The Safety of Peripheral Nerve Blocks: The Role of Triple Monitoring in Regional Anaesthesia, a Comprehensive Review.

Regional anaesthesia, referred to as regional blocks, is one of the most frequently used methods of anaesthesia for surgery and for pain management. Local anaesthetic drug should be administered as close to the nerve as possible. If administered too far away, this may result in insufficient block. If it is administrated too close, severe nerve damage can occur. Neurostimulation techniques and ultrasound imaging have improved the effectiveness and safety of blockade, but the risk of nerve injury with permanent nerve disfunction has not been eliminated. Intraneural administration of a local anaesthetic damages the nerve mechanically by the needle and the high pressure generated by the drug inside the nerve. In many studies, injection pressure is described as significantly higher for unintended intraneural injections than for perineural ones. In recent years, the concept of combining techniques (neurostimulation + USG imaging + injection pressure monitoring) has emerged as a method increasing safety and efficiency in regional anaesthesia. This study focuses on the contribution of nerve identification methods to improve the safety of peripheral nerve blocks by reducing the risk of neural damage.

A Fast Convergent Ordered-Subsets Algorithm With Subiteration-Dependent Preconditioners for PET Image Reconstruction.

We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35%-50% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms.

Feasibility of an ultrasound-guided approach to radiofrequency ablation of the superolateral, superomedial and inferomedial genicular nerves: a cadaveric study.

INTRODUCTION: Genicular nerve radiofrequency (RF)denervation appears to be a promising treatment for knee pain in patients with degenerative osteoarthritis of the knee, when candidates are not suitable for arthroplasty. This study aimed to assess the accuracy and reliability of ultrasound-guided placement of RF cannulas in cadavers for genicular nerve treatment, by measuringthe needle-to-nerve proximity. MATERIALS AND METHODS: Five soft-fix human cadavers were included in this study, totaling 10 knees (meanage 93.8 years). Using the ultrasound-guided technique,which we have described previously, RF cannulas were directed toward the superolateral genicular nerve(SLGN), the superomedial genicular nerve (SMGN) and the inferomedial genicular nerve (IMGN). Indocyaninegreen (ICG) dye (0.1 mL) was infiltrated. An anatomical dissection was performed and the distance from the center of the ICG mark to the genicular nerve concerned was measured. RESULTS: The mean distances from the center of the ICG mark to the SLGN, SMGN and IMGN were 2.33 mm(range 0.00-6.05 mm), 3.44 mm (range 0.00-10.59mm) and 1.32 mm (range 0.00-2.99 mm), respectively. There was no statistical difference in distances from the center of the ICG mark to the targeted nerve between the different nerves (p=0.18). CONCLUSION: The results of this study demonstrate that ultrasound-guided treatment of the genicular nerves is feasible. However, for RF ablations, there are some limitations, which mostly can be overcome by using appropriate RF ablation settings.

Sparsity promoting regularization for effective noise suppression in SPECT image reconstruction.

The purpose of this research is to develop an advanced reconstruction method for low-count, hence high-noise, single-photon emission computed tomography (SPECT) image reconstruction. It consists of a novel reconstruction model to suppress noise while conducting reconstruction and an efficient algorithm to solve the model. A novel regularizer is introduced as the nonconvex denoising term based on the approximate sparsity of the image under a geometric tight frame transform domain. The deblurring term is based on the negative log-likelihood of the SPECT data model. To solve the resulting nonconvex optimization problem a preconditioned fixed-point proximity algorithm (PFPA) is introduced. We prove that under appropriate assumptions, PFPA converges to a local solution of the optimization problem at a global O ( 1 / k ) convergence rate. Substantial numerical results for simulation data are presented to demonstrate the superiority of the proposed method in denoising, suppressing artifacts and reconstruction accuracy. We simulate noisy 2D SPECT data from two phantoms: hot Gaussian spheres on random lumpy warm background, and the anthropomorphic brain phantom, at high- and low-noise levels (64k and 90k counts, respectively), and reconstruct them using PFPA. We also perform limited comparative studies with selected competing state-of-the-art total variation (TV) and higher-order TV (HOTV) transform-based methods, and widely used post-filtered maximum-likelihood expectation-maximization. We investigate imaging performance of these methods using: contrast-to-noise ratio (CNR), ensemble variance images (EVI), background ensemble noise (BEN), normalized mean-square error (NMSE), and channelized hotelling observer (CHO) detectability. Each of the competing methods is independently optimized for each metric. We establish that the proposed method outperforms the other approaches in all image quality metrics except NMSE where it is matched by HOTV. The superiority of the proposed method is especially evident in the CHO detectability tests results. We also perform qualitative image evaluation for presence and severity of image artifacts where it also performs better in terms of suppressing 'staircase' artifacts, as compared to TV methods. However, edge artifacts on high-contrast regions persist. We conclude that the proposed method may offer a powerful tool for detection tasks in high-noise SPECT imaging.

Infimal convolution-based regularization for SPECT reconstruction.

PURPOSE: Total variation (TV) regularization is efficient in suppressing noise, but is known to suffer from staircase artifacts. The goal of this work was to develop a regularization method using the infimal convolution of the first- and the second-order derivatives to reduce or even prevent staircase artifacts in the reconstructed images, and to investigate if the advantage in noise suppression by this TV-type regularization can be translated into dose reduction. METHODS: In the present work, we introduce the infimal convolution of the first- and the second-order total variation (ICTV) as the regularization term in penalized maximum likelihood reconstruction. The preconditioned alternating projection algorithm (PAPA), previously developed by the authors of this article, was employed to produce the reconstruction. Using Monte Carlo-simulated data, we evaluate noise properties and lesion detectability in the reconstructed images and compare the results with conventional total variation (TV) and clinical EM-based methods with Gaussian post filter (GPF-EM). We also evaluate the quality of ICTV regularized images obtained for lower photon number data, compared with clinically used photon number, to verify the feasibility of radiation-dose reduction to patients by use of the ICTV reconstruction method. RESULTS: By comparison with GPF-EM reconstructed images, we have found that the ICTV-PAPA method can achieve a lower background variability level while maintaining the same level of contrast. Images reconstructed by the ICTV-PAPA method with 80,000 counts per view exhibit even higher channelized Hotelling observer (CHO) signal-to-noise ratio (SNR), as compared to images reconstructed by the GPF-EM method with 120,000 counts per view. CONCLUSIONS: In contrast to the TV-PAPA method, the ICTV-PAPA reconstruction method avoids substantial staircase artifacts, while producing reconstructed images with higher CHO SNR and comparable local spatial resolution. Simulation studies indicate that a 33% dose reduction is feasible by switching to the ICTV-PAPA method, compared with the GPF-EM clinical standard.

Injection pressure mapping of intraneural vs. perineural injections: further lessons from cadaveric studies.

BACKGROUND: The aim of the study was to investigate the difference between intraneural and perineural injection pressures in human cadavers. Targeted nerves included the cervical roots, the supraclavicular and infraclavicular brachial plexus, the sciatic-subgluteal nerve and the common peroneal and tibial nerves. METHODS: Ten readings were obtained for each nerve location. Over ten seconds, 1 mL of 0.9% NaCl was injected - deliberately slower than in clinical practice to eliminate the risk of aberrant readings relating to the speed of injection. Perineural injections occurred at least 1 mm outside the epineurium. After pressure recordings were completed 0.1mL of dye was injected, and dissection performed to confirm needle placement. Ultrasound and dissection images were matched with light microscopy pictures for all locations. RESULTS: The average pressure for intraneural injections was 24.1±5.7 psi and 6.1±2.1 psi for perinereural. The average injection pressure generated for the cervical trunk, supraclavicular, infraclavicular, sciatic subgluteal, peroneal and tibial nerves respectively were 31.2±6.0 psi, 24±15.0 psi, 23.4±9.5 psi, 22.6±8.8 psi 19.7±6 psi, 17±7.3 psi intraneurally and 6.1±2.0 psi, 9.1±5.5 psi, 10±4.9 psi, 6±2.4 psi, 6±2.4 psi and 7±2.5 psi perineurally. For intraneural injections statistically significant differences were demonstrated between the peroneal and tibial nerves compared to cervical roots/trunks/division/cords of brachial plexus. CONCLUSIONS: The study has consistently demonstrated statistically significant differences between intraneural and perineural injection pressures. It effectively created a "map" of intraneural injection pressures for the most common peripheral nerves blocks and demonstrated a pattern between proximal and distal locations. The study also revealed limitations of either techniques, ultrasound and injection pressure monitoring reinforcing the concept of their simultaneous application.

Preconditioned alternating projection algorithm for solving the penalized-likelihood SPECT reconstruction problem.

PURPOSE: The authors recently developed a preconditioned alternating projection algorithm (PAPA) for solving the penalized-likelihood SPECT reconstruction problem. The proposed algorithm can solve a wide variety of non-differentiable optimization models. This work is dedicated to comparing the performance of PAPA with total variation (TV) regularization (TV-PAPA) and a novel forward-backward algorithm with nested expectation maximization (EM)-TV iteration scheme (FB-EM-TV). METHODS: Monte Carlo technique was used to simulate multiple noise realizations of the fan-beam collimated SPECT data for a piecewise constant phantom with warm background, and hot and cold spheres with uniform activities at two noise levels. They were reconstructed using the aforementioned algorithms with attenuation, scatter, distance-dependent collimator blurring and sensitivity corrections. Noise suppressing performance, lesion detectability, lesion contrast, contrast recovery coefficient, convergence speed and selection of optimal parameters were evaluated. The conventional EM algorithms with TV post-filter (TVPF-EM) and Gaussian post-filter (GPF-EM) were used as benchmarks. RESULTS: The TV-PAPA and FB-EM-TV demonstrated similar performance in all investigated categories. Both algorithms outperformed TVPF-EM in terms of image noise suppression, lesion detectability, lesion contrast and convergence speed. We established that the optimal parameters versus information density approximately followed power laws, which offers a guidance in parameter selection for reconstruction methods. CONCLUSIONS: For the simulated SPECT data, TV-PAPA and FB-EM-TV produced qualitatively and quantitatively similar images. They performed better than the benchmark TVPF-EM and GPF-EM, with only limited loss of lesion contrast.

Relaxed ordered subset preconditioned alternating projection algorithm for PET reconstruction with automated penalty weight selection.

PURPOSE: Performance of the preconditioned alternating projection algorithm (PAPA) using relaxed ordered subsets (ROS) with a non-smooth penalty function was investigated in positron emission tomography (PET). A higher order total variation (HOTV) regularizer was applied and a method for unsupervised selection of penalty weights based on the measured data is introduced. METHODS: A ROS version of PAPA with HOTV penalty (ROS-HOTV-PAPA) for PET image reconstruction was developed and implemented. Two-dimensional PET data were simulated using two synthetic phantoms (geometric and brain) in geometry similar to GE D690/710 PET/CT with uniform attenuation, and realistic scatter (25%) and randoms (25%). Three count levels (high/medium/low) corresponding to mean information densities (ID¯s) of 125, 25, and 5 noise equivalent counts (NEC) per support voxel were reconstructed using ROS-HOTV-PAPA. The patients' brain and whole body PET data were acquired at similar ID¯s on GE D690 PET/CT with time-of-fight and were reconstructed using ROS-HOTV-PAPA and available clinical ordered-subset expectation-maximization (OSEM) algorithms. A power-law model of the penalty weights' dependence on ID¯ was semi-empirically derived. Its parameters were elucidated from the data and used for unsupervised selection of the penalty weights within a reduced search space. The resulting image quality was evaluated qualitatively, including reduction of staircase artifacts, image noise, spatial resolution and contrast, and quantitatively using root mean squared error (RMSE) as a global metric. The convergence rates were also investigated. RESULTS: ROS-HOTV-PAPA converged rapidly, in comparison to non-ROS-HOTV-PAPA, with no evidence of limit cycle behavior. The reconstructed image quality was superior to optimally post-filtered OSEM reconstruction in terms of noise, spatial resolution, and contrast. Staircase artifacts were not observed. Images of the measured phantom reconstructed using ROS-HOTV-PAPA showed reductions in RMSE of 5%-44% as compared with optimized OSEM. The greatest improvement occurred in the lowest count images. Further, ROS-HOTV-PAPA reconstructions produced images with RMSE similar to images reconstructed using optimally post-filtered OSEM but at one-quarter the NEC. CONCLUSION: Acceleration of HOTV-PAPA was achieved using ROS. This was accompanied by an improved RMSE metric and perceptual image quality that were both superior to that obtained with either clinical or optimized OSEM. This may allow up to a four-fold reduction of the radiation dose to the patients in a PET study, as compared with current clinical practice. The proposed unsupervised parameter selection method provided useful estimates of the penalty weights for the selected phantoms' and patients' PET studies. In sum, the outcomes of this research indicate that ROS-HOTV-PAPA is an appropriate candidate for clinical applications and warrants further research.

Initial performance studies of a wearable brain positron emission tomography camera based on autonomous thin-film digital Geiger avalanche photodiode arrays.

Using analytical and Monte Carlo modeling, we explored performance of a lightweight wearable helmet-shaped brain positron emission tomography (PET), or BET camera, based on thin-film digital Geiger avalanche photodiode arrays with Lutetium-yttrium oxyorthosilicate (LYSO) or [Formula: see text] scintillators for imaging in vivo human brain function of freely moving and acting subjects. We investigated a spherical cap BET and cylindrical brain PET (CYL) geometries with 250-mm diameter. We also considered a clinical whole-body (WB) LYSO PET/CT scanner. The simulated energy resolutions were 10.8% (LYSO) and 3.3% ([Formula: see text]), and the coincidence window was set at 2 ns. The brain was simulated as a water sphere of uniform F-18 activity with a radius of 100 mm. We found that BET achieved [Formula: see text] better noise equivalent count (NEC) performance relative to the CYL and [Formula: see text] than WB. For 10-mm-thick [Formula: see text] equivalent mass systems, LYSO (7-mm thick) had [Formula: see text] higher NEC than [Formula: see text]. We found that [Formula: see text] scintillator crystals achieved [Formula: see text] full-width-half-maximum spatial resolution without parallax errors. Additionally, our simulations showed that LYSO generally outperformed [Formula: see text] for NEC unless the timing resolution for [Formula: see text] was considerably smaller than that presently used for LYSO, i.e., well below 300 ps.

Biomodification of PCL Scaffolds with Matrigel, HA, and SR1 Enhances De Novo Ectopic Bone Marrow Formation Induced by rhBMP-2.

The de novo formation of ectopic bone marrow was induced using 1.2-mm-thin polycaprolactone (PCL) scaffolds biomodified with several different biomaterials. In vivo investigations of de novo bone and bone marrow formation indicated that subcutaneous implantation of PCL scaffolds coated with recombinant human bone morphogenetic protein-2 (rhBMP-2) plus Matrigel, hydroxyapatite (HA), or StemRegenin 1 (SR1) improved formation of bone and hematopoietic bone marrow as determined by microcomputed tomography, and histological and hematopoietic characterizations. Our study provides evidence that thin PCL scaffolds biomodified with Matrigel, HA, and SR1 mimic the environments of real bone and bone marrow, thereby enhancing the de novo ectopic bone marrow formation induced by rhBMP-2. This ectopic bone marrow model will serve as a unique and essential tool for basic research and for clinical applications of postnatal tissue engineering and organ regeneration.

Effective noise-suppressed and artifact-reduced reconstruction of SPECT data using a preconditioned alternating projection algorithm.

PURPOSE: The authors have recently developed a preconditioned alternating projection algorithm (PAPA) with total variation (TV) regularizer for solving the penalized-likelihood optimization model for single-photon emission computed tomography (SPECT) reconstruction. This algorithm belongs to a novel class of fixed-point proximity methods. The goal of this work is to investigate how PAPA performs while dealing with realistic noisy SPECT data, to compare its performance with more conventional methods, and to address issues with TV artifacts by proposing a novel form of the algorithm invoking high-order TV regularization, denoted as HOTV-PAPA, which has been explored and studied extensively in the present work. METHODS: Using Monte Carlo methods, the authors simulate noisy SPECT data from two water cylinders; one contains lumpy "warm" background and "hot" lesions of various sizes with Gaussian activity distribution, and the other is a reference cylinder without hot lesions. The authors study the performance of HOTV-PAPA and compare it with PAPA using first-order TV regularization (TV-PAPA), the Panin-Zeng-Gullberg one-step-late method with TV regularization (TV-OSL), and an expectation-maximization algorithm with Gaussian postfilter (GPF-EM). The authors select penalty-weights (hyperparameters) by qualitatively balancing the trade-off between resolution and image noise separately for TV-PAPA and TV-OSL. However, the authors arrived at the same penalty-weight value for both of them. The authors set the first penalty-weight in HOTV-PAPA equal to the optimal penalty-weight found for TV-PAPA. The second penalty-weight needed for HOTV-PAPA is tuned by balancing resolution and the severity of staircase artifacts. The authors adjust the Gaussian postfilter to approximately match the local point spread function of GPF-EM and HOTV-PAPA. The authors examine hot lesion detectability, study local spatial resolution, analyze background noise properties, estimate mean square errors (MSEs), and report the convergence speed and computation time. RESULTS: HOTV-PAPA yields the best signal-to-noise ratio, followed by TV-PAPA and TV-OSL/GPF-EM. The local spatial resolution of HOTV-PAPA is somewhat worse than that of TV-PAPA and TV-OSL. Images reconstructed using HOTV-PAPA have the lowest local noise power spectrum (LNPS) amplitudes, followed by TV-PAPA, TV-OSL, and GPF-EM. The LNPS peak of GPF-EM is shifted toward higher spatial frequencies than those for the three other methods. The PAPA-type methods exhibit much lower ensemble noise, ensemble voxel variance, and image roughness. HOTV-PAPA performs best in these categories. Whereas images reconstructed using both TV-PAPA and TV-OSL are degraded by severe staircase artifacts; HOTV-PAPA substantially reduces such artifacts. It also converges faster than the other three methods and exhibits the lowest overall reconstruction error level, as measured by MSE. CONCLUSIONS: For high-noise simulated SPECT data, HOTV-PAPA outperforms TV-PAPA, GPF-EM, and TV-OSL in terms of hot lesion detectability, noise suppression, MSE, and computational efficiency. Unlike TV-PAPA and TV-OSL, HOTV-PAPA does not create sizable staircase artifacts. Moreover, HOTV-PAPA effectively suppresses noise, with only limited loss of local spatial resolution. Of the four methods, HOTV-PAPA shows the best lesion detectability, thanks to its superior noise suppression. HOTV-PAPA shows promise for clinically useful reconstructions of low-dose SPECT data.

Pressure Monitoring of Intraneural an Perineural Injections Into the Median, Radial, and Ulnar Nerves; Lessons From a Cadaveric Study.

BACKGROUND: Nerve damage after regional anesthesia has been of great concern to anesthetists. Various modalities have been suggested to recognize and prevent its incidence. An understudied area is the measurement of intraneural pressure during peripheral nerve blockade. Previous investigations have produced contradicting results with only one study being conducted on human cadavers. OBJECTIVES: The purpose of this investigation was to systematically record intraneural and perineural injection pressures on the median, ulnar, and radial nerves exclusively as a primary outcome. MATERIALS AND METHODS: Ultrasonography-guided injections of 1 mL of 0.9% NaCl over ten seconds were performed on phenol glycerine embalmed cadaveric median, ulnar, and radial nerves. A total of 60 injections were performed, 30 intraneural and 30 perineural injections. The injections pressure was measured using a controlled disc stimulation device. Anatomic dissection was used to confirm needle placement. RESULTS: Intraneural needle placement produced significantly greater pressures than perineural injections did. The mean generated pressures in median, radial, and ulnar nerves were respectively 29.4 ± 9.3, 27.3 ± 8.5, and 17.9 ± 7.0 pound per square inch (psi) (1 psi = 51.7 mmHg) for the intraneural injections and respectively 7.2 ± 2.5, 8.3 ± 2.5, and 6.7 ± 1.8 psi for perineural injections. Additionally the intraneural injection pressures of the ulnar nerve were lower than those of the median and radial nerves. CONCLUSIONS: Obtained results demonstrate significant differences between intraneural and perineural injection pressures in the median, ulnar, and radial nerves. Intraneural injection pressures show low specificity but high sensitivity suggesting that pressure monitoring might be a valuable tool in improving the safety and efficacy of peripheral nerve blockade in regional anesthesia. Peripheral nerves "pressure mapping" hypothetically might show difference amongst various nerves depending on anatomic location, histologic structure, and ultrasonographic appearance.

Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology.

OBJECTIVE: While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. METHODS AND MATERIALS: We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. RESULTS: Of the feature vectors investigated, the best performance was observed with Minkowski functional 'perimeter' while comparable performance was observed with 'area'. Of the dimension reduction algorithms tested with 'perimeter', the best performance was observed with Sammon's mapping (0.84±0.10) while comparable performance was achieved with exploratory observation machine (0.82±0.09) and principal component analysis (0.80±0.10). CONCLUSIONS: The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction.

Classification of small lesions in dynamic breast MRI: Eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement over time.

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they don't exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of sixty annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were also used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter, thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented (p < 0.05). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.

Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection.

Dynamic texture quantification, i.e., extracting texture features from the lesion enhancement pattern in all available post-contrast images, has not been evaluated in terms of its ability to classify small lesions. This study investigates the classification performance achieved with texture features extracted from all five post-contrast images of lesions (mean lesion diameter of 1.1 cm) annotated in dynamic breast magnetic resonance imaging exams. Sixty lesions are characterized dynamically using Haralick texture features. The texture features are then used in a classification task with support vector regression and a fuzzy k-nearest neighbor classifier; free parameters of these classifiers are optimized using random sub-sampling cross-validation. Classifier performance is determined through receiver-operator characteristic (ROC) analysis, specifically through computation of the area under the ROC curve (AUC). Mutual information is used to evaluate the contribution of texture features extracted from different post-contrast stages to classifier performance. Significant improvements (p < 0.05) are observed for six of the thirteen texture features when the lesion enhancement pattern is quantified using the proposed approach of dynamic texture quantification. The highest AUC value observed (0.82) is achieved with texture features responsible for capturing aspects of lesion heterogeneity. Mutual information analysis reveals that texture features extracted from the third and fourth post-contrast images contributed most to the observed improvement in classifier performance. These results show that the performance of automated character classification with small lesions can be significantly improved through dynamic texture quantification of the lesion enhancement pattern.

Preconditioned Alternating Projection Algorithms for Maximum a Posteriori ECT Reconstruction.

We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constrain involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the preconditioned alternating projection algorithm. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality.

A K-alpha x-ray source using high energy and high repetition rate laser system for phase contrast imaging.

K-alpha x-ray sources from laser produced plasmas provide completely new possibilities for x-ray phase-contrast imaging applications. By tightly focusing intense femtosecond laser pulses onto a solid target K-alpha x-ray pulses are generated through the interaction of energetic electrons created in the plasma with the bulk target. In this paper, we present a continuous and efficient Mo K-alpha x-ray source produced by a femtosecond laser system operating at 100 Hz repetition rate with maximum pulse energy of 110 mJ before compression. The source has an x-ray conversion efficiency of greater than 10(-5) into K-alpha line emission. In preparation for phase contrast imaging applications, the size of the resultant K-alpha x-ray emission spot has been also characterized. The source exhibits sufficient spatial coherence to observe phase contrast. We observe a relatively small broadening of the K-alpha source size compared to the size of the laser beam itself. Detailed characterization of the source including the x-ray spectrum and the x-ray average yield along with phase contrast images of test objects will be presented.

Development of ultrafast laser-based x-ray in-vivo phase-contrast micro-CT beamline for biomedical applications at Advanced Laser Light Source (ALLS).

We are developing and exploring the imaging performance of, an in vivo, in-line holography, x-ray phase-contrast, micro-CT system with an ultrafast laser-based x-ray (ULX) source. By testing and refining our system, and by performing computer simulations, we plan to improve system performance in terms of contrast resolution and multi-energy imaging to a level beyond what can be obtained using a conventional microfocal x-ray tube. Initial CT projection sets at single energy (Mo K(alpha) and K(beta) lines) were acquired in the Fresnel regime and reconstructed for phantoms and a euthanized mouse. We also performed computer simulations of phase-contrast micro-CT scans for low-contrast, soft-tissue, tumor imaging. We determined that, in order to perform a phase-contrast, complete micro-CT scan using ULX, the following conditions must be met: (i) the x-ray source needs to be stable during the scan; (ii) the laser focal spot size needs to be less than 10 mum for source-to-object distance greater than 30 cm; (iii) the laser light intensity on the target needs to be in the range of 5 x 10(17) to 5 x 10(19) W/cm(2); (iv) the ablation protection system needs to allow uninterrupted scans; (v) the laser light focusing on the target needs to remain accurate during the entire scan; (vi) a fresh surface of the target must be exposed to consecutive laser shots during the entire scan; (vii) the effective detector element size must be less than 12 mum. Based on the results obtained in this research project, we anticipate that the new 10 Hz, 200 TW laser with 50 W average power that is being commissioned at ALLS will allow us practical implementation of in vivo x-ray phase-contrast micro-CT.

Soft tissue small avascular tumor imaging with x-ray phase-contrast micro-CT in-line holography.

To assess the feasibility of small soft tissue avascular tumor micro-CT imaging with x-ray phase-contrast in-line holography, we have studied micro-CT imaging with in-line geometry of small spheroidal avascular tumor models with quiescent cell core (< 250 mum) and various distributions of the proliferating cell density (PCD) forming the outer shell. We have simulated imaging with an ultrafast laser-based x-ray source with a Mo target. We observe phase-contrast enhancement of the tumor boundaries in the reconstructed transaxial images, resulting in improved detection of small soft tissue tumors, providing that the PCD density gradient is sufficiently large.