Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI

Background: Prostate cancer is one of the most common forms of cancer found in males making early diagnosis important. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with the use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity inhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these inhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification and noise level variations. Methods: In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity corrected endorectal MRI which allows for effective noise compensation and preservation of details within the prostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting non-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available. Results: SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement of 11.7 dB and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared to existing approaches. Conclusions: A new noise compensation method was developed for the purpose of improving the quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise compensation performance can be achieved for the proposed approach, which is particularly important for processing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.Comment: 23 page

Separating Technological and Clinical Safety Assurance for Medical Devices

The safety and clinical effectiveness of medical devices are closely associated with their specific use in clinical treatments. Assuring safety and the desired clinical effectiveness is challenging. Different people may react differently to the same treatment due to variability in their physiology and genetics. Thus, we need to consider the outputs and behaviour of the device itself as well as the effect of using the device to treat a wide variety of patients. High-intensity focused ultrasound systems and radiation therapy machines are examples of systems in which this is a primary concern. Conventional monolithic assurance cases are complex, and this complexity affects our ability to address these concerns adequately. Based on the principle of separation of concerns, we propose separating the assurance of the use of these types of systems in clinical treatments into two linked assurance cases. The first assurance case demonstrates the safety of the manufacturer's device independent of the clinical treatment. The second demonstrates the safety and clinical effectiveness of the device when it is used in a specific clinical treatment. We introduce the idea of these separate assurance cases, and describe briefly how they are separated and linked