MR signature matching (MRSIGMA) implementation for true real-time free-breathing volumetric imaging with sub-200 ms latency on an MR-Linac.

PURPOSE: Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac. METHODS: MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground-truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real-time implementation of MRSIGMA. Dice coefficients between real-time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance. RESULTS: Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3-1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum-stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk. CONCLUSION: The real-time implementation of MRSIGMA enabled true real-time free-breathing 3D MRI with sub-200 ms imaging latency on a clinical MR-Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion.

Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI.

PURPOSE: To develop a novel deep learning approach for 4D-MRI reconstruction, named Movienet, which exploits space-time-coil correlations and motion preservation instead of k-space data consistency, to accelerate the acquisition of golden-angle radial data and enable subsecond reconstruction times in dynamic MRI. METHODS: Movienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD-GRASP image used for training. Movienet is demonstrated for motion-resolved 4D MRI and motion-resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR-Linac (1.5-fold acquisition acceleration) and diagnostic 3T MRI scanners (2-fold and 2.25-fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers. RESULTS: The reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD-GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD-GRASP with similar overall image quality and improved suppression of streaking artifacts. CONCLUSION: Movienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion-resistant 3D anatomical imaging or motion-resolved 4D imaging.

Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours.

BACKGROUND: Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent. METHODS: This study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra's algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects. RESULTS: The proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm. CONCLUSION: The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function. Video Abstract.

Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system.

Objective. To develop real-time 4D MRI using MR signature matching (MRSIGMA) for volumetric motion imaging in patients with pancreatic cancer on a 1.5T MR-Linac system.Approach. Two consecutive MRI scans with 3D golden-angle radial stack-of-stars acquisitions were performed on ten patients with inoperable pancreatic cancer. The complete first scan (905 angles) was used to compute a 4D motion dictionary including ten pairs of 3D motion images and signatures. The second scan was used for real-time imaging, where each angle (275 ms) was processed separately to match it to one of the dictionary entries. The complete second scan was also used to compute a 4D reference to assess motion tracking performance.Dicecoefficients of the gross tumor volume (GTV) and two organs-at-risk (duodenum-stomach and small bowel) were calculated between signature matching and reference. In addition, volume changes, displacements, center of mass shifts, andDicescores over time were calculated to characterize motion.Main results. Total imaging latency of MRSIGMA (acquisition + matching) was less than 300 ms. TheDicecoefficients were 0.87 ± 0.06 (GTV), 0.86 ± 0.05 (duodenum-stomach), and 0.85 ± 0.05 (small bowel), which indicate high accuracy (high mean value) and low uncertainty (low standard deviation) of MRSIGMA for real-time motion tracking. The center of mass shift was 3.1 ± 2.0 mm (GTV), 5.3 ± 3.0 mm (duodenum-stomach), and 3.4 ± 1.5 mm (small bowel). TheDicescores over time (0.97 ± [0.01-0.03]) were similarly high for MRSIGMA and reference scans in all the three contours.Significance. This work demonstrates the feasibility of real-time 4D MRI using MRSIGMA for volumetric motion tracking on a 1.5T MR-Linac system. The high accuracy and low uncertainty of real-time MRSIGMA is an essential step towards continuous treatment adaptation of tumors affected by real-time respiratory motion and could ultimately improve treatment safety by optimizing ablative dose delivery near gastrointestinal organs.

Finite element technique for the assessment of 3-1 and "super 1-3" connectivity piezoelectric composite transducers.

This paper describes the use of finite element (FE) technique in the assessment of new types of multilayered piezoelectric composite structure using the PZFlex code. The background information leading to model configuration, including materials properties and boundary conditions, is discussed. This is coupled with an experimental program of model corroboration via a number of key stage prototypes to achieve a model-build-test methodology. Initially the 3-1 connectivity plate and multilayered piezoelectric composites are analyzed. Results from FE and experimental assessment indicate that the 3-1 plate devices offer no benefit over the conventional 1-3 connectivity arrangement. A simple, minimally diced, 3-1 connectivity multilayer device is analyzed and is shown to be suitable for the manufacture of wideband and efficient transducers operating in the 10-100 kHz frequency range for high power applications.

Comparison of the frequency and physical nature of the lowest order parasitic mode in single crystal and ceramic 2-2 and 1-3 piezoelectric composite transducers.

This work describes an investigation into the first order parasitic mode (i.e., that closest to the fundamental thickness mode) that can occur in 2-2 and 1-3 thickness drive piezoelectric composite transducers. Specifically, the authors compare the performance of piezoceramic and piezocrystal composites with a common passive phase. A local Lamb wave approach is used to describe the generation of such modes, and the validity of this theory is investigated over the entire volume fraction range. It is shown that, when the parasitic mode is primarily generated by Lamb wave activity in the passive phase, both active materials demonstrate similar behavior. However, at higher volume fractions, the first order mode is related to the lateral resonance of the active material, and quite different behavior may be observed between the two sets of devices. The phase velocity of the parasitic modes in each device configuration was investigated by a combination of experimental measurement on a number of transducers along with simulations using the finite-element code PZFlex. Both 2-2 and 1-3 composites made from the single crystal materials pzn-4.5%pt, pzn-8%pt, and pmn-30%pt were investigated along with composites made from pzt5h ceramic. The PZFlex results are compared with experimental impedance analysis and laser scanning of surface displacement, with good agreement demonstrated. By comparing two very different active materials, additional insight into parasitic resonant activity within composite devices is demonstrated.

Multiscale AM-FM methods on EEG signals for motor task classification.

In this manuscript, we present the use of customized, multiscale amplitude-modulation frequency-modulation (AMFM) methods on electroencephalography (EEG) brain signals during the subject development a motor task: right hand and left hand. This approach is compared to various non-linear patterns and methods that have been applied in order to characterize and understand the dynamic behavior of the EEG signals. The AM-FM methods have been optimized in terms of multiscale filters for the mu band (8-12 Hz). The instantaneous AM-FM values are processed using their probability density function and classified using multiple layer perceptron (MLP) and the partial least squares regression (PLS). The system is tested using the standard BCI dataset with results with a precision to 89% and an area under the ROC to 91%.

A multiscale decomposition approach to detect abnormal vasculature in the optic disc.

This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%.

A multiscale optimization approach to detect exudates in the macula.

Pathologies that occur on or near the fovea, such as clinically significant macular edema (CSME), represent high risk for vision loss. The presence of exudates, lipid residues of serous leakage from damaged capillaries, has been associated with CSME, in particular if they are located one optic disc-diameter away from the fovea. In this paper, we present an automatic system to detect exudates in the macula. Our approach uses optimal thresholding of instantaneous amplitude (IA) components that are extracted from multiple frequency scales to generate candidate exudate regions. For each candidate region, we extract color, shape, and texture features that are used for classification. Classification is performed using partial least squares (PLS). We tested the performance of the system on two different databases of 652 and 400 images. The system achieved an area under the receiver operator characteristic curve (AUC) of 0.96 for the combination of both databases and an AUC of 0.97 for each of them when they were evaluated independently.

Detection of neovascularization in the optic disc using an AM-FM representation, granulometry, and vessel segmentation.

Neovascularization, defined as abnormal formation of blood vessels in the retina, is a sight-threatening condition indicative of late-stage diabetic retinopathy (DR). Ischemia due to leakage of blood vessels causes the body to produce new and weak vessels that can lead to complications such as vitreous hemorrhages. Neovascularization on the disc (NVD) is diagnosed when new vessels are located within one disc-diameter of the optic disc. Accurately detecting NVD is important in preventing vision loss due to DR. This paper presents a method for detecting NVD in digital fundus images. First, a region of interest (ROI) containing the optic disc is manually selected from the image. By adaptively combining contrast enhancement methods with a vessel segmentation technique, the ROI is reduced to the regions indicated by the segmented vessels. Textural features extracted by using amplitude-modulation frequency-modulation (AM-FM) techniques and granulometry are used to differentiate NVD from a normal optic disc. Partial least squares is used to perform the final classification. Leave-one-out cross-validation was used to evaluate the performance of the system with 27 NVD and 30 normal cases. We obtained an area under the receiver operator characteristic curve (AUC) of 0.85 by using all features, increasing to 0.94 with feature selection.

Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images.

PURPOSE: To describe and evaluate the performance of an algorithm that automatically classifies images with pathologic features commonly found in diabetic retinopathy (DR) and age-related macular degeneration (AMD). METHODS: Retinal digital photographs (N = 2247) of three fields of view (FOV) were obtained of the eyes of 822 patients at two centers: The Retina Institute of South Texas (RIST, San Antonio, TX) and The University of Texas Health Science Center San Antonio (UTHSCSA). Ground truth was provided for the presence of pathologic conditions, including microaneurysms, hemorrhages, exudates, neovascularization in the optic disc and elsewhere, drusen, abnormal pigmentation, and geographic atrophy. The algorithm was used to report on the presence or absence of disease. A detection threshold was applied to obtain different values of sensitivity and specificity with respect to ground truth and to construct a receiver operating characteristic (ROC) curve. RESULTS: The system achieved an average area under the ROC curve (AUC) of 0.89 for detection of DR and of 0.92 for detection of sight-threatening DR (STDR). With a fixed specificity of 0.50, the system's sensitivity ranged from 0.92 for all DR cases to 1.00 for clinically significant macular edema (CSME). CONCLUSIONS: A computer-aided algorithm was trained to detect different types of pathologic retinal conditions. The cases of hard exudates within 1 disc diameter (DD) of the fovea (surrogate for CSME) were detected with very high accuracy (sensitivity = 1, specificity = 0.50), whereas mild nonproliferative DR was the most challenging condition (sensitivity = 0.92, specificity = 0.50). The algorithm was also tested on images with signs of AMD, achieving a performance of AUC of 0.84 (sensitivity = 0.94, specificity = 0.50).

Multiscale AM-FM demodulation and image reconstruction methods with improved accuracy.

We develop new multiscale amplitude-modulation frequency-modulation (AM-FM) demodulation methods for image processing. The approach is based on three basic ideas: (i) AM-FM demodulation using a new multiscale filterbank, (ii) new, accurate methods for instantaneous frequency (IF) estimation, and (iii) multiscale least squares AM-FM reconstructions. In particular, we introduce a variable-spacing local linear phase (VS-LLP) method for improved instantaneous frequency (IF) estimation and compare it to an extended quasilocal method and the quasi-eigen function approximation (QEA). It turns out that the new VS-LLP method is a generalization of the QEA method where we choose the best integer spacing between the samples to adapt as a function of frequency. We also introduce a new quasi-local method (QLM) for IF and IA estimation and discuss some of its advantages and limitations. The new IF estimation methods lead to significantly improved estimates. We present different multiscale decompositions to show that the proposed methods can be used to reconstruct and analyze general images.

Multiscale AM-FM methods for diabetic retinopathy lesion detection.

In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM) methods for discriminating between normal and pathological retinal images. The method presented in this paper is tested using standard images from the early treatment diabetic retinopathy study. We use 120 regions of 40 x 40 pixels containing four types of lesions commonly associated with diabetic retinopathy (DR) and two types of normal retinal regions that were manually selected by a trained analyst. The region types included microaneurysms, exudates, neovascularization on the retina, hemorrhages, normal retinal background, and normal vessels patterns. The cumulative distribution functions of the instantaneous amplitude, the instantaneous frequency magnitude, and the relative instantaneous frequency angle from multiple scales are used as texture feature vectors. We use distance metrics between the extracted feature vectors to measure interstructure similarity. Our results demonstrate a statistical differentiation of normal retinal structures and pathological lesions based on AM-FM features. We further demonstrate our AM-FM methodology by applying it to classification of retinal images from the MESSIDOR database. Overall, the proposed methodology shows significant capability for use in automatic DR screening.