Multiparametric Functional MRI Of The Kidney: Current State And Future Trends With Deep Learning Approaches Ⅱ

III. Data postprocessing and analysis: new strategies with deep learning 

With multiparametric fMRI, a vast amount of data can be generated. How should this quantity of data be handled? How can this information be directly extracted?

Data postprocessing and analysis is a topic rarely described in detail in most clinical studies regarding functional renal imaging. 

▶ Fig. 1 Multiparametric renal functional imaging of a volunteer showing multiple slices of different functional parameters. Tissue characterization with T1 mapping. B BOLD MRI with T2* mapping. C Assessing microstructure with ADC mapping. D Perfusion imaging with ASL (renal blood flow (RBF) mapping).

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Nonetheless, it is one of the main limiting factors for use in clinical practice, since it can have a significant impact on data interpretation. For the successful application of multiparametric fMRI protocols in the clinical routine and studies of large patient cohorts in the future, standardized data postprocessing, and data analysis workflows are needed.

for machine learning applications on renal MRI and for “bridging the gap between data and technology” [48].

▶ Fig. 2 Image processing pipeline depicting different application steps for deep learning approaches in multiparametric renal MRI

The term “deep learning” refers to a deep network of multilayer neural networks to analyze data. The main difference of the DL approach compared to other machine learning techniques is its

from data acquisition to computer-aided diagnosis.

 ▶ Fig. 2 gives an overview of the different application steps for deep learning approaches.

Starting from MR image acquisition, DL can be implemented in the process of image reconstruction to significantly improve robustness, accuracy, and image quality from undersampled k-space data as well as to optimize speed compared to conventional reconstruction approaches [51–56]. Impressive results have been seen for instance in dynamic MR image reconstruction of cardiac MRI, where real-time image reconstruction [57] and 4 D DL reconstruction networks have been developed [58]. Also, CNN-based methods can assist in the detection of artifacts [59], prospective motion correction [60], and image denoising [61–63]. In image super-resolution, deep learning techniques are implemented for the reconstruction of higher-resolution images or image sequences from low-resolution images [64–66]. Further areas of application include image synthesis to derive new parametric images of tissue contrast from a collection of MR acquisitions [67, 68], quantitative susceptibility mapping (QSM) to noninvasively estimate the magnetic susceptibility of biological tissue [69, 70], and MR fingerprinting (MRF) [71]. 

Multiparametric renal fMRI poses several challenges for data analysis regarding registration and segmentation, where DL seems especially promising to boost further development and the path to the clinical routine. First, renal parenchyma is difficult to differentiate from the surrounding organs and structures only by signal intensity. Secondly, multiparametric fMRI encompasses heterogeneous signal contrasts and image qualities. Furthermore, kidneys can vary dramatically in their anatomical position, size, and features, such as cysts. Last but not least, motion due to breathing leads to a considerable variation in the position of the kidneys not only between but also within measurements. Image registration and segmentation, therefore, are a precondition for efficient analysis of multiparametric fMRI data.

Image registration implies spatial alignment of intra- and inter-subject kidney images to enable further processing steps. There is a range of strategies for image registration with different approaches, which can be grouped into image acquisition techniques and post-processing methods [72]. The emerging use of DL shows the greatest potential to contribute to more efficient image registration, thereby surpassing standard deformable registration algorithms in accuracy and speed. Still, application to renal MRI is pending, which might also be attributed to the lack of public datasets and validation protocols. The application of newly developed methods to other imaging modalities and organs, however, seems promising for the transfer to renal MRI [50].

For quantitative analysis of multiparametric fMRI in the kidneys, organ segmentation for the assessment of total kidney volume (TKV) but also kidney compartments including the cortex and medulla is an essential step. Due to the challenges of renal multiparametric fMRI as described above, manual segmentation has been the prevalent segmentation technique in renal MRI studies. However, for clinical use of renal fMRI, this time-consuming and laborious method needs to be replaced by more efficient segmentation techniques. Besides other semi-automatic and automatic segmentation techniques such as image processing and model-based image segmentation, machine learning and especially deep learning approaches have again been shown to be the most promising to deal with the more complex multiparametric datasets [73]. DL has already been applied in a few studies for the segmentation of the kidneys to estimate TKV [74–78].

Going even further beyond the tasks of image pre-and post-processing, DL can be implemented for computer-aided diagnosis. By combining the ability to analyze imaging data and retrieve clinical information, automated classification systems can be developed to assist clinical diagnostics, as has been successfully shown in the diagnosis of prostate cancer, for example [79, 80]. In kidney diagnostics, DL methods have been demonstrated to assist in the diagnosis of transplant rejection with fMRI and the integration of clinical data, thereby forming a computer-assisted diagnostic (CAD) system for the assessment of kidney function including DWI, BOLD, and creatinine clearance [81–83]. Another application of DL is the differentiation of renal cell carcinoma [84, 85].

IV. Discussion 

Renal MRI is an emerging technique that has not yet been established in the clinical routine. Several other more established imaging techniques with different strengths and weaknesses are usually preferred by clinicians 

The most commonly applied method for renal imaging is ultrasonography. It is also a non-invasive, non-ionizing technique, which provides the ability to dynamically image morphological abnormalities with high resolution, to measure blood flow with the Doppler method, and to apply a safe contrast agent to visualize perfusion without harming the kidney [86–89]. In contrast to MRI, it is widely available and cost-efficient [90]. However, image quality is operator-dependent and can be considerably reduced by gas between the transducer and the organ of interest or by the obesity of subjects. Measurements and images are more difficult to reproduce and quantification is feasible only to a limited extent [2, 91, 92].

Like MRI, CT is a tomographical imaging technique that uses radiation to obtain images. Even though MRI techniques have become a lot more time-efficient in the past decades, CT is still much faster than MRI and is more cost-effective [93, 94]. Apart from morphological and angiographical imaging, CT has the ability to measure renal blood flow and perfusion as well as GFR and tubular function [2]. The major drawback of CT is the need for a nephrotoxic contrast agent for most tasks besides detecting renal obstruction, which limits its use for kidney diseases [95].

Functional renal imaging in the clinical routine also includes renal scintigraphy. It is the gold standard for measuring glomerular filtration and tubular function [92] and enables accurate evaluation of split function and renal obstruction. Nonetheless, image resolution and quality are very poor in comparison to other imaging modalities, and diagnostic value is limited.

Most clinicians are not aware of the potential of MRI to offer functional imaging of the kidneys even without the administration of contrast media. But there are also several disadvantages to using MRI that might present an obstacle to broader application. The leading drawback of MRI is the limited availability, especially in smaller hospitals, and the cost expenditure associated with purchase and maintenance [90]. Renal MRI can be performed with 1.5 and 3 Tesla, although studies have shown the benefits of 3 Tesla for SNR, examination time, and spatial resolution [9]. Moreover, the use of MRI needs experienced operators. When using multiparametric fMRI protocols to examine kidneys, there is still a need for standardized procedures, protocols, and postprocessing as well as more offers by medical technology companies [6]. Contrary to general belief, the examination time of MRI has been reduced significantly in the past decades and single parameters can be measured in a few minutes. Furthermore, breathing strategies such as breath-hold, respiratory-triggered, or free-breathing imaging can be adapted to the patient's condition for most sequences [9]. Last but not least, relative contraindications such as pacemakers and cochlear implants have to be considered.

Nonetheless, MRI has a lot to offer and might help to reduce the costs of multiple sometimes even invasive diagnostic examinations. Besides high-resolution anatomical imaging, the range of functional parameters offered by MRI is exceptional and can be achieved mainly without the use of a contrast agent. Examination protocols can be adapted to the clinical issue, kidney disease, and patient condition and are suited for both short-term and long-term monitoring. Eventually, the aim of developing multiparametric fMRI for the kidneys is not to replace established techniques such as ultrasonography and renal scintigraphy, but to broaden and improve renal imaging and help clinicians and patients in the treatment of kidney diseases.

V. Summary 

Multiparametric functional MRI of the kidneys is a promising approach for assessing renal function and pathophysiology. Combinations of perfusion, diffusion, and BOLD imaging together with techniques for tissue characterization such as T1 and T2 mapping and further MR biomarkers can be selected depending on the clinical question and the kidney pathology to gain more comprehensive insight into the cause and consequences of diseases and the effect of therapeutic interventions. However, several obstacles have to be overcome before the method can be implemented in the clinical routine. On the one hand, there is a need for standardization of fMRI protocols to enable comparability of studies and to facilitate clinical application. On the other hand, new strategies for handling the emergence of huge amounts of data are necessary. Recent advances in DL techniques open new possibilities for data postprocessing and analysis and might give the clinical use of renal fMRI a decisive push forward. More studies examining different kidney pathologies with larger cohorts and longitudinal design with the implementation of a standardized workflow for data acquisition, postprocessing, and analysis are needed to further improve and at the same time demonstrate the ability of multi-parametric fMRI to enhance the diagnostic imaging of the kidneys.

Conflict of Interest

The authors declare that they have no conflict of interest. 

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