Multiparametric renal MRI: an intrasubject test-retest repeatability study

Background: Renal multiparametric magnetic resonance imaging (MRI) is a promising tool for diagnosis, prognosis, and treatment monitoring in kidney disease.Purpose: To determine intrasubject test-retest repeatability of renal MRI measurements.Study Type: Prospective.Population: Nineteen healthy subjects aged over 40 years.Field Strength/Sequences: T-1 and T-2 mapping, R-2* mapping or blood oxygenation level-dependent (BOLD) MRI, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI), 2D phase contrast, arterial spin labelling (ASL), dynamic contrast enhanced (DCE) MRI, and quantitative Dixon for fat quantification at 3T.Assessment: Subjects were scanned twice with similar to 1 week between visits. Total scan time was similar to 1 hour. Postprocessing included motion correction, semiautomated segmentation of cortex and medulla, and fitting of the appropriate signal model. Statistical Test: To assess the repeatability, a Bland-Altman analysis was performed and coefficients of variation (CoVs), repeatability coefficients, and intraclass correlation coefficients were calculated.Results: CoVs for relaxometry (T-1, T-2, R-2*/BOLD) were below 6.1%, with the lowest CoVs for T-2 maps and highest for R-2*/BOLD. CoVs for all diffusion analyses were below 7.2%, except for perfusion fraction (FP), with CoVs ranging from 18-24%. The CoV for renal sinus fat volume and percentage were both around 9%. Perfusion measurements were most repeatable with ASL (cortical perfusion only) and 2D phase contrast with CoVs of 10% and 13%, respectively. DCE perfusion had a CoV of 16%, while single kidney glomerular filtration rate (GFR) had a CoV of 13%. Repeatability coefficients (RCs) ranged from 7.7-87% (lowest/highest values for medullary mean diffusivity and cortical FP, respectively) and intraclass correlation coefficients (ICCs) ranged from -0.01 to 0.98 (lowest/highest values for cortical FP and renal sinus fat volume, respectively).Data Conclusion: CoVs of most MRI measures of renal function and structure (with the exception of FP and perfusion as measured by DCE) were below 13%, which is comparable to standard clinical tests in nephrology.Cardiovascular Aspects of Radiolog

Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy.

To enable magnetic resonance imaging (MRI)-guided radiotherapy with real-time adaptation, motion must be quickly estimated with low latency. The motion estimate is used to adapt the radiation beam to the current anatomy, yielding a more conformal dose distribution. As the MR acquisition is the largest component of latency, deep learning (DL) may reduce the total latency by enabling much higher undersampling factors compared to conventional reconstruction and motion estimation methods. The benefit of DL on image reconstruction and motion estimation was investigated for obtaining accurate deformation vector fields (DVFs) with high temporal resolution and minimal latency. 2D cine MRI acquired at 1.5 T from 135 abdominal cancer patients were retrospectively included in this study. Undersampled radial golden angle acquisitions were retrospectively simulated. DVFs were computed using different combinations of conventional- and DL-based methods for image reconstruction and motion estimation, allowing a comparison of four approaches to achieve real-time motion estimation. The four approaches were evaluated based on the end-point-error and root-mean-square error compared to a ground-truth optical flow estimate on fully-sampled images, the structural similarity (SSIM) after registration and time necessary to acquire k-space, reconstruct an image and estimate motion. The lowest DVF error and highest SSIM were obtained using conventional methods up to [Formula: see text]. For undersampling factors [Formula: see text], the lowest DVF error and highest SSIM were obtained using conventional image reconstruction and DL-based motion estimation. We have found that, with this combination, accurate DVFs can be obtained up to [Formula: see text] with an average root-mean-square error up to 1 millimeter and an SSIM greater than 0.8 after registration, taking 60 milliseconds. High-quality 2D DVFs from highly undersampled k-space can be obtained with a high temporal resolution with conventional image reconstruction and a deep learning-based motion estimation approach for real-time adaptive MRI-guided radiotherapy

Motion management for MRI-guided abdominal radiotherapy

The recent introduction of hybrid MRI-linac (MRL) systems will drive a paradigm shift in radiotherapy. For the first time in history we have the ability to visualize both the tumor and surrounding healthy tissue before and during treatment in real time. The first high-field MRL, which was developed within the department of radiotherapy at the UMC Utrecht, in cooperation with Philips and Elekta, successfully treated the first patients in May 2017 at the UMC Utrecht. MR-guided radiotherapy has the potential to boost successful treatment outcomes by decreasing uncertainties in tumor detection, location, and shape through unprecedented soft-tissue contrast. For abdominal tumors this could induce more conformal dose distributions using precies pre-treatment imaging, real-time image feedback, and accurate dose calculations in a large field-of-view. To maximize this potential, accurate MR image guidance in all stages of treatment is essential. A general challenge for these tumors is physiological-induced motion, such as respiration. This thesis described various acquisitions, reconstruction and post-processing methods for managing this motion in pre-treatment, pre-beam, beam-on and post-beam phase of an MR-guided abdominal radiotherapy treatment. First, a method is described for pre-treatment and pre-beam 4D-MRI motion characterization, based on a volumetric radial stack-of-stars (SOS) acquisition. It is shown that this sampling, in combination with an internal surrogate, is a robust method to generate phase-resolved 4D-MRIs. Second, the radial SOS sampling is used as a motion compensation method in the presence of bulk motion for robust free-breathing abdominal imaging. Using the free-induction decay signal, bulk motion is automatically detected and excluded in real time. It is shown that this increases image quality, reduces artifacts and results in an overall increase in acquisition robustness. Third, a motion model is introduced to generate volumetric MRI with high spatio-temporal resolution, so-called volumetric cine-MRI. Using the aforementioned 4D-MRI acquisition, a motion model is generated by parameterizing the underlying motion. Subsequently, 3D volumes are generated by filling in the missing volumetric information of fast 2D beam-on cine-MR images using the model. Fourth, these volumetric cine-MRIs are used to calculate the accumulated dose of abdominal treatments. It is shown that precise imaging with sufficient temporal resolution is required for accurate dose tracking in abdominal tumors and both fast and slow variations in breathing should be taken into account. Last, a mathematical framework is outlined to optimize acceleration parameters of simultaneous multi-slice acquisitions that can accelerate pre-treatment and pre-beam imaging, or increase volumetric coverage of beam-on imaging. By optimizing these acceleration parameters, higher acceleration can be accomplished, while maintaining similar signal-to-noise ratios. The presented methods can directly add valuable information for all stages of an MR-guided abdominal radiotherapy treatment. Ultimately, these methods can aid real-time image guidance and online plan adaptation to improve treatment outcomes

Assessment of 3D motion modeling performance for dose accumulation mapping on the MR-linac by simultaneous multislice MRI

Hybrid MR-linac systems enable intrafraction motion monitoring during radiation therapy. Since time-resolved 3D MRI is still challenging, various motion models have been developed that rely on time-resolved 2D imaging. Continuous validation of these models is important for accurate dose accumulation mapping. In this study we used 2D simultaneous multislice (SMS) imaging to improve the PCA-based motion modeling method developed previously (Stemkens et al 2016 Phys. Med. Biol. 61 5335-55). From the additional simultaneously acquired slices, several independent motion models could be generated, which allowed for an assessment of the sensitivity of the motion model to the location of the time-resolved 2D slices. Additionally, the best model could be chosen at every time-point, increasing the method's robustness. Imaging experiments were performed in six healthy volunteers using three simultaneous slices, which generated three independent models per volunteer. For each model the motion traces of the liver tip and both kidneys were estimated. We found that the location of the 2D slices influenced the model's error in five volunteers significantly with a p -value  <0.05, and that selecting the best model at every time-point can improve the method. This allows for more accurate and robust motion characterization in MR-guided radiotherapy

Assessment of 3D motion modeling performance for dose accumulation mapping on the MR-linac by simultaneous multislice MRI

Hybrid MR-linac systems enable intrafraction motion monitoring during radiation therapy. Since time-resolved 3D MRI is still challenging, various motion models have been developed that rely on time-resolved 2D imaging. Continuous validation of these models is important for accurate dose accumulation mapping. In this study we used 2D simultaneous multislice (SMS) imaging to improve the PCA-based motion modeling method developed previously (Stemkens et al 2016 Phys. Med. Biol. 61 5335-55). From the additional simultaneously acquired slices, several independent motion models could be generated, which allowed for an assessment of the sensitivity of the motion model to the location of the time-resolved 2D slices. Additionally, the best model could be chosen at every time-point, increasing the method's robustness. Imaging experiments were performed in six healthy volunteers using three simultaneous slices, which generated three independent models per volunteer. For each model the motion traces of the liver tip and both kidneys were estimated. We found that the location of the 2D slices influenced the model's error in five volunteers significantly with a p -value  <0.05, and that selecting the best model at every time-point can improve the method. This allows for more accurate and robust motion characterization in MR-guided radiotherapy