Development and Evaluation of a Semi-automated Segmentation Tool and a Modified Ellipsoid Formula for Volumetric Analysis of the Kidney in Non-contrast T2-Weighted MR Images

Reliable kidney size determination by magnetic resonance imaging in pathophysiological settings

AIM

Kidney diseases constitute a major health challenge, which requires noninvasive imaging to complement conventional approaches to diagnosis and monitoring. Several renal pathologies are associated with changes in kidney size, offering an opportunity for magnetic resonance imaging (MRI) biomarkers of disease. This work uses dynamic MRI and an automated bean-shaped model (ABSM) for longitudinal quantification of pathophysiologically relevant changes in kidney size.

METHODS

A geometry-based ABSM was developed for kidney size measurements in rats using parametric MRI (T₂ , T₂ * mapping). The ABSM approach was applied to longitudinal renal size quantification using occlusion of the (a) suprarenal aorta or (b) the renal vein, (c) increase in renal pelvis and intratubular pressure and (d) injection of an X-ray contrast medium into the thoracic aorta to induce pathophysiologically relevant changes in kidney size.

RESULTS

The ABSM yielded renal size measurements with accuracy and precision equivalent to the manual segmentation, with >70-fold time savings. The automated method could detect a ~7% reduction (aortic occlusion) and a ~5%, a ~2% and a ~6% increase in kidney size (venous occlusion, pelvis and intratubular pressure increase and injection of X-ray contrast medium, respectively). These measurements were not affected by reduced image quality following administration of ferumoxytol.

CONCLUSION

Dynamic MRI in conjunction with renal segmentation using an ABSM supports longitudinal quantification of changes in kidney size in pathophysiologically relevant experimental setups mimicking realistic clinical scenarios. This can potentially be instrumental for developing MRI-based diagnostic tools for various kidney disorders and for gaining new insight into mechanisms of renal pathophysiology.

Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network

PURPOSE

Total kidney volume (TKV) is an important measure in renal disease detection and monitoring. We developed a fully automated method to segment the kidneys from T₂ -weighted MRI to calculate TKV of healthy control (HC) and chronic kidney disease (CKD) patients.

METHODS

This automated method uses machine learning, specifically a 2D convolutional neural network (CNN), to accurately segment the left and right kidneys from T₂ -weighted MRI data. The data set consisted of 30 HC subjects and 30 CKD patients. The model was trained on 50 manually defined HC and CKD kidney segmentations. The model was subsequently evaluated on 50 test data sets, comprising data from 5 HCs and 5 CKD patients each scanned 5 times in a scan session to enable comparison of the precision of the CNN and manual segmentation of kidneys.

RESULTS

The unseen test data processed by the 2D CNN had a mean Dice score of 0.93 ± 0.01. The difference between manual and automatically computed TKV was 1.2 ± 16.2 mL with a mean surface distance of 0.65 ± 0.21 mm. The variance in TKV measurements from repeat acquisitions on the same subject was significantly lower using the automated method compared to manual segmentation of the kidneys.

CONCLUSION

The 2D CNN method provides fully automated segmentation of the left and right kidney and calculation of TKV in <10 s on a standard office computer, allowing high data throughput and is a freely available executable.

Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI

Purpose

Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical standard.

Methods

Using high-quality software available for academic purposes, we ran a comparative study of manual versus semi-automated segmentation of VS on MRI with 5 clinicians and scientists. We gathered both quantitative and qualitative data to compare the two approaches; including segmentation time, segmentation effort and segmentation accuracy.

Results

We found that the selected semi-automated segmentation approach is significantly faster (167 s vs 479 s, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001), less temporally and physically demanding and has approximately equal performance when compared with manual segmentation, with some improvements in accuracy. There were some limitations, including algorithmic unpredictability and error, which produced more frustration and increased mental effort in comparison with manual segmentation.

Conclusion

We suggest that semi-automated segmentation could be applied clinically for volumetric measurement of VS on MRI. In future, the generic software could be refined for use specifically for VS segmentation, thereby improving accuracy.

Electronic supplementary material

The online version of this article (10.1007/s11548-020-02222-y) contains supplementary material, which is available to authorized users.

Evaluating Magnetic Resonance Spectroscopy as a Tool for Monitoring Therapeutic Response of Whole Brain Radiotherapy in a Mouse Model for Breast-to-Brain Metastasis

Brain metastases are the most common intracranial tumor in adults and are associated with poor patient prognosis and median survival of only a few months. Treatment options for brain metastasis patients remain limited and largely depend on surgical resection, radio- and/or chemotherapy. The development and pre-clinical testing of novel therapeutic strategies require reliable experimental models and diagnostic tools that closely mimic technologies that are used in the clinic and reflect histopathological and biochemical changes that distinguish tumor progression from therapeutic response. In this study, we sought to test the applicability of magnetic resonance (MR) spectroscopy in combination with MR imaging to closely monitor therapeutic efficacy in a breast-to-brain metastasis model. Given the importance of radiotherapy as the standard of care for the majority of brain metastases patients, we chose to monitor the post-irradiation response by magnetic resonance spectroscopy (MRS) in combination with MR imaging (MRI) using a 7 Tesla small animal scanner. Radiation was applied as whole brain radiotherapy (WBRT) using the image-guided Small Animal Radiation Research Platform (SARRP). Here we describe alterations in different metabolites, including creatine and N-acetylaspartate, that are characteristic for brain metastases progression and lactate, which indicates hypoxia, while choline levels remained stable. Radiotherapy resulted in normalization of metabolite levels indicating tumor stasis or regression in response to treatment. Our data indicate that the use of MR spectroscopy in addition to MRI represents a valuable tool to closely monitor not only volumetrical but also metabolic changes during tumor progression and to evaluate therapeutic efficacy of intervention strategies. Adapting the analytical technology in brain metastasis models to those used in clinical settings will increase the translational significance of experimental evaluation and thus contribute to the advancement of pre-clinical assessment of novel therapeutic strategies to improve treatment options for brain metastases patients.

Technical recommendations for clinical translation of renal MRI: a consensus project of the Cooperation in Science and Technology Action PARENCHIMA

Purpose

The potential of renal MRI biomarkers has been increasingly recognised, but clinical translation requires more standardisation. The PARENCHIMA consensus project aims to develop and apply a process for generating technical recommendations on renal MRI.

Methods

A task force was formed in July 2018 focused on five methods. A draft process for attaining consensus was distributed publicly for consultation and finalised at an open meeting (Prague, October 2018). Four expert panels completed surveys between October 2018 and March 2019, discussed results and refined the surveys at a face-to-face meeting (Aarhus, March 2019) and completed a second round (May 2019).

Results

A seven-stage process was defined: (1) formation of expert panels; (2) definition of the context of use; (3) literature review; (4) collection and comparison of MRI protocols; (5) consensus generation by an approximate Delphi method; (6) reporting of results in vendor-neutral and vendor-specific terms; (7) ongoing review and updating. Application of the process resulted in 166 consensus statements.

Conclusion

The process generated meaningful technical recommendations across very different MRI methods, while allowing for improvement and refinement as open issues are resolved. The results are likely to be widely supported by the renal MRI community and thereby promote more harmonisation.

Estimation of split renal function using different volumetric methods: inter- and intraindividual comparison between MRI and CT

PURPOSE

This study aims to determine whether contrast-enhanced (CE)-magnetic resonance imaging (MRI) is comparable to CE-computed tomography (CT) for estimation of split renal function (SRF). For this purpose, two different kidney volumetry methods, the renal cortex volumetry (RCV) and modified ellipsoid volume (MELV), are compared for both acquisition types (CT vs. MRI) with regard to accuracy and reliability, subsequently referred to as RCV_CT/RCV_MRI and MELV_CT/MELV_MRI.

METHODS

This retrospective study included 29 patients (18 men and 11 women; mean age 62.8 ± 12.4 years) who underwent CE-MRI and CE-CT of the abdomen within a period of 3 months. Two independent readers (R1/R2) performed RCV and MELV in all datasets with corresponding semiautomated software tools. RCV was performed with datasets in the arterial phase and MELV in the venous phase. Statistics were calculated using one-way ANOVA, two-tailed Student's t test, Pearson´s correlation, and Bland-Altman plots with p ≤ 0.05 being considered statistically significant.

RESULTS

In all datasets, SRF was almost identical for both volumetry methods with a mean difference of < 1%. Bland-Altman analysis comparing RCV in CT and MRI showed very good agreement for R1/R2. Interreader agreement was strong for RCV_CT and good for RCV_MRI (r = 0.89; r = 0.69). MELV_CT/MRI interreader agreement was only moderate (r = 0.54; r = 0.50) with a high range of values. Intrareader agreement was excellent for all measurements, except MELV_MRI which showed a high mean bias and range of values (RCV_CT: r = 0.93, RCV_MRI: r = 0.98, MELV_CT: r = 0.89, MELV_MRI: r = 0.54).

CONCLUSION

Renal volumetric estimates of SRF are almost as accurate and reliable with CE-MRI as with CE-CT using RCV method. In distinction, the calculation of SRF using MELV was inferior to RCV with respect to accuracy and reliability. Thus, RCV method is recommended to estimate SRF, primarily using CT datasets. However, RCV with MRI datasets for kidney volumetry allows for comparable accuracy and reliability while sparing patients and healthy donors of unnecessary radiation exposure.

Multiparametric Renal Magnetic Resonance Imaging: Validation, Interventions, and Alterations in Chronic Kidney Disease

Background: This paper outlines a multiparametric renal MRI acquisition and analysis protocol to allow non-invasive assessment of hemodynamics (renal artery blood flow and perfusion), oxygenation (BOLD T₂^*), and microstructure (diffusion, T₁ mapping). Methods: We use our multiparametric renal MRI protocol to provide (1) a comprehensive set of MRI parameters [renal artery and vein blood flow, perfusion, T₁, T₂^*, diffusion (ADC, D, D^*, f_p), and total kidney volume] in a large cohort of healthy participants (127 participants with mean age of 41 ± 19 years) and show the MR field strength (1.5 T vs. 3 T) dependence of T₁ and T₂^* relaxation times; (2) the repeatability of multiparametric MRI measures in 11 healthy participants; (3) changes in MRI measures in response to hypercapnic and hyperoxic modulations in six healthy participants; and (4) pilot data showing the application of the multiparametric protocol in 11 patients with Chronic Kidney Disease (CKD). Results: Baseline measures were in-line with literature values, and as expected, T₁-values were longer at 3 T compared with 1.5 T, with increased T₁ corticomedullary differentiation at 3 T. Conversely, T₂^* was longer at 1.5 T. Inter-scan coefficients of variation (CoVs) of T₁ mapping and ADC were very good at <2.9%. Intra class correlations (ICCs) were high for cortex perfusion (0.801), cortex and medulla T₁ (0.848 and 0.997 using SE-EPI), and renal artery flow (0.844). In response to hypercapnia, a decrease in cortex T₂^* was observed, whilst no significant effect of hyperoxia on T₂^* was found. In CKD patients, renal artery and vein blood flow, and renal perfusion was lower than for healthy participants. Renal cortex and medulla T₁ was significantly higher in CKD patients compared to healthy participants, with corticomedullary T₁ differentiation reduced in CKD patients compared to healthy participants. No significant difference was found in renal T₂^*. Conclusions: Multiparametric MRI is a powerful technique for the assessment of changes in structure, hemodynamics, and oxygenation in a single scan session. This protocol provides the potential to assess the pathophysiological mechanisms in various etiologies of renal disease, and to assess the efficacy of drug treatments.