Multicomponent MR fingerprinting reconstruction using joint-sparsity and low-rank constraints

Purpose: To develop an efficient algorithm for multicomponent MR fingerprinting (MC-MRF) reconstructions directly from highly undersampled data without making prior assumptions about tissue relaxation times and expected number of tissues. Methods: The proposed method reconstructs MC-MRF maps from highly undersampled data by iteratively applying a joint-sparsity constraint to the estimated tissue components. Intermediate component maps are obtained by a low-rank multicomponent alternating direction method of multipliers (MC-ADMM) including the non-negativity of tissue weights as an extra regularization term. Over iterations, the used dictionary compression is adjusted. The proposed method (k-SPIJN) is compared with a two-step approach in which image reconstruction and multicomponent estimations are performed sequentially and tested in numerical simulations and in vivo by applying different undersampling factors in eight healthy volunteers. In the latter case, fully sampled data serves as the reference. Results: The proposed method shows improved precision and accuracy in simulations compared with a state-of-art sequential approach. Obtained in vivo magnetization fraction maps for different tissue types show reduced systematic errors and reduced noise-like effects. Root mean square errors in estimated magnetization fraction maps significantly reduce from 13.0% (Formula presented.) 5.8% with the conventional, two-step approach to 9.6% (Formula presented.) 3.9% and 9.6% (Formula presented.) 3.2% with the proposed MC-ADMM and k-SPIJN methods, respectively. Mean standard deviation in homogeneous white matter regions reduced significantly from 8.6% to 2.9% (two step vs. k-SPIJN). Conclusion: The proposed MC-ADMM and k-SPIJN reconstruction methods estimate MC-MRF maps from highly undersampled data resulting in improved image quality compared with the existing method.ImPhys/Computational ImagingImPhys/Medical Imagin

A hybrid optimization strategy for registering images with large local deformations and intensity variations

Purpose: To develop a method for intra-patient registration of pre- and post-contrast abdominal MR images with large local deformations and large intensity variations. Method: A hybrid method is proposed to deal with this problem. It consists of two coupled techniques: (1) descriptor matching (DM) at the original resolution using a discrete optimization strategy to avoid getting trapped in a local minimum; (2) continuous optimization to refine the registration outcome based on autocorrelation of local image structure (ALOST). Our method—called DM-ALOST—has become insensitive to the local uptake of contrast agent by exploiting the mean phase and the phase congruency extracted from the multi-scale monogenic signal. The method was extensively tested on abdominal MR data of 30 patients with Crohn’s disease. Results: DM-ALOST produced significantly larger mean Dice coefficients than two state-of-the-art methods (Formula presented.). Conclusion: Both qualitative and quantitative tests demonstrated improved registration using the proposed method compared to the state-of-the-art. The DM-ALOST method facilitates measurement of corresponding features from different abdominal MR images, which can aid to assess certain diseases, particularly Crohn’s disease.ImPhys/Quantitative Imagin

Image registration based on the structure tensor of the local phase

Image registration of medical images in the presence of large intra-image signal fluctuations is a challenging task. Our paper addresses this problem by introducing a new concept based on the structure tensor of the local phase. The local phase is calculated from the monogenic signal representation of the images. The local phase image is hardly affected by unwanted signal fluctuations due to a space-variant background and a space-variant contrast. The boundary structure tensor combines the responses of edges and corners/junctions in one tensor, which has several advantages, compared to other structure tensors. We reorient the structure tensor during the registration by means of the finite-strain technique. The structure tensor is only calculated once during a preprocessing step. The results demonstrate that the proposed method effectively deals with large signal fluctuations. It performs significantly better than competing techniques.ImPhys/Imaging PhysicsApplied Science

Real-time, adaptive measurement of corneal shapes

Conventional tools for measuring the shape of the cornea perform poorly when applied to abnormal eyes. The image processing regularly fails, and the shape reconstruction often produces inaccurate results. This article describes a single measurement instrument that could integrate real-time solutions to both problem

Efficient seeding and defragmentation of curvature streamlines for colonic polyp detection

Many computer aided diagnosis (CAD) schemes have been developed for colon cancer detection using Virtual Colonoscopy (VC). In earlier work, we developed an automatic polyp detection method integrating flow visualization techniques, that forms part of the CAD functionality of an existing Virtual Colonoscopy pipeline. Curvature streamlines were used to characterize polyp surface shape. Features derived from curvature streamlines correlated highly with true polyp detections. During testing with a large number of patient data sets, we found that the correlation between streamline features and true polyps could be affected by noise and our streamline generation technique. The seeding and spacing constraints and CT noise could lead to streamline fragmentation, which reduced the discriminating power of our streamline features. In this paper, we present two major improvements of our curvature streamline generation. First, we adapted our streamline seeding strategy to the local surface properties and made the streamline generation faster. It generates a significantly smaller number of seeds but still results in a comparable and suitable streamline distribution. Second, based on our observation that longer streamlines are better surface shape descriptors, we improved our streamline tracing algorithm to produce longer streamlines. Our improved techniques are more efficient and also guide the streamline geometry to correspond better to colonic surface shape. These two adaptations support a robust and high correlation between our streamline features and true positive detections and lead to better polyp detection results.Electrical Engineering, Mathematics and Computer Scienc

Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity, robustness is not ensured when reconstructing data unseen during training. We aim to embed data consistency (DC) in deep networks while balancing the degree of network complexity. While doing so, we will assess whether either explicit or implicit enforcement of DC in varying network architectures is preferred to optimize performance. Approach. We propose a scheme called Cascades of Independently Recurrent Inference Machines (CIRIM) to assess DC through unrolled optimization. Herein we assess DC both implicitly by gradient descent and explicitly by a designed term. Extensive comparison of the CIRIM to compressed sensing as well as other Machine Learning methods is performed: the End-to-End Variational Network (E2EVN), CascadeNet, KIKINet, LPDNet, RIM, IRIM, and UNet. Models were trained and evaluated on T1-weighted and FLAIR contrast brain data, and T2-weighted knee data. Both 1D and 2D undersampling patterns were evaluated. Robustness was tested by reconstructing 7.5× prospectively undersampled 3D FLAIR MRI data of multiple sclerosis (MS) patients with white matter lesions. Main results. The CIRIM performed best when implicitly enforcing DC, while the E2EVN required an explicit DC formulation. Through its cascades, the CIRIM was able to score higher on structural similarity and PSNR compared to other methods, in particular under heterogeneous imaging conditions. In reconstructing MS patient data, prospectively acquired with a sampling pattern unseen during model training, the CIRIM maintained lesion contrast while efficiently denoising the images. Significance. The CIRIM showed highly promising generalization capabilities maintaining a very fair trade-off between reconstructed image quality and fast reconstruction times, which is crucial in the clinical workflow.ImPhys/Computational ImagingImPhys/Medical Imagin

Classifying CT Image Data Into Material Fractions by a Scale and Rotation Invariant Edge Model

A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges—arch model—is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named L and H) and thereby classify the boundary. Second, the model is used to approximate the underlying noisefree material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the material boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise complicated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy.Imaging Science and TechnologyApplied Science

Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow

Today’s computer aided detection systems for computed tomography colonography (CTC) enable automated detection and segmentation of colorectal polyps.We present a paradigm shift by proposing a method that measures the amount of protrudedness of a candidate object in a scale adaptive fashion. One of the main results is that the performance of the candidate detection depends only on one parameter, the amount of protrusion. Additionally the method yields correct polyp segmentation without the need of an additional segmentation step. The supervised pattern recognition involves a clear distinction between size related features and features related to shape or intensity. A Mahalanobis transformation of the latter facilitates ranking of the objects using a logistic classifier. We evaluate two implementations of the method on 84 patients with a total of 57 polyps larger than or equal to 6 mm.We obtained a performance of 95% sensitivity at four false positives per scan for polyps larger than or equal to 6 mm.Imaging Science and TechnologyApplied Science

Simulation of scanner- and patient-specific low-dose CT imaging from existing CT images

Purpose: Simulating low-­dose Computed Tomography (CT) facilitates in-­silico studies into the required dose for a diagnostic task. Conventionally, low-­‐dose CT images are created by adding noise to the projection data. However, in practice the raw data is often simply not available. This paper presents a new method for simulating patient-­‐specific, low-dose CT images without the needof the original projection data. Methods: The low-­dose CT simulation method included the following: (1) computation of a virtual sinogram from a high dose CT image through aradon transform; (2) simulation of a 'reduced'­‐dose sinogram with appropriateamounts of noise; (3) subtraction of the high-­‐dose virtual sinogram from thereduced-­‐dose sinogram; (4) reconstruction of a noise volume via filtered back-projection; (5) addition of the noise image to the original high-dose image. Therequired scanner-­Specific parameters, such as the apodization window, bowtiefilter, the X-ray tube output parameter (reflecting the photon flux) and the detector read-­out noise, were retrieved from calibration images of a watercylinder. The low-­‐dose simulation method was evaluated by comparing thenoise characteristics in simulated images with experimentally acquireddata.Results:The models used to recover the scanner-­specific parameters fitted accurately tothe calibration data, and the values of the parameters were comparable to valuesreported in literature. Finally, the simulated low-dose images accurately reproduced the noise characteristics in experimentally acquired low-dose­‐volumes.Conclusion:The developed methods truthfully simulate low-­dose CT imaging for a specificscanner and reconstruction using filtered backprojection. The scanner-­‐specificparameters can be estimated from calibration data.Accepted Author ManuscriptImPhys/Quantitative Imagin

Reconstructing 3D proton dose distribution using ionoacoustics

In proton therapy high energy protons are used to irradiate a tumor. Ideally, the delivered proton dose distribution is measured during treatment to ensure patient safety and treatment effectiveness. Here we investigate if we can use the ionoacoustic wave field to monitor the actual proton dose distribution for the two most commonly used proton accelerators; the isochronous cyclotron and the synchrocyclotron. To this end we model the acoustic field generated by the protons when irradiating a heterogeneous cancerous breast with a 89 MeV proton beam. To differentiate between the systems, idealized temporal micro-structures of the beams have been implemented. Results show that by employing model-based inversion we are able to reconstruct the 3D dose distributions from the simulated noisy pressure fields. Good results are obtained for both systems; the absolute error in the position of the maximum amplitude of the dose distribution is 5.0 mm for the isochronous cyclotron and 5.2 mm for the synchrocyclotron. In conclusion, this numerical study suggests that the ionoacoustic wave field may be used to monitor the proton dose distribution during breast cancer treatment.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Acoustical Wavefield ImagingRST/Medical Physics & TechnologyImPhys/Quantitative Imagin