Anisotropic Spatial Resolution Research Articles

HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images

BackgroundAccurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity.MethodsIn this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios.ResultsOur baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks.ConclusionsComprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.

Exploring the spatial resolution of TMS-EEG coupling on the sensorimotor region

The use of TMS-EEG coupling as a neuroimaging tool for the functional exploration of the human brain recently gained strong interest. If this tool directly inherits the fine temporal resolution from EEG, its spatial counterpart remains unknown. In this study, we explored the spatial resolution of TMS-EEG coupling by evaluating the minimal distance between two stimulated cortical sites that would significantly evoke different response dynamics. TMS evoked responses were mapped on the sensorimotor region in twenty participants. The stimulation grid was composed of nine targets separated between 10 and 15 mm on average. The dynamical signatures of TMS evoked activity were extracted and compared between sites using both local and remote linear regression scores and spatial generalized mixed models. We found a significant effect of the distance between stimulated sites on their dynamical signatures, neighboring sites showing differentiable response dynamics. Besides, common dynamical signatures were also found between sites up to 25-30 mm from each other. This overlap in dynamical properties decreased with distance and was stronger between sites within the same Brodmann area. Our results suggest that the spatial resolution of TMS-EEG coupling might be at least as high as 10 mm. Furthermore, our results reveal an anisotropic spatial resolution that was higher across than within the same Brodmann areas, in accordance with the TMS induced E-field modeling. Common cytoarchitectonic leading to shared dynamical properties within the same Brodmann area could also explain this anisotropy. Overall, these findings suggest that TMS-EEG benefits from the spatial resolution of TMS, which makes it an accurate technique for meso-scale brain mapping.

Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy.

Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target images, our method greatly reduces the effort to be put into practice as the training of a network requires only a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport-driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in the lateral image plane and low-resolution 2D images in other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution but also restores suppressed visual details between the imaging planes and removes imaging artifacts.

Cardiac Strain Imaging Using Multi-Perspective Ultrafast Ultrasound

The heart is a complex organ with a high level of deformation occurring in different directions. Ultrasound imaging has proven to be a valuable tool to quantify these deformations. However, strain is not always measured accurately in vital parts of the heart when only using a single probe, even at a high frame rate, because of the anisotropic spatial resolution and contrast. Therefore, a multi-perspective ultrafast ultrasound (US) strain imaging method was developed, aiming at investigating improvements in strain while operating two probes at different relative angles. In an ex-vivo experiment of a beating porcine heart, parasternal short axis views of the left ventricle (LV) were acquired by two phased array probes with different relative angles (30°–75°) at a frame rate of 170 frames per second (FPS). A fully automatic registration algorithm was developed to register the image datasets for all cases. Next, radio frequency (RF) based strain imaging was performed. Axial displacements were compounded based on the unit axial vectors of the dual probes to improve motion tracking and strain estimation. After performing multi-perspective strain imaging, compounded radial and circumferential strain both improve compared to single probe strain imaging. While increasing the inter-probe angle from 30° to 75°, the mean tracking error (ME), mean drift error (MDE) and strain variability (SV) decreased, and signal-to-noise ratio (SNRe) increased for both strain components. For the largest angle (75°), large reductions in ME (−42%), MDE (−50%) and SV (−48%) were observed. The SNRe increased by 253 and 39% for radial and circumferential strain, respectively, and strain curves revealed less noise for each region. In summary, a multi-perspective ultrafast US strain imaging method was introduced to improve cardiac strain estimation in an ex-vivo beating porcine heart setup.

On the shape of convolution kernels in MRI reconstruction: Rectangles versus ellipsoids.

Many MRI reconstruction methods (including GRAPPA, SPIRiT, ESPIRiT, LORAKS, and convolutional neural network [CNN] methods) involve shift-invariant convolution models. Rectangular convolution kernel shapes are often chosen by default, although ellipsoidal kernel shapes have potentially appealing theoretical characteristics. In this work, we systematically investigate the differences between different kernel shape choices in several contexts. It is well-understood that a rectangular region of k-space is associated with anisotropic spatial resolution, while ellipsoidal regions can be associated with more isotropic resolution. Further, for a fixed spatial resolution, ellipsoidal kernels are associated with substantially fewer parameters than rectangular kernels. These characteristics suggest that ellipsoidal kernels may have certain advantages over rectangular kernels. We used real retrospectively undersampled k-space data to empirically study the characteristics of rectangular and ellipsoidal kernels in the context of seven methods (GRAPPA, SPIRiT, ESPIRiT, SAKE, LORAKS, AC-LORAKS, and CNN-based reconstructions). Empirical results suggest that both kernel shapes can produce reconstructed images with similar error metrics, although the ellipsoidal shape can often achieve this with reduced computation time and memory usage and/or fewer model parameters. Ellipsoidal kernel shapes may offer advantages over rectangular kernel shapes in various MRI applications.

3D PBV-Net: An automated prostate MRI data segmentation method

Prostate cancer is one of the most common deadly diseases in men worldwide, which is seriously affecting people's life and health. Reliable and automated segmentation of the prostate gland in MRI data is exceptionally critical for diagnosis and treatment planning of prostate cancer. Although many automated segmentation methods have emerged, including deep learning based approaches, segmentation performance is still poor due to the large variability of image appearance, anisotropic spatial resolution, and imaging interference. This study proposes an automated prostate MRI data segmentation approach using bicubic interpolation with improved 3D V-Net (dubbed 3D PBV-Net). Considering the low-frequency components in the prostate gland, the bicubic interpolation is applied to preprocess the MRI data. On this basis, a 3D PBV-Net is developed to perform prostate MRI data segmentation. To illustrate the effectiveness of our approach, we evaluate the proposed 3D PBV-Net on two clinical prostate MRI data datasets, i.e., PROMISE 12 and TPHOH, with the manual delineations available as the ground truth. Our approach generates promising segmentation results, which have achieved 97.65% and 98.29% of average accuracy, 0.9613 and 0.9765 of Dice metric, 3.120 mm and 0.9382 mm of Hausdorff distance, and average boundary distance of 1.708, 0.7950 on PROMISE 12 and TPHOH datasets, respectively. Our method has effectively improved the accuracy of automated segmentation of the prostate MRI data and is promising to meet the accuracy requirements for telehealth applications.

X-ray phase tomography with near-field speckles for three-dimensional virtual histology

High-contrast, high-resolution imaging of biomedical specimens is indispensable for studying organ function and pathologies. Conventional histology, the gold standard for soft-tissue visualization, is limited by its anisotropic spatial resolution, elaborate sample preparation, and lack of quantitative image information. X-ray absorption or phase tomography have been identified as promising alternatives enabling non-destructive, distortion-free three-dimensional (3D) imaging. However, reaching sufficient contrast and resolution with a simple experimental procedure remains a major challenge. Here, we present a solution based on x-ray phase tomography through speckle-based imaging (SBI). We demonstrate on a mouse kidney that SBI delivers comprehensive 3D maps of hydrated, unstained soft tissue, revealing its microstructure and delivering quantitative tissue-density values at a density resolution of better than 2 m g / c m 3 and spatial resolution of better than 8 µm. We expect that SBI virtual histology will find widespread application in biomedicine and will open up new possibilities for research and histopathology.

Isotropic three-dimensional imaging with lattice light-sheet difference microscopy

Through the interference of several beams with the same phase at the rear pupil, an optical lattice is generated and used as the excitation light field in lattice light-sheet microscopy (LLSM). Although, LLSM offers fast imaging ability through the dithered mode, it suffers from poor axial resolution. On the other hand, the structured illumination microscopy (SIM) mode in LLSM provides a high but anisotropic spatial resolution at the price of a lower imaging speed. In this Letter, we propose a new, to the best of our knowledge, type of optical lattice by defining the composed beam vectors with different phases. The lattice light-sheet difference microscopy (LLSDM) proposed in this work utilizes the new optical lattice along with the conventional one by simply changing the pattern loaded on the spatial light modulator. Through simulations with parameters obtained from the real acquisition procedures and the microscope properties, we examine the feasibility of LLSDM, and obtain nearly isotropic spatial resolution and great alleviation of the sidelobes. These characteristics enable LLSDM to broaden the accessible imaging content of light-sheet microscopes and further facilitate their applications in life sciences.

Comments on the NEMA NU 4-2008 Standard on Performance Measurement of Small Animal Positron Emission Tomographs

The National Electrical Manufacturers Association’s (NEMA) NU 4-2008 standard specifies methodology for evaluating the performance of small-animal PET scanners. The standard’s goal is to enable comparison of different PET scanners over a wide range of technologies and geometries used. In this work, we discuss if the NEMA standard meets these goals and we point out potential flaws and improvements to the standard.For the evaluation of spatial resolution, the NEMA standard mandates the use of filtered backprojection reconstruction. This reconstruction method can introduce star-like artifacts for detectors with an anisotropic spatial resolution, usually caused by parallax error. These artifacts can then cause a strong dependence of the resulting spatial resolution on the size of the projection window in image space, whose size is not fully specified in the NEMA standard. If the PET ring has detectors which are perpendicular to a Cartesian axis, then the resolution along this axis will typically improve with larger projection windows.We show that the standard’s equations for the estimation of the random rate for PET systems with intrinsic radioactivity are circular and not satisfiable. However, a modified version can still be used to determine an approximation of the random rates under the assumption of negligible random rates for small activities and a constant scatter fraction. We compare the resulting estimated random rates to random rates obtained using a delayed coincidence window and two methods based on the singles rates. While these methods give similar estimates, the estimation method based on the NEMA equations overestimates the random rates.In the NEMA standard’s protocol for the evaluation of the sensitivity, the standard specifies to axially step a point source through the scanner and to take a different scan for each source position. Later, in the data analysis section, the standard does not specify clearly how the different scans have to be incorporated into the analysis, which can lead to unclear interpretations of publicized results.The standard’s definition of the recovery coefficients in the image quality phantom includes the maximum activity in a region of interest, which causes a positive correlation of noise and recovery coefficients. This leads to an unintended trade-off between desired uniformity, which is negatively correlated with variance (i.e., noise), and recovery.With this work, we want to start a discussion on possible improvements in a next version of the NEMA NU-4 standard.

3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images.

Accurate and reliable segmentation of the prostate gland using magnetic resonance (MR) imaging has critical importance for the diagnosis and treatment of prostate diseases, especially prostate cancer. Although many automated segmentation approaches, including those based on deep learning have been proposed, the segmentation performance still has room for improvement due to the large variability in image appearance, imaging interference, and anisotropic spatial resolution. In this paper, we propose the 3D adversarial pyramid anisotropic convolutional deep neural network (3D APA-Net) for prostate segmentation in MR images. This model is composed of a generator (i.e., 3D PA-Net) that performs image segmentation and a discriminator (i.e., a six-layer convolutional neural network) that differentiates between a segmentation result and its corresponding ground truth. The 3D PA-Net has an encoder-decoder architecture, which consists of a 3D ResNet encoder, an anisotropic convolutional decoder, and multi-level pyramid convolutional skip connections. The anisotropic convolutional blocks can exploit the 3D context information of the MR images with anisotropic resolution, the pyramid convolutional blocks address both voxel classification and gland localization issues, and the adversarial training regularizes 3D PA-Net and thus enables it to generate spatially consistent and continuous segmentation results. We evaluated the proposed 3D APA-Net against several state-of-the-art deep learning-based segmentation approaches on two public databases and the hybrid of the two. Our results suggest that the proposed model outperforms the compared approaches on three databases and could be used in a routine clinical workflow.

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).

Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods. Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels pl and ph ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise and spatial resolution trade-offs. The optimal operating points determined through the d' metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods, there was a strong dependence of the overall d' magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d' value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work. A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object.

Fast multiresolution data acquisition for magnetic particle imaging using adaptive feature detection.

Magnetic particle imaging is a tomographic imaging modality capable of determining the distribution of magnetic nanoparticles with high temporal resolution. The spatial resolution of magnetic particle imaging is influenced by the gradient strength of the selection field used for spatial encoding. By increasing the gradient strength, the spatial resolution is improved, but at the same time the imaging volume decreases. For a high-resolution image of an extended field-of-view, a multipatch approach can be used by shifting the sampling trajectory in space. As the total imaging timescales with the number of patches, the downside of the multipatch method is the degradation of the temporal resolution. The purpose of this work was to develop a scanning procedure incorporating the advantages of imaging at multiple gradient strengths. A low-resolution overview scan is performed at the beginning followed by a small number of high-resolution scans at adaptively detected locations extracted from the low-resolution scan. By combining all data during image reconstruction, it is possible to obtain a large field-of-view image of anisotropic spatial resolution. It is measured in a fraction of time compared to a fully sampled high-resolution field of view image. Magnetic particle imaging is a flexible imaging method allowing to rapidly scan small volumes. When scaling magnetic particle imaging from small animal to human applications, it will be essential to keep the acquisition time low while still capturing larger volumes at high resolution. With our proposed adaptive multigradient imaging sequence, it is possible to capture a large field of view while keeping both the temporal and the spatial resolution high.

Murine pulmonary imaging at 7T: T2* and T1 with anisotropic UTE.

To measure the T2* and T1 of mouse lung at 7T via anisotropic-resolution radial ultrashort echo-time imaging with ellipsoidal k-space coverage. Ellipsoidal field-of-view was created by expanding uniform spherical k-space coverage. The effects of T2* and ellipsoidal sampling on image resolution were investigated by using point-spread-function analysis and resolution phantoms. Finally, this ellipsoidal sampling approach was used to measure the lung T2* and T1 of healthy C57BL/6 mice at the increasingly common preclinical field strength of 7T. Lung parenchyma T2* of 17- to 23-week-old mice at 7T was 0.395 ± 0.033 ms. T1 of lung and left- and right-heart ventricles was 1452.5 ± 87.0 ms, 1810.5 ± 54.6 ms, and 1602.6 ± 120.9 ms, respectively. Ellipsoidal k-space sampling provides enhanced resolution for a fixed scanning time or provides equivalent (although anisotropic) spatial resolution with reduced scanning times, while simultaneously avoiding fold-in artifacts. Using these techniques, the first T2* and T1 measures of mouse lung at 7T are reported. Ultrashort echo-time imaging with ellipsoidal k-space coverage significantly increases in-plane resolution without increasing scanning time, or equivalently, decreases scanning time while maintaining equivalent in-plane resolution. Magn Reson Med 79:2254-2264, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

High-resolution in vivo imaging of human locus coeruleus by magnetization transfer MRI at 3T and 7T

Locus Coeruleus (LC) is a neuromelanin-rich brainstem structure that is the source of noradrenaline in the cortex and is thought to modulate attention and memory. LC imaging in vivo is commonly performed with a 2D T1-weighted Turbo Spin Echo (TSE) MRI sequence, an approach that suffers from several drawbacks at 3T, including long acquisition times and highly anisotropic spatial resolution. In this study, we developed a high-resolution Magnetization Transfer (MT) sequence for LC imaging at both 7T and 3T and compared its performance to a TSE sequence. Results indicate that LC imaging can be achieved with an MT sequence at both 7 and 3T at higher spatial resolution than the 3T TSE. Furthermore, we investigated whether the currently disputed source of contrast in the LC region with a TSE sequence relates to MT effects or shortened T1 and T2* due to increased iron concentration. Our results suggest that the contrast in the LC area relates to MT effects. To conclude, in this study we managed to image the LC, for the first time, at 7T and at an increased resolution compared to the current state-of-the-art. Imaging the LC is highly relevant for clinical diagnostics as structural tissue properties of the LC may hold promise as a biomarker in neurodegenerative diseases.

Integrated Digital Tomosynthesis for patient positioning of image-guided radiation therapy

PurposeDigital Tomosynthesis (DTS), originally developed for diagnostic applications, has been recently introduced to image guided radiotherapy (IGRT). Due to limited scan range, DTS was featured by anisotropic spatial resolution. A new approach, integrated DTS (IDTS), was developed to improve image resolution of DTS for patient positioning in IGRT. Materials and methodsFor registration purpose, both on-board IDTS (O-IDTS) and reference IDTS (R-IDTS) were required. O-IDTS was generated from cone-beam (CB) projections acquired in two narrow scan ranges separated by 90°. R-IDTS was generated from digitally reconstructed radiographs (DRR) computed from planning CT in the same two narrow scan ranges. Target offsets were determined by registration of O-IDTS and R-IDTS. The reconstruction algorithms of DRR and IDTS were implemented on general purpose graphics processing unit (GPGPU) for acceleration purpose. IDTS approach was evaluated by phantom and patient cases. ResultsComparing with DTS, IDTS provided high-resolution images in both coronal view and sagittal views. The image resolution of IDTS in axial view was significantly improved compared to that of DTS, but still inferior compared to that of cone-beam computed tomography (CBCT). Reconstruction accuracy and registration accuracy for all cases were high which was within 1mm. Reconstruction performance of IDTS using general purpose graphics processing unit (GPGPU) can be substantially improved, thus competent for daily clinical use. ConclusionsIDTS can provide high-resolution images in coronal and sagittal views with fewer CB projections. Image registration based on IDTS was simple and reliable compared to DTS. IDTS is potentially a useful imaging tool for fast patient positioning in IGRT.

Comparison of FBP and Iterative Algorithms with Non-Uniform Angular Sampling.

Some people may believe that the filtered backprojection (FBP) algorithm does not work if the projection data are measured non-uniformly. Some may also believe that iterative algorithms can automatically handle the non-uniformly sampled data in the projector/backprojector. This paper claims that the FBP algorithm can effectively handle the situation where the angular sampling is not uniform. This paper compares the images that are reconstructed by both the FBP and the iterative Landweber algorithms when the angular sampling is nonuniform. When the iteration number is low, the iterative algorithms do not handle the non-uniform sampling properly. A weighting strategy is then suggested and it makes the image resolution more isotropic. In few-view tomography, the FBP and iterative algorithms both perform poorly if no other prior information is used. We have made the following observations: 1) When using an iterative algorithm, one must use early solutions due to noise amplification. 2) An early solution can have anisotropic spatial resolution if the angular sampling is not uniform. 3) The anisotropic resolution problem can be solved by introducing angle dependent weighting, which is not noise dependent. 4) The weighting is not effective when the iteration number is large. The weighting only affects the early solutions, and does not affect the converged solution. 5) When the iteration number is large, the model-mismatch errors are amplified and cause artifacts in the image. 6) The FBP algorithm is not sensitive to the model-mismatch errors, and does not have the "early solution" problems. 7) In few-view tomography, both FBP and iterative algorithms perform poorly, while the FBP algorithm gives a sharper image than the iterative algorithm does.

X-ray strain tensor imaging: FEM simulation and experiments with a micro-CT

In tissue elasticity imaging, measuring the strain tensor components is necessary to solve the inverse problem. However, it is impractical to measure all the tensor components in ultrasound or MRI elastography because of their anisotropic spatial resolution. The objective of this study is to compute 3D strain tensor maps from the 3D CT images of a tissue-mimicking phantom. We took 3D micro-CT images of the phantom twice with applying two different mechanical compressions to it. Applying the 3D image correlation technique to the CT images under different compression, we computed 3D displacement vectors and strain tensors at every pixel. To evaluate the accuracy of the strain tensor maps, we made a 3D FEM model of the phantom, and we computed strain tensor maps through FEM simulation. Experimentally obtained strain tensor maps showed similar patterns to the FEM-simulated ones in visual inspection. The correlation between the strain tensor maps obtained from the experiment and the FEM simulation ranges from 0.03 to 0.93. Even though the strain tensor maps suffer from high level noise, we expect the x-ray strain tensor imaging may find some biomedical applications such as malignant tissue characterization and stress analysis inside the tissues.

The Utility of Micro-CT and MRI in the Assessment of Longitudinal Growth of Liver Metastases in a Preclinical Model of Colon Carcinoma

Liver is a common site for distal metastases in colon and rectal cancer. Numerous clinical studies have analyzed the relative merits of different imaging modalities for detection of liver metastases. Several exciting new therapies are being investigated in preclinical models. But, technical challenges in preclinical imaging make it difficult to translate conclusions from clinical studies to the preclinical environment. This study addresses the technical challenges of preclinical magnetic resonance imaging (MRI) and micro-computed tomography (CT) to enable comparison of state-of-the-art methods for following metastatic liver disease. We optimized two promising preclinical protocols to enable a parallel longitudinal study tracking metastatic human colon carcinoma growth in a mouse model: T2-weighted MRI using two-shot PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) and contrast-enhanced micro-CT using a liposomal contrast agent. Both methods were tailored for high throughput with attention to animal support and anesthesia to limit biological stress. Each modality has its strengths. Micro-CT permitted more rapid acquisition (<10 minutes) with the highest spatial resolution (88-micron isotropic resolution). But detection of metastatic lesions requires the use of a blood pool contrast agent, which could introduce a confound in the evaluation of new therapies. MRI was slower (30 minutes) and had lower anisotropic spatial resolution. But MRI eliminates the need for a contrast agent and the contrast-to-noise between tumor and normal parenchyma was higher, making earlier detection of small lesions possible. Both methods supported a relatively high-throughput, longitudinal study of the development of metastatic lesions.

Characterization of statistical prior image constrained compressed sensing (PICCS): II. Application to dose reduction

The ionizing radiation imparted to patients during computed tomography exams is raising concerns. This paper studies the performance of a scheme called dose reduction using prior image constrained compressed sensing (DR-PICCS). The purpose of this study is to characterize the effects of a statistical model of x-ray detection in the DR-PICCS framework and its impact on spatial resolution. Both numerical simulations with known ground truth and in vivo animal dataset were used in this study. In numerical simulations, a phantom was simulated with Poisson noise and with varying levels of eccentricity. Both the conventional filtered backprojection (FBP) and the PICCS algorithms were used to reconstruct images. In PICCS reconstructions, the prior image was generated using two different denoising methods: a simple Gaussian blur and a more advanced diffusion filter. Due to the lack of shift-invariance in nonlinear image reconstruction such as the one studied in this paper, the concept of local spatial resolution was used to study the sharpness of a reconstructed image. Specifically, a directional metric of image sharpness, the so-called pseudopoint spread function (pseudo-PSF), was employed to investigate local spatial resolution. In the numerical studies, the pseudo-PSF was reduced from twice the voxel width in the prior image down to less than 1.1 times the voxel width in DR-PICCS reconstructions when the statistical model was not included. At the same noise level, when statistical weighting was used, the pseudo-PSF width in DR-PICCS reconstructed images varied between 1.5 and 0.75 times the voxel width depending on the direction along which it was measured. However, this anisotropy was largely eliminated when the prior image was generated using diffusion filtering; the pseudo-PSF width was reduced to below one voxel width in that case. In the in vivo study, a fourfold improvement in CNR was achieved while qualitatively maintaining sharpness; images also had a qualitatively more uniform noise spatial distribution when including a statistical model. DR-PICCS enables to reconstruct CT images with lower noise than FBP and the loss of spatial resolution can be mitigated to a large extent. The introduction of statistical modeling in DR-PICCS may improve some noise characteristics, but it also leads to anisotropic spatial resolution properties. A denoising method, such as the directional diffusion filtering, has been demonstrated to reduce anisotropy in spatial resolution effectively when it was combined with DR-PICCS with statistical modeling.

Spatial Resolution Properties of Motion-Compensated Tomographic Image Reconstruction Methods

Many motion-compensated image reconstruction (MCIR) methods have been proposed to correct for subject motion in medical imaging. MCIR methods incorporate motion models to improve image quality by reducing motion artifacts and noise. This paper analyzes the spatial resolution properties of MCIR methods and shows that nonrigid local motion can lead to nonuniform and anisotropic spatial resolution for conventional quadratic regularizers. This undesirable property is akin to the known effects of interactions between heteroscedastic log-likelihoods (e.g., Poisson likelihood) and quadratic regularizers. This effect may lead to quantification errors in small or narrow structures (such as small lesions or rings) of reconstructed images. This paper proposes novel spatial regularization design methods for three different MCIR methods that account for known nonrigid motion. We develop MCIR regularization designs that provide approximately uniform and isotropic spatial resolution and that match a user-specified target spatial resolution. Two-dimensional PET simulations demonstrate the performance and benefits of the proposed spatial regularization design methods.