Dynamic MRI and isotope renogram in the functional evaluation of pelviureteric junction obstruction: A comparative study

Sivakumar, Vidyashankar; Indiran, Venkatraman; Sathyanathan, Babu Peter

doi:10.5152/tud.2018.08365

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…For example, as shown in Figure 7 , the change in greyscale intensity over time indicates the rate at which the gadolinium contrast agent reaches the kidney and, thus, an insight into the condition of this organ. Moreover, it might be useful to explore a large-scale analysis of the medulla and cortex time–intensity curves against corresponding DCE-MRI scans in order to develop a method that guides the clinical classification of each kidney directly from the curves [ 35 , 36 ] and, thus, help to establish the likelihood of ureteral obstruction quickly. The respective comparison with ground-truth plots can be located in Appendix A .…”

Section: Discussionmentioning

confidence: 99%

Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function

Asaturyan

Villarini

Sarao

et al. 2021

Sensors

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There is a growing demand for fast, accurate computation of clinical markers to improve renal function and anatomy assessment with a single study. However, conventional techniques have limitations leading to overestimations of kidney function or failure to provide sufficient spatial resolution to target the disease location. In contrast, the computer-aided analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) could generate significant markers, including the glomerular filtration rate (GFR) and time–intensity curves of the cortex and medulla for determining obstruction in the urinary tract. This paper presents a dual-stage fully modular framework for automatic renal compartment segmentation in 4D DCE-MRI volumes. (1) Memory-efficient 3D deep learning is integrated to localise each kidney by harnessing residual convolutional neural networks for improved convergence; segmentation is performed by efficiently learning spatial–temporal information coupled with boundary-preserving fully convolutional dense nets. (2) Renal contextual information is enhanced via non-linear transformation to segment the cortex and medulla. The proposed framework is evaluated on a paediatric dataset containing 60 4D DCE-MRI volumes exhibiting varying conditions affecting kidney function. Our technique outperforms a state-of-the-art approach based on a GrabCut and support vector machine classifier in mean dice similarity (DSC) by 3.8% and demonstrates higher statistical stability with lower standard deviation by 12.4% and 15.7% for cortex and medulla segmentation, respectively.

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Section: Discussionmentioning

confidence: 99%