Multislice Approach Research Articles

Feasibility of interleaved multislice averaged magnetization inversion-recovery acquisitions of the spinal cord.

To establish an interleaved multislice variant of the averaged magnetization inversion-recovery acquisitions (AMIRA) approach for 2D spinal cord imaging with increased acquisition efficiency compared with the conventional 2D single-slice approach(es), and to determine essential prerequisites for a working interleaved multislice AMIRA approach in practice. The general AMIRA concept is based on an inversion recovery-prepared, segmented, and time-limited cine balanced SSFP sequence, generating images of different contrast. For AMIRA imaging of multiple, independent slices in a 2D interleaved fashion, a slice loop within the acquisition loops was programmed. The former non-selective inversions were replaced with slice-selective inversions with user-definable slice thickness. The thickness of the slice-selective inversion in 2D interleaved multislice AMIRA should be doubled compared with the manufacturer's standard setting to avoid an increased sensitivity to flow and pulsation effects particularly in the CSF. However, this solution also limits its practical applicability, as slices located at directly adjacent vertebrae cannot be imaged together. Successful interleaved two-slice AMIRA imaging for a "reference" in vivo protocol with 0.50 × 0.50 mm2 in-plane resolution and 8-mm slice thickness is demonstrated, therefore halving its acquisition time per slice from 3 min down to 1.5 min. The investigated 2D interleaved two-slice AMIRA variant facilitates spinal cord imaging that maintains similar contrast and the same resolution as the conventional 2D single-slice AMIRA approach, but does so with a halved acquisition time.

Fast cardiac T1ρ,adiab mapping using slice-selective adiabatic spin-lock preparation pulses.

Myocardial T1ρ mapping techniques commonly acquire multiple images in one breathhold to calculate a single-slice T1ρ map. Recently, non-selective adiabatic pulses have been used for robust spin-lock preparation (T1ρ,adiab). The objective of this study was to develop a fast multi-slice myocardial T1ρ,adiab mapping approach. The proposed-sequence reduces the number of breathholds required for whole-heart 2D T1ρ,adiab mapping by acquiring multiple interleaved slices in each breathhold using slice-selective T1ρ,adiab preparation pulses. The proposed-sequence was implemented with two interleaved slices per breathhold scan and was quantitatively evaluated in phantom experiments and 10 healthy-volunteers against a single-slice T1ρ,adiab mapping sequence. The sequence was demonstrated in two patients with myocardial scar. The phantom experiments showed the proposed-sequence had slice-to-slice variation of 1.62% ± 1.05% and precision of 4.51 ± 0.68 ms. The healthy volunteer cohort subject-wise mean relaxation time was lower for the proposed-sequence than the single-slice sequence (137.7 ± 5.3 ms vs. 148.4 ± 8.3 ms, p < 0.001), and spatial-standard-deviation was better (18.7 ± 1.8 ms vs. 21.8 ± 3.4 ms, p < 0.018). The mean within-subject, coefficient of variation was 5.93% ± 1.57% for the proposed-sequence and 6.31% ± 1.92% for the single-slice sequence (p = 0.35) and the effect of slice variation (0.81 ± 4.87 ms) was not significantly different to zero (p = 0.61). In both patient examples increased T1ρ,adiab (maximum American Heart Association-segment mean = 174 and 197 ms) was measured within the myocardial scar. The proposed sequence provides a twofold acceleration for myocardial T1ρ,adiab mapping using a multi-slice approach. It has no significant difference in within-subject variability, and significantly better precision, compared to a 2D T1ρ,adiab mapping sequence based on non-selective adiabatic spin-lock preparations.

Ultrafast laser direct writing of material independent integrated nonlinear components based on NPE

Material independent integrated nonlinear components (MIINC) based on nonlinear polarization evolution (NPE) play an important role in the emerging quantum computing, integrated photonics and ultrafast science. However, to our knowledge, no one has yet employed ultrafast laser direct writing for NPE-based MIINC. Here, for the first time, we finish the MIINC direct writing based on NPE using an ultrafast laser, and we suggest a multi-slice (MS) approach to in-fiber waveplate direct writing that allows for flexible adjustment of the phase difference, the waveplate fast and slow axis, and the stress birefringence value. To complete the measurement of the nonlinear transmittance curve, a polarization-splitter prism is attached to the output light path's back end. The measurement results agree with the theoretical calculation. MIINC based on NPE with arbitrary nonlinear transmittance curves are fabricated via the direct writing method, which is expected to be used in optical chips in the future.

Modelling dynamical 3D electron diffraction intensities. I. A scattering cluster algorithm.

Three-dimensional electron diffraction (3D-ED) is a powerful technique for crystallographic characterization of nanometre-sized crystals that are too small for X-ray diffraction. For accurate crystal structure refinement, however, it is important that the Bragg diffracted intensities are treated dynamically. Bloch wave simulations are often used in 3D-ED, but can be computationally expensive for large unit cell crystals due to the large number of diffracted beams. Proposed here is an alternative method, the `scattering cluster algorithm' (SCA), that replaces the eigen-decomposition operation in Bloch waves with a simpler matrix multiplication. The underlying principle of SCA is that the intensity of a given Bragg reflection is largely determined by intensity transfer (i.e. `scattering') from a cluster of neighbouring diffracted beams. However, the penalty for using matrix multiplication is that the sample must be divided into a series of thin slices and the diffracted beams calculated iteratively, similar to the multislice approach. Therefore, SCA is more suitable for thin specimens. The accuracy and speed of SCA are demonstrated on tri-isopropyl silane (TIPS) pentacene and rubrene, two exemplar organic materials with large unit cells.

Tilted multislice approach for quantitative STEM simulation

Tilted multislice approach for quantitative STEM simulation

A 3-slice cardiac quantitative native and post-contrast T1 and T2 MRI protocol requiring only four BHs using a 72-channel receive array coil

IntroductionCurrent practice to obtain left ventricular (LV) native and post-contrast T1 and T2 comprises single-slice readouts with multiple breath-holds (BHs). We propose a multi-slice parallel-imaging approach with a 72-channel receive-array to reduce BHs and demonstrate this in healthy subjects and hypertrophic cardiomyopathy (HCM) patients.MethodsA T1/T2 phantom was scanned at 3 T using a 16-channel and a novel 72-channel coil to assess the impact of different coils and acceleration factors on relaxation times. 16–18 healthy participants (8 female, age 28.4 ± 5.1 years) and 3 HCM patients (3 male, age 55.3 ± 4.2 years) underwent cardiac-MRI with the 72-channel coil, using a Modified Look-Locker scan with a shared inversion pulse across 3 slices and a Gradient-Spin-Echo scan. Acceleration was done by sensitivity encoding (SENSE) with accelerations 2, 4, and 6. LV T1 and T2 values were analyzed globally, per slice, and in 16 segments, with SENSE = 2 as the reference.ResultsThe phantom scans revealed no bias between coils and acceleration factors for T1 or T2, except for T2 with SENSE = 2, which resulted in a bias of 8.0 ± 6.7 ms (p &amp;lt; 0.001) between coils. SENSE = 4 and 6 enabled T1 mapping of three slices in a single BH, and T2 mapping of three slices within two BHs. In healthy subjects, T1 and T2 values varied. We found an average overestimation of T1 in 3 slices of 25 ± 87 ms for SENSE = 4 and 30 ± 103 ms using SENSE = 6, as compared to SENSE = 2. Acceleration resulted in decreased signal-to-noise; however, visually insignificant and without increased incidence of SENSE-artifacts. T2 was overestimated by 2.1 ± 5.0 ms for SENSE = 4 and 6.4 ± 9.7 ms using SENSE = 6, as compared to SENSE = 2. Native and post-contrast T1 measurements with SENSE = 4 and ECV quantification in HCM patients was successful.ConclusionThe 72-channel receiver-array coil with SENSE = 4 and 6, enabled LV-tissue characterization in three slices. Pre- and post-contrast T1 maps were obtained in a single BH, while T2 required two BHs.

Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool.

To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard. This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool. Overall technical success rates and agreement with the manual reference standard were assessed. The overall success rate of the DL tool in this heterogeneous patient cohort was significantly higher than that of the older image processing BMD algorithm (99.3% vs 89.4%, P < .001). Using this DL tool, the closest median Hounsfield unit values for single-, three-, and seven-slice vertebral ROIs were within 5% of the manual reference standard Hounsfield unit values in 35.1%, 56.9%, and 85.8% of scans; within 10% in 56.6%, 75.6%, and 92.9% of scans; and within 25% in 76.5%, 89.3%, and 97.1% of scans, respectively. Trade-offs in sensitivity and specificity for osteoporosis assessment were observed from the single-slice approach (sensitivity, 39.4%; specificity, 98.3%) to the minimum value of the multislice approach (for seven contiguous slices; sensitivity, 71.3% and specificity, 94.6%). The new DL BMD tool demonstrated a higher success rate than the older feature-based image processing tool, and its outputs can be targeted for higher specificity or sensitivity for osteoporosis assessment.Keywords: CT, CT-Quantitative, Abdomen/GI, Skeletal-Axial, Spine, Deep Learning, Machine Learning Supplemental material is available for this article. © RSNA, 2022.

A Multislice Approach to Quantify Laser-Induced Lattice Temperature from Ultrafast Electron Diffraction Measurements of Single-Crystal Films

A Multislice Approach to Quantify Laser-Induced Lattice Temperature from Ultrafast Electron Diffraction Measurements of Single-Crystal Films

Nonlocal Effective Medium (NLEM) for Quantitative Modelling of Nanoroughness in Spectroscopic Reflectance

Spectroscopic reflectance is a versatile optical methodology for the characterization of transparent and semi-transparent thin films in terms of thickness and refractive index. The Fresnel equations are used to interpret the measurements, but their accuracy is limited when surface roughness is present. Nanoroughness can be modelled through a discretized multi-slice and effective medium approach, but to date, this offered mostly qualitative and not quantitative accuracy. Here we introduce an adaptive and nonlocal effective medium approach, which considers the relative size and environment of each discretized slice. We develop our model using finite-difference time-domain simulation results and demonstrate its ability to predict nanoroughness size and shape with relative errors &lt; 3% in a variety of test systems. The accuracy of the model is directly compared to the prediction capabilities of the Bruggeman and Maxwell–Garnett models, highlighting its superiority. Our model is fully parametrized and ready to use for exploring the effects of roughness on reflectance without the need for costly 3D simulations and to be integrated into the Fresnel simulator of spectroscopic reflectance tools.

Explainability of radiomics through formal methods

Background and Objective: Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently an open issue because results’ explainability is advantageous in understanding the reasoning behind the model, both for patients than for implementing a feedback mechanism for medical specialists using decision support systems. Methods: Addressing transparency issues related to the Artificial Intelligence field, the innovative technique of Formal methods use a mathematical logic reasoning to produce an automatic, quick and reliable diagnosis. In this paper we analyze results given by the adoption of Formal methods for the diagnosis of the Coronavirus disease: specifically, we want to analyse and understand, in a more medical way, the meaning of some radiomic features to connect them with clinical or radiological evidences. Results: In particular, the usage of Formal methods allows the authors to do statistical analysis on the feature value distributions, to do pattern recognition on disease models, to generalize the model of a disease and to reach high performances of results and interpretation of them. A further step for explainability can be accounted by the localization and selection of the most important slices in a multi-slice approach. Conclusions: In conclusion, we confirmed the clinical significance of some First order features as Skewness and Kurtosis. On the other hand, we suggest to decline the use of the Minimum feature because of its intrinsic connection with the Computational Tomography exam of the lung.

New 3-Dimensional Volumetric Ultrasound Method for Accurate Quantification of Atherosclerotic Plaque Volume

BackgroundCarotid and femoral plaque burden is a recognized biomarker of cardiovascular disease risk. A new electronic-sweep 3-dimensional (3D)–matrix transducer method can improve the functionality and image quality of vascular ultrasound atherosclerosis imaging. ObjectivesThis study aimed to validate this method for plaque volume measurement in early and intermediate–advanced plaques in the carotid and femoral territories. MethodsPlaque volumes were measured ex vivo in pig carotid and femoral artery specimens by 3-dimensional vascular ultrasound (3DVUS) using a 3D-matrix (electronic-sweep) transducer and its associated 3D plaque quantification software, and were compared with gold-standard histology. To test the clinical feasibility and accuracy of the 3D-matrix transducer, an experiment was conducted in intermediate–high risk individuals with carotid and femoral atherosclerosis. The results were compared with those obtained using the previously validated mechanical-sweep 3D transducer and established 2-dimensional (2D)–based plaque quantification software. ResultsIn the ex vivo study, the authors assessed 19 atherosclerotic plaques (plaque volume, 0.76 µL-56.30 μL), finding strong agreement between measurements with the 3D-matrix transducer and the histological gold-standard (intraclass correlation coefficient [ICC]: 0.992; [95% CI: 0.978-0.997]). In the clinical analysis of 20 patients (mean age 74.6 ± 4.45 years; 40% men), the authors found 64 (36 carotid and 28 femoral) of 80 scanned territories with atherosclerosis (measured atherosclerotic volume, 10 μL-859 μL). There was strong agreement between measurements made from electronic-sweep and mechanical-sweep 3DVUS transducers (ICC: 0.997 [95% CI: 0.995-0.998]). Agreement was also high between plaque volumes estimated by the 2D and 3D plaque quantification software applications (ICC: 0.999 [95% CI: 0.998-0.999]). Analysis time was significantly shorter with the 3D plaque quantification software than with the 2D multislice approach with a mean time reduction of 46%. Conclusions3DVUS using new matrix transducer technology, together with improved 3D plaque quantification software, simplifies the accurate volume measurement of early (small) and intermediate–advanced plaques located in carotid and femoral arteries.

RELIABILITY AND VALIDITY OF SINGLE AXIAL SLICE VS. MULTIPLE SLICE QUANTITATIVE MEASUREMENT OF KNEE EFFUSION-SYNOVITIS ON 3T MRI

Inflammation is increasingly recognized as critical in the pathogenesis of KOA, and effusion-synovitis (ES) on knee MRI has been identified as an important biomarker of OA. Semi-automated software methods offer objective measurements of ES volume on MRI, but efficiency is crucial, especially for studies involving large numbers of scans. We have developed a single-slice quantitative measurement of ES using the single axial slice with the largest area of ES, and compared this to previously validated multi-slice measurements. We evaluated the reliability of a new single-slice quantitative measurement of ES in knees with radiographic OA, and correlated this measurement of ES with a validated multi-slice quantitative measurement of ES, and with a validated semi-quantitative scoring of ES in order to demonstrate that the more efficient single-slice ES method is a valid substitute for the multi-slice approach. OAI participants with radiographic OA (i.e., Kellgren-Lawrence [KL] 2,3) in at least one knee at baseline with prior semi-quantitative assessment of ES by a MSK radiologist were randomly selected (n=50 knees; 1 knee per participant). Four non-expert readers were trained to use custom-designed semi-automated software to measure ES volume on 3T MRI, under the direction of a MSK radiologist, and then read scans from the KL 2,3 sample blinded to prior assessments (twice for 3 readers and once for 1 reader). Intra- and inter-reader reliability was evaluated based on intraclass correlation coefficients (ICC) from a linear mixed model with random effects for knee, reader, and interaction between knee and reader. We assessed agreement with the mean absolute difference (MAD). In a subset of the FNIH study (n=301), one reader measured single-slice ES volume, blinded to prior multi-slice quantitative measurement of ES. We estimated the Spearman correlation coefficient between single-slice and multi-slice volume of ES in the FNIH subset. Validity was evaluated based on Spearman correlation between single-slice ES volume and semi-quantitative ES MRI Osteoarthritis Knee Score (MOAKS) in the KL 2,3 sample and the FNIH subset. 95% confidence intervals (CI) were generated from bias-corrected and acceleration-adjusted bootstrap. In the sample of KL 2,3 knees, the mean single-slice ES volume was 312.8 mm 3 with standard deviation 218.4 mm 3 . The intra-reader ICC was 0.96 (95%CI: 0.93, 0.97), and inter-reader ICC was 0.90 (95%CI: 0.87, 0.95); the intra-reader MAD was 36 mm 3 (95%CI: 28, 44), and inter-reader MAD was 61 mm 3 (95%CI: 48, 75). The estimated correlation between single-slice and multi-slice ES volume was 0.75 (95%CI: 0.68, 0.81; Figure 1). Correlation between single-slice ES volume and MOAKS ES was 0.62 (95%CI: 0.39, 0.79) in the KL 2,3 sample and 0.67 (95%CI: 0.59, 0.73) in the FNIH subset (Figure 2A), while correlation between multi-slice ES volume and MOAKS ES was 0.74 (95%CI: 0.68, 0.79) in the FNIH subset (Figure 2B). Single-slice quantitative measurement of ES had excellent intra-and inter-reader reliability with non-expert readers, and good correlation with both multi-slice quantitative ES measurement and with MOAKS. Single-slice quantitative measurement is an efficient surrogate for multi-slice assessment, having shown similar validity based on comparison to semi-quantitative scoring by MSK radiologists. None CKK is consultant to Regeneron, LG Chem, and Express Scripts. He is principal investigator for pharma sponsored clinical trials to Abbvie, Cumberland, and GSK and DSMB to Kolon TissueGene and Avalor Therapeutics. CORRESPONDENCE ADDRESS: kwoh@arthritis.arizona.edu

A split-label design for simultaneous measurements of perfusion in distant slices by pulsed arterial spin labeling.

PurposeMultislice arterial spin labeling (ASL) MRI acquisitions are currently challenging in skeletal muscle because of long transit times, translating into low‐perfusion SNR in distal slices when large spatial coverage is required. However, fiber type and oxidative capacity vary along the length of healthy muscles, calling for multislice acquisitions in clinical studies. We propose a new variant of flow alternating inversion recovery (FAIR) that generates sufficient ASL signal to monitor exercise‐induced perfusion changes in muscle in two distant slices.MethodsLabel around and between two 7‐cm distant slices was created by applying the presaturation/postsaturation and selective inversion modules selectively to each slice (split‐label multislice FAIR). Images were acquired using simultaneous multislice EPI. We validated our approach in the brain to take advantage of the high resting‐state perfusion, and applied it in the lower leg muscle during and after exercise, interleaved with a single‐slice FAIR as a reference.ResultsWe show that standard multislice FAIR leads to an underestimation of perfusion, while the proposed split‐label multislice approach shows good agreement with separate single‐slice FAIR acquisitions in brain, as well as in muscle following exercise.ConclusionSplit‐label FAIR allows measuring muscle perfusion in two distant slices simultaneously without losing sensitivity in the distal slice.

Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.

Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data.

Automatic boundary detection of 4D ultrasound (4DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardium, models 1, 2, and 3 yielded average R2 values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models.

Multi-slice ptychography enables high-resolution measurements in extended chemical reactors

Ptychographic X-ray microscopy is an ideal tool to observe chemical processes under in situ conditions. Chemical reactors, however, are often thicker than the depth of field, limiting the lateral spatial resolution in projection images. To overcome this limit and reach higher lateral spatial resolution, wave propagation within the sample environment has to be taken into account. Here, we demonstrate this effect recording a ptychographic projection of copper(I) oxide nanocubes grown on two sides of a polyimide foil. Reconstructing the nanocubes using the conventional ptychographic model shows the limitation in the achieved resolution due to the thickness of the foil. Whereas, utilizing a multi-slice approach unambiguously separates two sharper reconstructions of nanocubes on both sides of the foil. Moreover, we illustrate how ptychographic multi-slice reconstructions are crucial for high-quality imaging of chemical processes by ex situ studying copper(I) oxide nanocubes grown on the walls of a liquid cell.

Statistically correcting dynamical electron scattering improves the refinement of protein nanocrystals, including charge refinement of coordinated metals.

Electron diffraction allows protein structure determination when only nanosized crystals are available. Nevertheless, multiple elastic (or dynamical) scattering, which is prominent in electron diffraction, is a concern. Current methods for modeling dynamical scattering by multi-slice or Bloch wave approaches are not suitable for protein crystals because they are not designed to cope with large molecules. Here, dynamical scattering of nanocrystals of insulin, thermolysin and thaumatin was limited by collecting data from thin crystals. To accurately measure the weak diffraction signal from the few unit cells in the thin crystals, alow-noise hybrid pixel Timepix electron-counting detector was used. The remaining dynamical component was further reduced in refinement using a likelihood-based correction, which was introduced previously for analyzing electron diffraction data of small-molecule nanocrystals and was adapted here for protein crystals. The procedure is shown to notably improve the structural refinement, in one case allowing the location of solvent molecules. It also allowed refinement of the charge states of bound metal atoms, an important element in protein function, through B-factor analysis of the metal atoms and their ligands. These results clearly increase the value of macromolecular electron crystallography as a complementary structural biology technique.

Differential Image Based Robot to MRI Scanner Registration with Active Fiducial Markers for an MRI-Guided Robotic Catheter System.

In magnetic resonance imaging (MRI) guided robotic catheter ablation procedures, reliable tracking of the catheter within the MRI scanner is needed to safely navigate the catheter. This requires accurate registration of the catheter to the scanner. This paper presents a differential, multi-slice image-based registration approach utilizing active fiducial coils. The proposed method would be used to preoperatively register the MRI image space with the physical catheter space. In the proposed scheme, the registration is performed with the help of a registration frame, which has a set of embedded electromagnetic coils designed to actively create MRI image artifacts. These coils are detected in the MRI scanner's coordinate system by background subtraction. The detected coil locations in each slice are weighted by the artifact size and then registered to known ground truth coil locations in the catheter's coordinate system via least-squares fitting. The proposed approach is validated by using a set of target coils placed withing the workspace, employing multi-planar capabilities of the MRI scanner. The average registration and validation errors are respectively computed as 1.97 mm and 2.49 mm. The multi-slice approach is also compared to the single-slice method and shown to improve registration and validation by respectively 0.45 mm and 0.66 mm.

Calibrationless parallel imaging reconstruction for multislice MR data using low-rank tensor completion.

To provide joint calibrationless parallel imaging reconstruction of highly accelerated multislice 2D MR k-space data. Adjacent image slices in multislice MR data have similar coil sensitivity maps, spatial support, and image content. Such similarities can be utilized to improve image quality by reconstructing multiple slices jointly with low-rank tensor completion. Specifically, the multichannel k-space data from multiple slices are constructed into a block-wise Hankel tensor and iteratively updated by promoting tensor low-rankness through higher-order SVD. This multislice block-wise Hankel tensor completion was implemented for 2D spiral and Cartesian k-space undersampling where sampling patterns vary between adjacent slices. The approach was evaluated with human brain MR data and compared to the traditional single-slice simultaneous autocalibrating and k-space estimation reconstruction. The proposed multislice block-wise Hankel tensor completion approach robustly reconstructed highly undersampled multislice 2D spiral and Cartesian data. It produced substantially lower level of artifacts compared to the traditional single-slice simultaneous autocalibrating and k-space estimation reconstruction. Quantitative evaluation using error maps and root mean square error demonstrated its significantly improved performance in terms of residual artifacts and root mean square error. Our proposed multislice block-wise Hankel tensor completion method exploits the similar coil sensitivity and image content within multislice MR data through a tensor completion framework. It offers a new and effective approach to acquire and reconstruct highly undersampled multislice MR data in a calibrationless manner.

Comparison of distributed memory algorithms for X-ray wave propagation in inhomogeneous media.

Calculations of X-ray wave propagation in large objects are needed for modeling diffractive X-ray optics and for optimization-based approaches to image reconstruction for objects that extend beyond the depth of focus. We describe three methods for calculating wave propagation with large arrays on parallel computing systems with distributed memory: (1) a full-array Fresnel multislice approach, (2) a tiling-based short-distance Fresnel multislice approach, and (3) a finite difference approach. We find that the first approach suffers from internode communication delays when the transverse array size becomes large, while the second and third approaches have similar scaling to large array size problems (with the second approach offering about three times the compute speed).