<i>In Vivo</i> Detection of Chronic Kidney Disease Using Tissue Deformation Fields From Dynamic MR Imaging

Hodneland, Erlend; Keilegavlen, Eirik; Hanson, Erik Andreas; Andersen, Erling D.; Monssen, Jan Ankar; Rørvik, Jarle; Leh, Sabine; Marti, Hans-Peter; Lundervold, Arvid; Svarstad, Einar; Nordbotten, Jan Martin

doi:10.1109/tbme.2018.2879362

Cited by 19 publications

(10 citation statements)

References 31 publications

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“…In forced deep breathing translations, up to 86 mm are reported [42]. The deformation component is more difficult to estimate and the extent of expected deformation is currently not clearly evaluated, although it has been shown that the kidney shape variability can be modeled using an elastic model [43] or an active shape model [44]. In clinical practice, it is considered that the extent of deformation is negligible and a rigid model is sufficient for reaching the correct diagnosis [20,40].…”

Section: Geometric Transformation Modelmentioning

confidence: 99%

Image registration in dynamic renal MRI—current status and prospects

Zöllner

Serifovic-Trbalic

Kabelitz

et al. 2019

Magn Reson Mater Phy

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Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today’s limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.

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Section: Geometric Transformation Modelmentioning

confidence: 99%

Image registration in dynamic renal MRI—current status and prospects

Zöllner

Serifovic-Trbalic

Kabelitz

et al. 2019

Magn Reson Mater Phy

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“…21 While the current gold standard for diagnosis of CKD is a renal biopsy, recent studies present magnetic resonance imaging (MRI) as a less invasive alternative. 22 Implementation of a non-invasive modality, such as MRI, is proposed to decrease the number of undiagnosed cases of CKD in the population. 22 The magnetic resonance (MR) technique provides broad spatial coverage compared to traditional tissue biopsy and allows for detailed analysis of atherosclerosis associated with CKD.…”

Section: Discussionmentioning

confidence: 99%

Chronic Kidney Disease: Detection and Evaluation

Pettitt

Brumbaugh

Gartman

et al. 2020

OFP

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Chronic kidney disease (CKD) is a prevalent disease that continues to affect more than one-tenth of the American population. Early detection is essential to slow the natural progression of CKD. This can be accomplished by urine and blood screening tests, which are analyzed for creatinine, urine albumin, and urine protein. Screening is often indicated for individuals with known comorbidities such as cardiovascular disease, mineral and bone disorders, and diabetes. Asymptomatic patients with early renal disease can make detection problematic, requiring clinicians to recognize risk factors that may warrant further testing. When symptoms do appear, the renal manifestations are often broad, including changes in kidney size, electrolyte abnormalities, and proteinuria. Changes in biomarkers may be evaluated in the early stages of CKD before significant kidney damage. The current, most accurate determination of renal function is the estimated glomerular filtration rate (GFR), which must be less than 60 mL/min to prompt further testing for CKD. Novel biomarkers may allow for earlier diagnosis of CKD as they can be detected at lower levels than standard biomarkers. Biomarkers such as homocysteine, cystatin C, and kidney injury molecule-1 are predicted to become more prevalent in a clinical setting. The current gold standard for diagnosis of CKD is a renal biopsy, but MRI is a less invasive alternative. Proper staging of CKD allows for appropriate evaluation and treatment of the patient. The early stages of CKD should be treated to limit complications and to prolong the life and health of patients.

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“…To sum up, when f ( x ) > 0, it indicates that the sample is marked +1 and is in the same category as samples marked with “+1”; otherwise, it indicates that the sample is marked −1 and is in the same category as samples marked with “−1”. Linear hyper planes [ 30 ] cannot properly identify data points when training data include noise. Slack variables ξ i are introduced to the constraint, resulting in a modification of the original (3):

…”

Section: Support Vector Machinementioning

confidence: 99%

Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease

et al. 2022

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Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.

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In Vivo Detection of Chronic Kidney Disease Using Tissue Deformation Fields From Dynamic MR Imaging

Cited by 19 publications

References 31 publications

Image registration in dynamic renal MRI—current status and prospects

Image registration in dynamic renal MRI—current status and prospects

Chronic Kidney Disease: Detection and Evaluation

Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease

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