The use of 3D imaging solutions in the modern medical field provides practitioners with he ability to analyse internal body tissues non-invasively which provides vital data for various therapeutic approaches.
In this case the use of MRI scans from three patients suffering from renal artery stenosis (RAS) necessitated the use of volume estimation methods to determine kidney size prior to and after the application of revascularisation therapy. The review utilised ImageJ software to conduct a volume estimation using segmentation methods. This was informed by an analysis of the literature which indicated the viability of imaging alongside segmentation and total renal volume (TRV) analyses in estimating the sizes of organs and malignant tissues across the medical field. The analysis indicated an increase of 22.8% in Patient 1s right kidney and 21.5% and 4.7% respectively in Patient 2s right and left kidneys. Patient 3s case highlighted a possibility of irreversible damage since the patients left kidney showed an 11.9% decrease in kidney size. However there were also considerations for further review of Patient 2s left kidney due to the comparatively lower growth rate.
Introduction3D imaging solutions are a relatively new entrant to the medical sector but are accompanied by enormous improvements in diagnostic methods and therapeutic practice. However these advancements are also accompanied by difficulties in establishing the accuracy of these tests due to a number of method-specific challenges. The variety of approaches available in the field is comparable to the number of ailments that these methods seek to analyse. In this case the three patient cases under review involved renal artery stenosis (RAS) which necessitated the selection and implementation of an imaging strategy that can achieve optimal results in highlighting the efficacy or lack thereof of the therapies applied. For this ailment the primary therapeutic strategy aims at revascularisation therapy whereby a stent is used as a means of improving blood flow by opening up arterial pathways and increasing renal blood flow (Cheong et al. 2007). In this activity the availability of imaging strategies becomes instrumental in analysing the efficacy of the interventions whereby the use of volume estimates provided actionable information on the resultant changes to the patients kidney sizes.
To remedy the symptoms of their RAS ailments Patient 1 (P1) received a unilateral stent in the right renal artery Patient 2 (P2) received bilateral stents and Patient 3 (P2) received a unilateral stent in the left renal artery. Lesage et al. (2009) provide a review of imaging models and techniques with vascular segmentation arising as a key method due to its ability to reduce the manual element while also reducing the variability of results across applications. However it is vital to also note that this there are a variety of ways to conduct angiographies whereby additional modalities such as traditional x-ray scanning magnetic resonance angiograms (MRAs) computed tomography angiograms (CTAs) and ultrasounds among others (Lesage et al. 2009). This review focused on the 3D approaches of MRI sequencing with the analytical methods being applied based on the subsequent images. Considering the implications to the patients treatment it was thereby necessary to analyse the efficacy of the selected models and comparative approaches to ensure an effective measurement and conclusion on the outcomes of the individual patient therapies.
Segmentation analysis is a vital tool for analysing and abstracting individual elements in MRI imaging due to its focus on labelling individual pixels to increase the ability to quantify focus areas in these images (Cohen et al. 2007). Thresholding features as the most universal of these methods due to its simplicity whereby the images are converted into 8-bit binary images to improve the ability to group pixels as members of individual groups using evolutionary algorithms and fuzzy logic rules (Ertekin 2011). On the other hand clustering methods concentrate on using cluster centres as a means for determining pixel membership whereby there is a focus on assigning groupings to pixels based on their proximity to the centre (Zllner et al. 2012). Even with these approaches in existence the addition of compression-based models is instrumental in its ability to include lossless compression algorithms in its extraction of image clusters based on the shortest coding length (Al-Attar et al. 2007). However the ease of use and inclusion of fuzzy logic considerations made imperative to use the thresholding segmentation method in this analysis.MethodsThe analysis required the use of ImageJ software whereby version 1.6.0_24 was downloaded from the NIH websi
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