Volumetric Maps Research Articles

Method for the Calculation of Local Bead Volume in Multi-Axis Additive Manufacturing

In recent history, Multi-Axis Additive Manufacturing enjoys increasing attention for both industrial and research application. In comparison to traditional processes, the additional rotation axes enable parts to be printed with greater design flexibility, while reducing the need for support structures. The multi-axis motion additionally allows for optimization of structural properties of the parts. However, curved-layer printing requires significantly higher effort for planning and computation, as the layers exhibit curvature in their shape and feature local variations of the layer height and bead width. Consideration of these properties is often neglected in the development of path planning methods, although they pose a significant influence to the optical and structural quality of the printed parts. Local over- or under-extrusion can result in material accumulation or voids within the part and need to be avoided. Additionally, some Multi-Axis Additive Manufacturing processes like Directed Energy Deposition are especially prone to errors in practice, as the melt-pool behavior is hard to model and predict accurately. This paper introduces a method for the precise calculation and adjustment of local bead volume, as a possible solution to these problems. The method processes existing Cartesian paths and uses a model of the bead geometry to simulate the extrusion by rasterization. The method considers printing head orientation, as well as neighborhood information of adjacent beads and builds a volumetric map of the paths. Local extrusion volume is then determined for each waypoint of the path from this volume map and provided to the printer along with the G-Code, enabling adjusted volume deposition both in advance and during the process by iterative path-adaptation. The provided implementation is able to achieve favorable results for both use-cases but proves especially beneficial for the latter.

ADJUST: a dictionary-based joint reconstruction and unmixing method for spectral tomography

Advances in multi-spectral detectors are causing a paradigm shift in x-ray computed tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image reconstruction step is rather straightforward. If they are not known, however, the maps as well as the responses need to be estimated jointly. A conventional workflow in spectral CT involves performing volume reconstruction followed by material decomposition, or vice versa. However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem. To resolve this issue, we propose ‘A Dictionary-based Joint reconstruction and Unmixing method for Spectral Tomography’ (ADJUST). Our formulation relies on forming a dictionary of spectral signatures of materials common in CT and prior knowledge of the number of materials present in an object. In particular, we decompose the spectral volume linearly in terms of spatial material maps, a spectral dictionary, and the indicator of materials for the dictionary elements. We propose a memory-efficient accelerated alternating proximal gradient method to find an approximate solution to the resulting bi-convex problem. From numerical demonstrations on several synthetic phantoms, we observe that ADJUST performs exceedingly well compared to other state-of-the-art methods. Additionally, we address the robustness of ADJUST against limited and noisy measurement patterns. The demonstration of the proposed approach on a spectral micro-CT dataset shows its potential for real-world applications. Code is available at https://github.com/mzeegers/ADJUST.

Continual Adaptation of Semantic Segmentation Using Complementary 2D-3D Data Representations

Semantic segmentation networks are usually pre-trained once and not updated during deployment. As a consequence, misclassifications commonly occur if the distribution of the training data deviates from the one encountered during the robot's operation. We propose to mitigate this problem by adapting the neural network to the robot's environment during deployment, without any need for external supervision. Leveraging complementary data representations, we generate a supervision signal, by probabilistically accumulating consecutive 2D semantic predictions in a volumetric 3D map. We then train the network on renderings of the accumulated semantic map, effectively resolving ambiguities and enforcing multi-view consistency through the 3D representation. In contrast to scene adaptation methods, we aim to retain the previously-learned knowledge, and therefore employ a continual learning experience replay strategy to adapt the network. Through extensive experimental evaluation, we show successful adaptation to real-world indoor scenes both on the ScanNet dataset and on in-house data recorded with an RGB-D sensor. Our method increases the segmentation accuracy on average by 9.9% compared to the fixed pre-trained neural network, while retaining knowledge from the pre-training dataset.

Inhibitory Effect of DMSO on Halohydrin Dehalogenase: Experimental and Computational Insights into the Influence of an Organic Co-solvent on the Structural and Catalytic Properties of a Biocatalyst.

Invited for the cover of this issue are Zlatko Brkljača, Maja Majerić Elenkov and co-workers at the Ruđer Bošković Institute and University of Zagreb. The image depicts the enzyme halohydrin dehalogenase HheC, which is made up of four identical subunits, with marked catalytic residues and volumetric maps of water and DMSO in the active site. Read the full text of the article at 10.1002/chem.202201923.

3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T.

Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., T1 , T2 , and T1ρ ) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D-MRF) technique for simultaneous, volumetric mapping of T1 , T2 , and T1ρ in knee articular cartilage in under 9 min. It is evaluated on 11 healthy volunteers (mean age: 53 ± 9 years), five mild knee osteoarthritis (OA) patients (Kellgren-Lawrence (KL) score: 2, mean age: 60 ± 4 years), and the National Institute of Standards and Technology (NIST)/International Society for Magnetic Resonance in Medicine (ISMRM) system phantom. Proton density image, and T1 , T2, T1ρ relaxation times, and B1 + were estimated in the NIST/ISMRM system phantom as well as in the human knee medial and lateral femur, medial and lateral tibia, and patellar cartilage. The repeatability and reproducibility of the proposed technique were assessed in the phantom using analysis of the Bland-Altman plots. The intrasubject repeatability was assessed with the coefficient of variation (CV) and root mean square CV (rmsCV). The Mann-Whitney U test was used to assess the difference between healthy subjects and mild knee OA patients. The Bland-Altman plots in the NIST/ISMRM phantom demonstrated an average difference of 0.001% ± 015%, 1.2% ±7.1%, and 0.47% ±3% between two scans from the same 3-T scanner (repeatability), and 0.002% ± 015%, 0.62% ±10.5%, and 0.97% ± 14% between the scans acquired on two different 3-T scanners (reproducibility) for T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for T1 , T2 , and T1ρ , respectively. T1ρ relaxation time in the mild knee OA patients was significantly higher (p< 0.05) than in healthy subjects. The proposed 3D-MRF sequence is fast, reproducible, robust to B1 + inhomogeneity, and can simultaneously measure the T1 , T2 , T1ρ , and B1 + volumetric maps of the knee joint in a single scan within a clinically feasible scan time.

Comparison study of reconstruction algorithms for volumetric necrosis maps from 2D multi-slice GRE thermometry images

Cancer is a disease which requires a significant amount of careful medical attention. For minimally-invasive thermal ablation procedures, the monitoring of heat distribution is one of the biggest challenges. In this work, three approaches for volumetric heat map reconstruction (Delauney triangulation, minimum volume enclosing ellipsoids (MVEE) and splines) are presented based on uniformly distributed 2D MRI phase images rotated around the applicator’s main axis. We compare them with our previous temperature interpolation method with respect to accuracy, robustness and adaptability. All approaches are evaluated during MWA treatment on the same data sets consisting of 13 ex vivo bio protein phantoms, including six phantoms with simulated heat sink effects. Regarding accuracy, the DSC similarity results show a strong trend towards the MVEE (0.80pm 0.03) and the splines (0.77pm 0.04) method compared to the Delauney triangulation (0.75pm 0.02) or the temperature interpolation (0.73pm 0.07). Robustness is increased for all three approaches and the adaptability shows a significant trend towards the initial interpolation method and the splines. To overcome local inhomogeneities in the acquired data, the use of adaptive simulations should be considered in the future. In addition, the transfer to in vivo animal experiments should be considered to test for clinical applicability.

Interplanetary scintillation (IPS) analyses during LOFAR campaign mode periods that include the first three Parker Solar Probe close passes of the Sun

The University of California, San Diego (UCSD) time-dependent three-dimensional (3-D) reconstruction technique provides volumetric maps of density, velocity, and solar surface extrapolated magnetic fields by iteratively fitting our kinematic 3-D model to interplanetary scintillation (IPS) observations. While we currently use data from the Institute for Space-Earth Environmental Research (ISEE), Japan, we have also integrated this system adding data from Worldwide IPS Stations (WIPSS) network groups to increase both spatial and temporal coverage when these data are available. Some of these stations, especially the LOw Frequency ARray (LOFAR), centered in the Netherlands, currently operate in “campaign” mode only during periods of interest when the Parker Solar Probe (PSP) makes close passes to the Sun. The UCSD 3-D iterative reconstruction technique is unique in its ability to yield a low-resolution seamless extension of density and velocity parameters measured in situ, going outward into the surrounding interplanetary medium at the resolution of the volumetric data. We here present analyses using archival data sets from both ISEE, LOFAR, and BSA3 (Pushchino, Russia), mostly during PSP close passes of the Sun. These analyses provide the location of all inner planets from Mercury to Mars, and the spacecraft PSP, BepiColombo, and Solar Orbiter in the 3-D reconstructed volumes and can show the heliospheric structures that reach them as in-situ predictions of the structures present and forecasts of these parameters in near real time compared with near-Earth data sets.

Advanced radiological investigation of levodopa- induced dyskinesia in Parkinson’s disease patients: assessment of blood brain barrier function

Background: The purpose of this study is to explore changes in blood-brain barrier (BBB) function and volumetry associated with Parkinson’s disease (PD) levodopa-induced-dyskinesia (LID). Methods: Twenty-six PD patients [matched pairs, 13 with LID (LID+) and 13 without (LID-)], performed high resolution 3D FSPGR MRI, applying a novel methodology developed for calculating delayed- enhancement-subtraction-maps, representing BBB function. Segmentation software calculated volumes of pre-determined brain structures and the mean BBB function was calculated for each structure. Comparisons between the LID+ and LID- paired patients and within patient, between the more and less affected hemisphere (MAH, LAH) and correlation tests with lateralized UPDRS motor scores were performed. Results: There were no significant differences in volumetric or BBB map characteristics between the matched LID+ and LID- patients regarding most brain areas except for the inferior parietal cortex (IPC) of the MAH that displayed a higher BBB disruption in LID+ vs. LID- patients. A positive correlation was found with the motor score of the side contralateral to the MAH(r = 0.58, p&lt;0.038) among the LID+ patients. Conclusions: We demonstrated an association between slight BBB disruption in the IPC and LID in patients with PD using a new MRI methodology. As currently there is no known straightforward biological explanation for this positive finding, it might be genuine and novel, or spurious. Further studies to explore BBB functioning in the various stages of PD and its motor complications are needed, as well as further investigation of the IPC clinical importance and correction for epiphenomenon.

Emerging trends in Diagnosis and Treatment of Brain Tumor

Brain tumors are rare but have high mortality rate among children and young adults. The purpose of this report is to portray the situation of imaging strategies and advancements for distinguishing reaction of cerebrum tumors to remedy within the placing of multicenter medical trials. Inside as of now utilized advances, usage of institutionalized image procurement and the usage of volumetric appraisals and subtraction maps are likely going to decorate tumor notion, depiction, and dimension. Throughout the subsequent couple of years, new innovations, for instance, 23Na MRI and CEST imaging improvements may be investigated for their usage in growing the ability to quantitatively photo tumor response in order to provide remedies in a scientific trial placing. The combination of poor visualization and absence of remedial choices urge the need to enhance clinical results for patients experiencing CNS malignancies

Bragg edge tomography characterization of additively manufactured 316L steel

In this work we perform a neutron Bragg edge tomography of stainless steel 316L additive manufacturing samples, one as built via standard laser powder bed fusion, and one using the novel three-dimensional (3D) laser shock peening technique. First, we consider conventional attenuation tomography of the two samples by integrating the signal for neutron wavelengths beyond the last Bragg edge, to analyze the bulk density properties of the material. This is used to map defects, such as porosities or cracks, which yield a lower density. Second, we obtain strain maps for each of the tomography projections by tracking the wavelength of the strongest Bragg edge corresponding to the {111} lattice plane family. Algebraic reconstruction techniques are used to obtain volumetric 3D maps of the strain in the bulk of the samples. It is found that not only the volume of the sample where the shock peening treatment was carried out yields a higher bulk density, but also a deep and remarkable compressive strain region. Finally, the analysis of the Bragg edge heights as a function of the projection angle is used to describe qualitatively crystallographic texture properties of the samples.

Volumetric Instance-Level Semantic Mapping Via Multi-View 2D-to-3D Label Diffusion

Robots operating in real-world settings often need to plan interactions with surrounding scene elements and therefore, it is crucial for them to understand their workspace at the level of individual objects. In this spirit, this work presents a novel approach to progressively build instance-level, dense 3D maps from color and depth cues acquired by either a moving RGB-D sensor or a camera-LiDAR setup, whose pose is being tracked. The proposed framework processes each input RGB image with a semantic instance segmentation neural network and uses depth information to extract a set of per-frame, semantically labeled 3D instance segments, which then get matched to object instances already identified in previous views. Following integration of these newly detected instance segments in a global volumetric map, an efficient label diffusion scheme that considers multi-view instance predictions together with the reconstructed scene geometry is used to refine 3D segmentation boundaries. Experiments on indoor benchmarking RGB-D sequences show that the proposed system achieves state-of-the-art performance in terms of 3D segmentation accuracy, while reducing the computational processing cost required at each frame. Furthermore, the applicability of the system to challenging domains outside the traditional office scenes is demonstrated by testing it on a robotic excavator equipped with a calibrated camera-LiDAR setup, with the goal of segmenting individual boulders in a highly cluttered construction scenario.

Monitoring tissue engineered constructs and protocols with laboratory-based x-ray phase contrast tomography

Tissue engineering (TE) aims to generate bioengineered constructs which can offer a surgical treatment for many conditions involving tissue or organ loss. Construct generation must be guided by suitable assessment tools. However, most current tools (e.g. histology) are destructive, which restricts evaluation to a single-2D anatomical plane, and has no potential for assessing constructs prior to or following their implantation. An alternative can be provided by laboratory-based x-ray phase contrast computed tomography (PC-CT), which enables the extraction of 3D density maps of an organ's anatomy. In this work, we developed a semi-automated image processing pipeline dedicated to the analysis of PC-CT slices of oesophageal constructs. Visual and quantitative (density and morphological) information is extracted on a volumetric basis, enabling a comprehensive evaluation of the regenerated constructs. We believe the presented tools can enable the successful regeneration of patient-specific oesophagus, and bring comparable benefit to a wide range of TE applications. Statement of significancePhase contrast computed tomography (PC-CT) is an imaging modality which generates high resolution volumetric density maps of biological tissue. In this work, we demonstrate the use of PC-CT as a new tool for guiding the progression of an oesophageal tissue engineering (TE) protocol. Specifically, we developed a semi-automated image-processing pipeline which analyses the oesophageal PC-CT slices, extracting visual and quantitative (density and morphological) information. This information was proven key for performing a comprehensive evaluation of the regenerated constructs, and cannot be obtained through existing assessment tools primarily due to their destructive nature (e.g. histology). This work paves the way for using PC-CT in a wide range of TE applications which can be pivotal for unlocking the potential of this field.

Spatial probability maps of the segments of the postcentral sulcus in the human brain.

The postcentral sulcus is the posterior boundary of the postcentral gyrus where the somatosensory cortex is represented. In the human brain, the postcentral sulcus is composed of five distinct segments that are related to the somatosensory representation of different parts of the body. Segment 1 of the postcentral sulcus, located near the dorsomedial boundary of each hemisphere, is associated with toe/leg representations, segment 2 with arm/hand representations, segment 3 with blinking, and segments 4 and 5, which are near the lateral fissure and the parietal operculum, with the mouth and tongue representations. The variability in location and spatial extent of these five segments were quantified in 40 magnetic resonance imaging (MRI) anatomical brain scans registered to the stereotaxic space of the Montreal Neurological Institute (MNI space), in the form of volumetric (using MINC Toolkit) and surface (using FreeSurfer) spatial probability maps. These probability maps can be used by researchers and clinicians to improve the localization of the segments of the postcentral sulcus in MRI images of interest and also to improve the interpretation of the location of activation peaks generated in functional neuroimaging studies investigating somatosensory cortex.

Dynamic lung compliance imaging from 4DCT-derived volume change estimation

Background. Lung compliance (LC) is the ability of the lung to expand with changes in pressure and is one of the earliest physiological measurements to be altered in patients with parenchymal lung disease. Therefore, compliance monitoring could potentially identify patients at risk for disease progression. However, in clinical practice, compliance measurements are prohibitively invasive for use as a routine monitoring tool. Purpose. We propose a novel method for computing dynamic lung compliance imaging (LCI) from non-contrast computed tomography (CT) scans. LCI applies image processing methods to free-breathing 4DCT images, acquired under two different continuous positive airway pressures (CPAP) applied using a full-face mask, in order to compute the lung volume change induced by the pressure change. LCI provides a quantitative volumetric map of lung stiffness. Methods. We compared mean LCI values computed for 10 patients with idiopathic pulmonary fibrosis (IPF) and 7 non-IPF patients who were screened for lung nodules. 4DCTs were acquired for each patient at 5 cm and 10 cm H20 CPAP, as the patients were free breathing at functional residual capacity. LCI was computed from the two 4DCTs. Mean LCI intensities, which represent relative voxel volume change induced by the change in CPAP pressure, were computed. Results. The mean LCI values for patients with IPF ranged between [0.0309, 0.1165], whereas the values ranged between [0.0704, 0.2185] for the lung nodule cohort. Two-sided Wilcoxon rank sum test indicated that the difference in medians is statistically significant (p value = 0.009) and that LCI -measured compliance is overall lower in the IPF patient cohort. Conclusion. There is considerable difference in LC scores between patients with IPF compared to controls. Future longitudinal studies should look for LC alterations in areas of lung prior to radiographic detection of fibrosis to further characterize LCI’s potential utility as an image marker for disease progression.

LV-GCNN: A lossless voxelization integrated graph convolutional neural network for surface reconstruction from point clouds

Surface reconstruction from a 3D point cloud is a long-standing problem in the field of computer graphics and vision, especially for point clouds that are sparse and noisy. To solve this problem, a novel method, termed LV-GCNN, that combines lossless voxelization and a graph convolutional neural network is proposed in this paper. Firstly, a lossless voxelization method for point clouds, which achieves lossless conversion from disordered point clouds to ordered volumetric maps, is proposed. Secondly, with the generated voxel, the feature pyramid is extracted through a 3D convolutional neural network. Thirdly, based on the similarity between the graph and surface mesh, the graph neural network is initialized as a unit spherical mesh. The coordinate relationship between the graph vertices and feature voxel units is used to match the corresponding hierarchical features for graph vertices. The initial spherical mesh is progressively deformed and upsampled via graph convolution and graph unpooling to achieve coarse-to-fine optimization of the surface mesh. Finally, because the deformation-based approach cannot reconstruct objects with a genus greater than zero, a genus optimization method is designed. Experiments show that the surface meshes generated by the LV-GCNN are comparable to or better than the results of state-of-the-art methods under several evaluation criteria. In addition, the reconstruction effect under different situations is discussed. Ablation experiments show the importance of several applied modules in LV-GCNN. Extra experiments show that the proposed method can achieve impressive results for point sets with diverse densities or that contain different levels of noise. The LV-GCNN results outperform other methods when the input point cloud is exceptionally sparse (256 points) or contains Gaussian noise with a standard deviation of 0.05. The chamfer distance (CD), Hausdorff distance (HD), voxel difference (VD), and depth difference (DD) of the reconstruction results of the LV-GCNN are 0.0494, 0.1457, 0.7390, and 18,402 when 256 points are used as input and are 0.0654, 0.1582, 0.6735, and 21,150 when points with noise with a standard deviation of 0.05 are used as input.

Common abnormality of gray matter integrity in substance use disorder and obsessive-compulsive disorder: A comparative voxel-based meta-analysis.

The objective of the current study is to determine robust transdiagnostic brain structural markers for compulsivity by capitalizing on the increasing number of case‐control studies examining gray matter volume (GMV) alterations in substance use disorders (SUD) and obsessive‐compulsive disorder (OCD). Voxel‐based meta‐analysis within the individual disorders and conjunction analysis were employed to reveal common GMV alterations between SUDs and OCD. Meta‐analytic coordinates and signed brain volumetric maps determining directed (reduced/increased) GMV alterations between the disorder groups and controls served as the primary outcome. The separate meta‐analysis demonstrated that SUD and OCD patients exhibited widespread GMV reductions in frontocortical regions including prefrontal, cingulate, and insular. Conjunction analysis revealed that the left inferior frontal gyrus (IFG) consistently exhibited decreased GMV across all disorders. Functional characterization suggests that the IFG represents a core hub in the cognitive control network and exhibits bidirectional (Granger) causal interactions with the striatum. Only OCD showed increased GMV in the dorsal striatum with higher changes being associated with more severe OCD symptomatology. Together the findings demonstrate robustly decreased GMV across the disorders in the left IFG, suggesting a transdiagnostic brain structural marker. The functional characterization as a key hub in the cognitive control network and casual interactions with the striatum suggest that deficits in inhibitory control mechanisms may promote compulsivity and loss of control that characterize both disorders.

Hierarchical topometric representation of 3D robotic maps

In this paper, we propose a method for generating a hierarchical, volumetric topological map from 3D point clouds. There are three basic hierarchical levels in our map: $storey - region - volume$. The advantages of our method are reflected in both input and output. In terms of input, we accept multi-storey point clouds and building structures with sloping roofs or ceilings. In terms of output, we can generate results with metric information of different dimensionality, that are suitable for different robotics applications. The algorithm generates the volumetric representation by generating $volumes$ from a 3D voxel occupancy map. We then add $passage$s (connections between $volumes$), combine small $volumes$ into a big $region$ and use a 2D segmentation method for better topological representation. We evaluate our method on several freely available datasets. The experiments highlight the advantages of our approach.

Altitude Performance and Fuel Consumption Modelling of Aircraft Piston Engine Rotax 912 S/ULS

Rotax 912 spark-ignition (SI) naturally aspirated aircraft piston engine is one of the most popular prime movers for the ultra-light weight aircrafts used for short and moderate range flights. SI engines operate at stoichiometric combustion and at high altitude conditions atmospheric pressure is lower than sea level reducing mass of air intake to the engine. At these conditions engine power degrades significantly from sea level, affecting flight parameters such as range and endurance. In order to optimize these parameters, engine power modelling is of vital importance. Within the scope of this study, a thermodynamic SI engine model with performance parameters (break torque and power) and fuel consumption estimation developed in MATLAB/Simulink software. Model tuning is realised using data extracted from Rotax engine operating manual at sea level and high altitude conditions. Model inputs are set as altitude, throttle lever and engine speed. Pressure drop at the intake port is modelled as a function of mass air flow using engine operating manual pressure data at various engine speed points. Mass air flow is determined via employing a volumetric efficiency map based on intake port pressure and engine speed inputs, calibrated using engine operating manual fuel data considering stoichiometric combustion. Compression and expansion strokes area modelled as isentropic events and combustion is modelled as constant volume heat input at top dead centre (TDC). However a combustion efficiency map based on engine speed and fuel flow is employed for the heat input in order to tune the work output of the engine and heat loss to the coolant and exhaust. Pumping loss is calculated based on friction mean effective pressure data. Introduced approach provides high accuracy performance and fuel consumption modelling based on engine operating manual data and can be used for flight parameters optimization studies.

LoLa-SLAM: Low-Latency LiDAR SLAM Using Continuous Scan Slicing

Real-time 6D pose estimation is a key component for autonomous indoor navigation of Unmanned Aerial Vehicles (UAVs). This letter presents a low-latency LiDAR SLAM framework based on LiDAR scan slicing and concurrent matching, called LoLa-SLAM. Our framework uses sliced point cloud data from a rotating LiDAR in a concurrent multi-threaded matching pipeline for 6D pose estimation with high update rate and low latency. The LiDAR is actuated using a 2D Lissajous spinning pattern to overcome the sensor's limited FoV. We propose a two-dimensional roughness model to extract the feature points for fine matching and registration of the point cloud. In addition, the pose estimator engages a temporal motion predictor that assists in finding the feature correspondences in the map for the fast convergence of the non-linear optimizer. Subsequently, an Extended Kalman Filter (EKF) is adopted for final pose fusion. The framework is evaluated in multiple experiments by comparing the accuracy, latency, and the update rate of the pose estimation for the trajectories flown in an indoor environment. We quantify the superior quality of the generated volumetric map in comparison to the state-of-the-art frameworks. We further examine the localization precision using ground truth pose information recorded by a total station unit.

Simultaneous multi-banding and multi-echo phase encoding for the accelerated acquisition of high-resolution volumetric diffusivity maps by spatiotemporally encoded MRI

PurposeSpatiotemporal Encoding (SPEN) is an ultrafast imaging technique where the low-bandwidth axis is rasterized in a joint spatial/k-domain. SPEN benefits from increased robustness to field inhomogeneities, folding-free reconstruction of subsampled data, and an ability to combine multiple interleaved or signal averaged scans –yet its relatively high SAR complicates volumetric uses. Here we show how this can be alleviated by merging simultaneous multi-band excitation, with intra-slab multi-echo (ME) phase encoding, for the acquisition of high definition volumetric DWI/DTI data. MethodsA protocol involving phase-cycling of simultaneous multi-banded z-slab excitations in independently ky-interleaved scans, together with ME trains that kz-encoded positions within these slabs, was implemented. A reconstruction incorporating a CAIPIRINHA-like encoding of the multiple bands and exploiting SPEN's ability to deliver self-referenced, per-shot phase maps, then led to high-definition diffusivity acquisitions, with reduced SAR and acquisition times vis-à-vis non-optimized 3D counterparts. ResultsThe new protocol was used to collect full brain 3 T DTI experiments at a variety of nominal voxel sizes, ranging from 1.95 to 2.54 mm3. In general, the new protocol yielded superior sensitivity and fewer distortions than what could be observed in comparably timed phase-encoded 3D SPEN, multi-slice 2D SPEN, or optimized EPI counterparts. ConclusionsA robust procedure for acquiring volumetric DWI/DTI data was developed and demonstrated.