomes of this project have been used by the European Federation of Radiographer Societies to describe the European Qualifications Framework levels for radiographers in radiation protection. Though variations exist between European countries radiographers and nuclear medicine technologists are normally the professional groups who are responsible for exposing screening populations and patients to X-radiation. As part of their training they learn fundamental principles of radiation protection and theoretical and practical approaches to dose minimisation. However dose minimisation is complex – it is not simply about reducing X-radiation without taking into account major contextual factors. These factors relate to the real world of clinical imaging and include the need to measure clinical image quality and lesion visibility when applying X-radiation dose reduction strategies. This requires the use of validated psychological and physics techniques to measure clinical image quality and lesion perceptibility

Patient radiation dose issues resulting from the use of CT in the UK

In this report, COMARE presents a comprehensive review of the radiation dose issues associated with CT scans in the UK. The implications of the increase in the numbers of CT scans in the UK are considered in the report, with focus on the number of younger patients undergoing CT scans, who have greater sensitivity to x-rays. The report provides an update on the radiation protection aspects of justification (balancing risk and benefit) and optimisation (balancing the risk from the radiation dose with the quality of the image)

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

Radiation-induced lung damage (RILD) is a common consequence of thoracic radiotherapy (RT). We present here a novel classification of the parenchymal features of RILD. We developed a deep learning algorithm (DLA) to automate the delineation of 5 classes of parenchymal texture of increasing density. 200 scans were used to train and validate the network and the remaining 30 scans were used as a hold-out test set. The DLA automatically labelled the data with Dice Scores of 0.98, 0.43, 0.26, 0.47 and 0.92 for the 5 respective classes. Qualitative evaluation showed that the automated labels were acceptable in over 80% of cases for all tissue classes, and achieved similar ratings to the manual labels. Lung registration was performed and the effect of radiation dose on each tissue class and correlation with respiratory outcomes was assessed. The change in volume of each tissue class over time generated by manual and automated segmentation was calculated. The 5 parenchymal classes showed distinct temporal patterns We quantified the volumetric change in textures after radiotherapy and correlate these with radiotherapy dose and respiratory outcomes. The effect of local dose on tissue class revealed a strong dose-dependent relationship We have developed a novel classification of parenchymal changes associated with RILD that show a convincing dose relationship. The tissue classes are related to both global and local dose metrics, and have a distinct evolution over time. Although less strong, there is a relationship between the radiological texture changes we can measure and respiratory outcomes, particularly the MRC score which directly represents a patient’s functional status. We have demonstrated the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible

Translation of quantitative MRI analysis tools for clinical neuroradiology application

Quantification of imaging features can assist radiologists by reducing subjectivity, aiding detection of subtle pathology, and increasing reporting consistency. Translation of quantitative image analysis techniques to clinical use is currently uncommon and challenging. This thesis explores translation of quantitative imaging support tools for clinical neuroradiology use. I have proposed a translational framework for development of quantitative imaging tools, using dementia as an exemplar application. This framework emphasises the importance of clinical validation, which is not currently prioritised. Aspects of the framework were then applied to four disease areas: hippocampal sclerosis (HS) as a cause of epilepsy; dementia; multiple sclerosis (MS) and gliomas. A clinical validation study for an HS quantitative report showed that when image interpreters used the report, they were more accurate and confident in their assessments, particularly for challenging bilateral cases. A similar clinical validation study for a dementia reporting tool found improved sensitivity for all image interpreters and increased assessment accuracy for consultant radiologists. These studies indicated benefits from quantitative reports that contextualise a patient’s results with appropriate normative reference data. For MS, I addressed a technical translational challenge by applying lesion and brain quantification tools to standard clinical image acquisitions which do not include a conventional T1-weighted sequence. Results were consistent with those from conventional sequence inputs and therefore I pursued this concept to establish a clinically applicable normative reference dataset for development of a quantitative reporting tool for clinical use. I focused on current radiology reporting of gliomas to establish which features are commonly missed and may be important for clinical management decisions. This informs both the potential utility of a quantitative report for gliomas and its design and content. I have identified numerous translational challenges for quantitative reporting and explored aspects of how to address these for several applications across clinical neuroradiology