Comparison between transperineal ultrasound and digital dectection of levator ani trauma : can we improve the odds?

Aims To investigate the predictive ability of four digital assessment parameters to detect levator ani (LA) muscle defects (avulsion injury) and compare these to transperineal tomographic ultrasound images. Methods This was an observational study imbedded in a larger quasi-experimental cohort study for women with urinary incontinence. Seventy-two women, ≥60 years who had attended or were going to attend physiotherapy for treatment of urinary incontinence, were included in the study. Inclusion criteria from the parent study were symptoms of stress, urge or both types of urinary incontinence. The predictive ability of the following digital parameters: direct palpation of a discontinuity of the LA muscle from insertion on the pubic ramus; palpation of the distance between the muscle insertion sites; palpation of LA strength; palpation of LA tone, were analyzed against findings from tomographic transperineal ultrasound images. Correlation between methods was measured using Cohen's kappa for each of the individual parameters. Results Seventeen women (24%) presented with a complete or partial avulsion of the puborectalis muscle as diagnosed with tomographic ultrasound imaging. Nine women (13%) had complete avulsions, one of which was bilateral. The predictive ability of the digital assessment parameters varied from poor (k = 0.187, 95% CI [0.02–0.36]) to moderate (k = 0.569, 95% CI [0.31–0.83]). The new parameter of ‘width between insertion sites’ performed best. Conclusions Adding the parameter of “width between insertion sites” appears to enhance our ability to detect avulsion of the levator ani (LA) muscle by digital examination however it does not distinguish between unilateral or bilateral avulsion.

Improving minimum rate predictors algorithm for compression of volumetric medical images

Medical imaging technologies are experiencing a growth in terms of usage and image resolution, namely in diagnostics systems that require a large set of images, like CT or MRI. Furthermore, legal restrictions impose that these scans must be archived for several years. These facts led to the increase of storage costs in medical image databases and institutions. Thus, a demand for more efficient compression tools, used for archiving and communication, is arising. Currently, the DICOM standard, that makes recommendations for medical communications and imaging compression, recommends lossless encoders such as JPEG, RLE, JPEG-LS and JPEG2000. However, none of these encoders include inter-slice prediction in their algorithms. This dissertation presents the research work on medical image compression, using the MRP encoder. MRP is one of the most efficient lossless image compression algorithm. Several processing techniques are proposed to adapt the input medical images to the encoder characteristics. Two of these techniques, namely changing the alignment of slices for compression and a pixel-wise difference predictor, increased the compression efficiency of MRP, by up to 27.9%. Inter-slice prediction support was also added to MRP, using uni and bi-directional techniques. Also, the pixel-wise difference predictor was added to the algorithm. Overall, the compression efficiency of MRP was improved by 46.1%. Thus, these techniques allow for compression ratio savings of 57.1%, compared to DICOM encoders, and 33.2%, compared to HEVC RExt Random Access. This makes MRP the most efficient of the encoders under study.

Prospective Estimation of Organ Dose Prior to Examination: Matching Patient to Digital Phantoms

Prospective estimation of patient CT organ dose prior to examination can help technologist adjust CT scan settings to reduce radiation dose to patient while maintaining certain image quality. One possible way to achieve this is matching patient to digital models precisely. In previous work, patient matching was performed manually by matching the trunk height which was defined as the distance from top of clavicle to bottom of pelvis. However, this matching method is time consuming and impractical in scout images where entire trunk is not included. Purpose of this work was to develop an automatic patient matching strategy and verify its accuracy.