Physics-informed neural networks for myocardial perfusion MRI quantification

Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters. These neural networks can be trained to fit the observed perfusion MR data while respecting the underlying physical conservation laws described by a multi-compartment exchange model. Here, we provide a framework for the implementation of PINNs in myocardial perfusion MR. The approach is validated both in silico and in vivo. In the in silico study, an overall decrease in mean-squared error with the ground-truth parameters was observed compared to a standard non-linear least squares fitting approach. The in vivo study demonstrates that the method produces parameter values comparable to those previously found in literature, as well as providing parameter maps which match the clinical diagnosis of patients.</p

High-resolution quantification of stress perfusion defects by cardiac magnetic resonance

AIMS: Quantitative stress perfusion cardiac magnetic resonance (CMR) is becoming more widely available, but it is still unclear how to integrate this information into clinical decision-making. Typically, pixel-wise perfusion maps are generated, but diagnostic and prognostic studies have summarized perfusion as just one value per patient or in 16 myocardial segments. In this study, the reporting of quantitative perfusion maps is extended from the standard 16 segments to a high-resolution bullseye. Cut-off thresholds are established for the high-resolution bullseye, and the identified perfusion defects are compared with visual assessment.METHODS AND RESULTS: Thirty-four patients with known or suspected coronary artery disease were retrospectively analysed. Visual perfusion defects were contoured on the CMR images and pixel-wise quantitative perfusion maps were generated. Cut-off values were established on the high-resolution bullseye consisting of 1800 points and compared with the per-segment, per-coronary, and per-patient resolution thresholds. Quantitative stress perfusion was significantly lower in visually abnormal pixels, 1.11 (0.75-1.57) vs. 2.35 (1.82-2.9) mL/min/g (Mann-Whitney U test P &lt; 0.001), with an optimal cut-off of 1.72 mL/min/g. This was lower than the segment-wise optimal threshold of 1.92 mL/min/g. The Bland-Altman analysis showed that visual assessment underestimated large perfusion defects compared with the quantification with good agreement for smaller defect burdens. A Dice overlap of 0.68 (0.57-0.78) was found. CONCLUSION: This study introduces a high-resolution bullseye consisting of 1800 points, rather than 16, per patient for reporting quantitative stress perfusion, which may improve sensitivity. Using this representation, the threshold required to identify areas of reduced perfusion is lower than for segmental analysis.</p

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

Method of visualizing the perfusion of an organ while utilizing a perfusion measurement

\u3cp\u3eThe invention relates to a method of visualizing the perfusion of an organ, notably the perfusion of the myocardium of the heart. A series of MR perfusion images is displayed on a visual display unit. Each pair of successive images from a series of images is transformed in such a manner that the organ that is shown on the display unit essentially maintains its position.\u3c/p\u3

Visualization of stress level cardiac functional

\u3cp\u3eThe invention relates to a system (100) for visualizing a cardiac parameter at a plurality of positions in a myocardium and at a plurality of stress levels, the system comprising a determination unit (110) for determining a value of the cardiac parameter at a position from the plurality of positions in the myocardium and at a stress level from the plurality of stress levels on the basis of stress level cardiac functional data, and a visualization unit (120) for visualizing the determined value of the cardiac parameter by displaying a point in a viewing plane. The visualized points are defined by their polar coordinates in a polar coordinate system in the viewing plane. A radial coordinate of the point visualizes the determined value of the cardiac parameter. An angular coordinate of the point visualizes an angular coordinate of the position in the myocardium in a cylindrical coordinate system. Thus, the system allows easy numerical comparison of local myocardial contractions at different stress level values.\u3c/p\u3

Apparatus, software and method for processing images from a patient's heart

\u3cp\u3eThe invention relates to an apparatus for processing images from a patient's heart comprising collecting means for collecting the images and processing means for processing said images in order to identify a necrotic area in the heart's myocardium, whereby the collecting means are arranged to collect functional images of the heart and late­ enhancement images of the heart, that registration means are provided to register the functional images in relation to the late-enhancement images and mapping means to map myocardial contours in the functional images in register with and onto the late-enhancement images.\u3c/p\u3

Method of correcting inhomogeneities/discontinuities in mr perfusion images

\u3cp\u3eThe invention relates to a method of correcting inhomogeneities in MR perfusion images of the myocardium of a patient, which perfusion images relate time-sequentially to a preliminary phase which precedes the administration of a contrast medium and to an examination phase which succeeds the administration of the contrast medium, the perfusion images from the examination phase being corrected, in a correction step, for a detected intensity variation of the perfusion images from the preliminary phase, the perfusion images from the preliminary phase being transformed, prior to the correction step, in such a manner that pixels or groups of pixels thereof register with corresponding pixels or groups of pixels of the perfusion images from the examination phase.\u3c/p\u3

Reliability measure concerning the registration of cardiac MR perfusion measurements

\u3cp\u3eA method is developed for automatic analysis of the reliability of an automatic registration of perfusion cardiovascular MR images. A parameter, for example, a similarity measure between the successive images, is calculated first in order to quantify the success of the registration process between these images for the data set. A criterion is then introduced, for example a threshold is imposed on the similarity measure. The successively registered images that have a calculated similarity measure that exceeds the defined threshold are automatically accepted for further analysis.\u3c/p\u3

Transform coding of images using directionally adaptive vector quantization

\u3cp\u3eAn image coding technique is described that compresses monochrome digital TV images from 8 to about 1 bit/pixel while maintaining high quality. First, for subblocks of 8 multiplied by 8 pixels the discrete cosine transform is calculated. Then, the resulting block of 8 multiplied by 8 transform coefficients is divided into a number of subvectors, each of which is normalized and quantized using vector quantization. The subvector construction and the vector quantization are performed adaptively to the 'direction' of spatial activity in the pixel subblock. The quantization also adapts to the energy of the subvector.\u3c/p\u3