Multi-Stage Adaptive Spline Autofocus (MASA) with a Learned Metric for Deformable Motion Compensation in Interventional Cone-Beam CT.

Purpose: Cone-beam CT (CBCT) is widespread in abdominal interventional imaging, but its long acquisition time makes it susceptible to patient motion. Image-based autofocus has shown success in CBCT deformable motion compensation, via deep autofocus metrics and multi-region optimization, but it is challenged by the large parameter dimensionality required to capture intricate motion trajectories. This work leverages the differentiable nature of deep autofocus metrics to build a novel optimization strategy, Multi-Stage Adaptive Spine Autofocus (MASA), for compensation of complex deformable motion in abdominal CBCT. Methods: MASA poses the autofocus problem as a multi-stage adaptive sampling strategy of the motion trajectory, sampled with Hermite spline basis with variable amplitude and knot temporal positioning. The adaptive method permits simultaneous optimization of the sampling phase, local temporal sampling density, and time-dependent amplitude of the motion trajectory. The optimization is performed in a multi-stage schedule with increasing number of knots that progressively accommodates complex trajectories in late stages, preconditioned by coarser components from early stages, and with minimal increase in dimensionality. MASA was evaluated in controlled simulation experiments with two types of motion trajectories: i) combinations of slow drifts with sudden jerk (sigmoid) motion; and ii) combinations of periodic motion sources of varying frequency into multi-frequency trajectories. Further validation was obtained in clinical data from liver CBCT featuring motion of contrast-enhanced vessels, and soft-tissue structures. Results: The adaptive sampling strategy provided successful motion compensation in sigmoid trajectories, compared to fixed sampling strategies (mean SSIM increase of 0.026 compared to 0.011). Inspection of the estimated motion showed the capability of MASA to automatically allocate larger sampling density to parts of the scan timeline featuring sudden motion, effectively accommodating complex motion without increasing the problem dimension. Experiments on multi-frequency trajectories with 3-stage MASA (5, 10, and 15 knots) yielded a twofold SSIM increase compared to single-stage autofocus with 15 knots (0.076 vs 0.040, respectively). Application of MASA to clinical datasets resulted in simultaneous improvement on the delineation of both contrast-enhanced vessels and soft-tissue structures in the liver. Conclusion: A new autofocus framework, MASA, was developed including a novel multi-stage technique for adaptive temporal sampling of the motion trajectory in combination with fully differentiable deep autofocus metrics. This novel adaptive sampling approach is a crucial step for application of deformable motion compensation to complex temporal motion trajectories.

Simulation of Random Deformable Motion in Soft-Tissue Cone-Beam CT with Learned Models.

Cone-beam CT (CBCT) is widely used for guidance in interventional radiology but it is susceptible to motion artifacts. Motion in interventional CBCT features a complex combination of diverse sources including quasi-periodic, consistent motion patterns such as respiratory motion, and aperiodic, quasi-random, motion such as peristalsis. Recent developments in image-based motion compensation methods include approaches that combine autofocus techniques with deep learning models for extraction of image features pertinent to CBCT motion. Training of such deep autofocus models requires the generation of large amounts of realistic, motion-corrupted CBCT. Previous works on motion simulation were mostly focused on quasi-periodic motion patterns, and reliable simulation of complex combined motion with quasi-random components remains an unaddressed challenge. This work presents a framework aimed at synthesis of realistic motion trajectories for simulation of deformable motion in soft-tissue CBCT. The approach leveraged the capability of conditional generative adversarial network (GAN) models to learn the complex underlying motion present in unlabeled, motion-corrupted, CBCT volumes. The approach is designed for training with unpaired clinical CBCT in an unsupervised fashion. This work presents a first feasibility study, in which the model was trained with simulated data featuring known motion, providing a controlled scenario for validation of the proposed approach prior to extension to clinical data. Our proof-of-concept study illustrated the potential of the model to generate realistic, variable simulation of CBCT deformable motion fields, consistent with three trends underlying the designed training data: i) the synthetic motion induced only diffeomorphic deformations - with Jacobian Determinant larger than zero; ii) the synthetic motion showed median displacement of 0. 5 mm in regions predominantly static in the training (e.g., the posterior aspect of the patient laying supine), compared to a median displacement of 3.8 mm in regions more prone to motion in the training; and iii) the synthetic motion exhibited predominant directionality consistent with the training set, resulting in larger motion in the superior-inferior direction (median and maximum amplitude of 4.58 mm and 20 mm, > 2x larger than the two remaining direction). Together, the proposed framework shows the feasibility for realistic motion simulation and synthesis of variable CBCT data.

Context-Aware, Reference-Free Local Motion Metric for CBCT Deformable Motion Compensation.

Deformable motion is one of the main challenges to image quality in interventional cone beam CT (CBCT). Autofocus methods have been successfully applied for deformable motion compensation in CBCT, using multi-region joint optimization approaches that leverage the moderately smooth spatial variation motion of the deformable motion field with a local neighborhood. However, conventional autofocus metrics enforce images featuring sharp image-appearance, but do not guarantee the preservation of anatomical structures. Our previous work (DL-VIF) showed that deep convolutional neural networks (CNNs) can reproduce metrics of structural similarity (visual information fidelity - VIF), removing the need for a matched motion-free reference, and providing quantification of motion degradation and structural integrity. Application of DL-VIF within local neighborhoods is challenged by the large variability of local image content across a CBCT volume, and requires global context information for successful evaluation of motion effects. In this work, we propose a novel deep autofocus metric, based on a context-aware, multi-resolution, deep CNN design. In addition to the inclusion of contextual information, the resulting metric generates a voxel-wise distribution of reference-free VIF values. The new metric, denoted CADL-VIF, was trained on simulated CBCT abdomen scans with deformable motion at random locations and with amplitude up to 30 mm. The CADL-VIF achieved good correlation with the ground truth VIF map across all test cases with R2 = 0.843 and slope = 0.941. When integrated into a multi-ROI deformable motion compensation method, CADL-VIF consistently reduced motion artifacts, yielding an average increase in SSIM of 0.129 in regions with severe motion and 0.113 in regions with mild motion. This work demonstrated the capability of CADL-VIF to recognize anatomical structures and penalize unrealistic images, which is a key step in developing reliable autofocus for complex deformable motion compensation in CBCT.

Targeted Deformable Motion Compensation for Vascular Interventional Cone-Beam CT Imaging.

Purpose: Cone-beam CT has become commonplace for 3D guidance in interventional radiology (IR), especially for vascular procedures in which identification of small vascular structures is crucial. However, its long image acquisition time poses a limit to image quality due to soft-tissue deformable motion that hampers visibility of small vessels. Autofocus motion compensation has shown promising potential for soft-tissue deformable motion compensation, but it lacks specific target to the imaging task. This work presents an approach for deformable motion compensation targeted at imaging of vascular structures. Methods: The proposed method consists on a two-stage framework for: i) identification of contrast-enhanced blood vessels in 2D projection data and delineation of an approximate region covering the vascular target in the volume space, and, ii) a novel autofocus approach including a metric designed to promote the presence of vascular structures acting solely in the region of interest. The vesselness of the image is quantified via evaluation of the properties of the 3D image Hessian, yielding a vesselness filter that gives larger values to voxels candidate to be part of a tubular structure. A cost metric is designed to promote large vesselness values and spatial sparsity, as expected in regions of fine vascularity. A targeted autofocus method was designed by combining the presented metric with a conventional autofocus term acting outside of the region of interest. The resulting method was evaluated on simulated data including synthetic vascularity merged with real anatomical features obtained from MDCT data. Further evaluation was obtained in two clinical datasets obtained during TACE procedures with a robotic C-arm (Artis Zeego, Siemens Healthineers). Results: The targeted vascular autofocus effectively restored the shape and contrast of the contrast-enhanced vascularity in the simulation cases, resulting in improved visibility and reduced artifacts. Segmentations performed with a single threshold value on the target vascular regions yielded a net increase of up to 42% in DICE coefficient computed against the static reference. Motion compensation in clinical datasets resulted in improved visibility of vascular structures, observed in maximum intensity projections of the contrast-enhanced liver vessel tree. Conclusion: Targeted motion compensation for vascular imaging showed promising performance for increased identification of small vascular structures in presence of motion. The development of autofocus metrics and methods tailored to vascular imaging opens the way for reliable compensation of deformable motion while preserving the integrity of anatomical structures in the image.

Reference-free learning-based similarity metric for motion compensation in cone-beam CT.

Purpose. Patient motion artifacts present a prevalent challenge to image quality in interventional cone-beam CT (CBCT). We propose a novel reference-free similarity metric (DL-VIF) that leverages the capability of deep convolutional neural networks (CNN) to learn features associated with motion artifacts within realistic anatomical features. DL-VIF aims to address shortcomings of conventional metrics of motion-induced image quality degradation that favor characteristics associated with motion-free images, such as sharpness or piecewise constancy, but lack any awareness of the underlying anatomy, potentially promoting images depicting unrealistic image content. DL-VIF was integrated in an autofocus motion compensation framework to test its performance for motion estimation in interventional CBCT.Methods. DL-VIF is a reference-free surrogate for the previously reported visual image fidelity (VIF) metric, computed against a motion-free reference, generated using a CNN trained using simulated motion-corrupted and motion-free CBCT data. Relatively shallow (2-ResBlock) and deep (3-Resblock) CNN architectures were trained and tested to assess sensitivity to motion artifacts and generalizability to unseen anatomy and motion patterns. DL-VIF was integrated into an autofocus framework for rigid motion compensation in head/brain CBCT and assessed in simulation and cadaver studies in comparison to a conventional gradient entropy metric.Results. The 2-ResBlock architecture better reflected motion severity and extrapolated to unseen data, whereas 3-ResBlock was found more susceptible to overfitting, limiting its generalizability to unseen scenarios. DL-VIF outperformed gradient entropy in simulation studies yielding average multi-resolution structural similarity index (SSIM) improvement over uncompensated image of 0.068 and 0.034, respectively, referenced to motion-free images. DL-VIF was also more robust in motion compensation, evidenced by reduced variance in SSIM for various motion patterns (σDL-VIF = 0.008 versusσgradient entropy = 0.019). Similarly, in cadaver studies, DL-VIF demonstrated superior motion compensation compared to gradient entropy (an average SSIM improvement of 0.043 (5%) versus little improvement and even degradation in SSIM, respectively) and visually improved image quality even in severely motion-corrupted images.Conclusion: The studies demonstrated the feasibility of building reference-free similarity metrics for quantification of motion-induced image quality degradation and distortion of anatomical structures in CBCT. DL-VIF provides a reliable surrogate for motion severity, penalizes unrealistic distortions, and presents a valuable new objective function for autofocus motion compensation in CBCT.

Deformable motion compensation for interventional cone-beam CT.

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5-30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%-92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5-5 mm-2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).

Accelerated 3D image reconstruction with a morphological pyramid and noise-power convergence criterion.

Model-based iterative reconstruction (MBIR) for cone-beam CT (CBCT) offers better noise-resolution tradeoff and image quality than analytical methods for acquisition protocols with low x-ray dose or limited data, but with increased computational burden that poses a drawback to routine application in clinical scenarios. This work develops a comprehensive framework for acceleration of MBIR in the form of penalized weighted least squares optimized with ordered subsets separable quadratic surrogates. The optimization was scheduled on a set of stages forming a morphological pyramid varying in voxel size. Transition between stages was controlled with a convergence criterion based on the deviation between the mid-band noise power spectrum (NPS) measured on a homogeneous region of the evolving reconstruction and that expected for the converged image, computed with an analytical model that used projection data and the reconstruction parameters. A stochastic backprojector was developed by introducing a random perturbation to the sampling position of each voxel for each ray in the reconstruction within a voxel-based backprojector, breaking the deterministic pattern of sampling artifacts when combined with an unmatched Siddon forward projector. This fast, forward and backprojector pair were included into a multi-resolution reconstruction strategy to provide support for objects partially outside of the field of view. Acceleration from ordered subsets was combined with momentum accumulation stabilized with an adaptive technique that automatically resets the accumulated momentum when it diverges noticeably from the current iteration update. The framework was evaluated with CBCT data of a realistic abdomen phantom acquired on an imaging x-ray bench and with clinical CBCT data from an angiography robotic C-arm (Artis Zeego, Siemens Healthineers, Forchheim, Germany) acquired during a liver embolization procedure. Image fidelity was assessed with the structural similarity index (SSIM) computed with a converged reconstruction. The accelerated framework provided accurate reconstructions in 60 s (SSIM = 0.97) and as little as 27 s (SSIM = 0.94) for soft-tissue evaluation. The use of simple forward and backprojectors resulted in 9.3× acceleration. Accumulation of momentum provided extra ∼3.5× acceleration with stable convergence for 6-30 subsets. The NPS-driven morphological pyramid resulted in initial faster convergence, achieving similar SSIM with 1.5× lower runtime than the single-stage optimization. Acceleration of MBIR to provide reconstruction time compatible with clinical applications is feasible via architectures that integrate algorithmic acceleration with approaches to provide stable convergence, and optimization schedules that maximize convergence speed.

Surgineering: curriculum concept for experiential learning in upper-level biomedical engineering.

PURPOSE: A strong foundation in the fundamental principles of medical intervention combined with genuine exposure to real clinical systems and procedures will improve engineering students' capability for informed innovation on clinical problems. To help build such a foundation, a new course (dubbed Surgineering) was developed to convey fundamental principles of surgery, interventional radiology (IR), and radiation therapy, with an emphasis on experiential learning, hands-on with real clinical systems, exposure to clinicians, and visits to real operating theaters. The concept, structure, and outcomes of the course of the first run of the first semester of the course are described. METHOD: The course included six segments spanning fundamental concepts and cutting-edge approaches in a spectrum of surgical specialties, body and neurological IR, and radiation therapy. Each class involved a minimum of didactic content and an emphasis on hands-on experience with instrumentation, equipment, surgical approaches, anatomical models, dissection, and visits to clinical theaters. Outcomes on the quality of the course and areas for continuing improvement were assessed by student surveys (5-point Likert scores and word-cloud representations of free response) as well as feedback from clinical collaborators. RESULT: Surveys assessed four key areas of feedback on the course and were analyzed quantitatively and in word-cloud representations of: (1) best aspects (hands-on experience with surgeons); (2) worst aspects (quizzes and reading materials); (3) areas for improvement (projects, quizzes, and background on anatomy); and (4) what prospective students should know (a lot background reading for every class). Five-point Likert scores from survey respondents (16/19 students) indicated: overall quality of the course 4.63 ± 0.72 (median 5.00); instructor teaching effectiveness 4.06 ± 1.06 (median 4.00); intellectual challenge 4.19 ± 0.40 (median 4.00); and workload somewhat heavier (62.5%) compared to other courses. Novel elements of the course included the opportunity to engage with clinical faculty and participate in realistic laboratory exercises, work with clinical instruments and equipment, and visit real operating theaters. An additional measure of the success of the course was evidenced by surveys and a strong escalation in enrollment in the following year. CONCLUSIONS: The Surgineering course presents an important addition to upper-level engineering curricula and a valuable opportunity for engineering students to gain hands-on experience and interaction with clinical experts. Close partnership with clinical faculty was essential to the schedule and logistics of the course as well as to the continuity of concepts delivered over the semester. The knowledge and experience gained provides stronger foundation for identification of un-met clinical needs and ideation of new engineering approaches in medicine. The course also provides a valuable prerequisite to higher-level coursework in systems engineering, human factors, and data science applied to medicine.

Convergence criterion for MBIR based on the local noise-power spectrum: Theory and implementation in a framework for accelerated 3D image reconstruction with a morphological pyramid.

Model-based iterative reconstruction (MBIR) offers improved noise-resolution tradeoffs and artifact reduction in cone-beam CT compared to analytical reconstruction, but carries increased computational burden. An important consideration in minimizing computation time is reliable selection of the stopping criterion to perform the minimum number of iterations required to obtain the desired image quality. Most MBIR methods rely on a fixed number of iterations or relative metrics on image or cost-function evolution, and it would be desirable to use metrics that are more representative of the underlying image properties. A second front for reduction of computation time is the use of acceleration techniques (e.g. subsets or momentum). However, most of these techniques do not strictly guarantee convergence of the resulting MBIR method. A data-dependent analytical model of noise-power spectrum (NPS) for penalized weighted least squares (PWLS) reconstruction is proposed as an absolute metric of image properties for the fully converged volume. Distance to convergence is estimated as the root mean squared error (RMSE) between the estimated NPS and an NPS measured on a uniform region of interest (ROI) in the evolving volume. Iterations are stopped when the RMSE falls below a threshold directly related with the properties of the target image. Further acceleration was achieved by combining the spectral stopping criterion with a morphological pyramid (mPyr) in which the minimization of the PWLS cost-function is divided in a cascade of stages. The algorithm parameters (voxel size in this work) change between stages to achieve faster evolution in early stages, and a final stage with the target parameters to guarantee convergence. Transition between stages is governed by the spectral stopping criterion. The approach was evaluated on simulated CBCT data of a realistic digital abdomen phantom. Accuracy of the NPS model and evolution with time of the distance from the measured NPS was assessed in two ROIs. Performance of the spectrally-driven mPyr architecture was compared to a conventional, single stage, PWLS, and to two mPyr designs running a fixed number of iterations. The spectrally-driven mPyr achieved faster convergence, with 40% lower RMSE than the single stage PWLS, and between 10% and 20% RMSE reduction compared to other mPyr designs. The proposed spectral stopping criterion proved to be a suitable choice for a stopping rule, and, in particular, to govern mPyr stage transition.

Time-resolved contrast-enhanced MRA (TWIST) with gadofosveset trisodium in the classification of soft-tissue vascular anomalies in the head and neck in children following updated 2014 ISSVA classification: first report on systematic evaluation of MRI and TWIST in a cohort of 47 children.

AIM: To evaluate the relative accuracy of contrast-enhanced time-resolved angiography with interleaved stochastic trajectories versus conventional contrast-enhanced magnetic resonance imaging (MRI) following International Society for the Study of Vascular Anomalies updated 2014-based classification of soft-tissue vascular anomalies in the head and neck in children. MATERIALS AND METHODS: Time-resolved angiography with interleaved stochastic trajectories versus conventional contrast-enhanced MRI of children with diagnosis of soft-tissue vascular anomalies in the head and neck referred for MRI between 2008 and 2014 were retrospectively reviewed. Forty-seven children (0-18 years) were evaluated. Two paediatric neuroradiologists evaluated time-resolved MRA and conventional MRI in two different sessions (30 days apart). Blood-pool endovascular MRI contrast agent gadofosveset trisodium was used. RESULTS: The present cohort had the following diagnoses: infantile haemangioma (n=6), venous malformation (VM; n=23), lymphatic malformation (LM; n=16), arteriovenous malformation (AVM; n=2). Time-resolved MRA alone accurately classified 38/47 (81%) and conventional MRI 42/47 (89%), respectively. Although time-resolved MRA alone is slightly superior to conventional MRI alone for diagnosis of infantile haemangioma, conventional MRI is slightly better for diagnosis of venous and LMs. Neither time-resolved MRA nor conventional MRI was sufficient for accurate diagnosis of AVM in this cohort. Conventional MRI combined with time-resolved MRA accurately classified 44/47 cases (94%). CONCLUSION: Time-resolved MRA using gadofosveset trisodium can accurately classify soft-tissue vascular anomalies in the head and neck in children. The addition of time-resolved MRA to existing conventional MRI protocols provides haemodynamic information, assisting the diagnosis of vascular anomalies in the paediatric population at one-third of the dose of other MRI contrast agents.

Identifying the impact of inflammatory breast cancer on survival: a retrospective multi-center cohort study.

PURPOSE: Inflammatory breast cancer (IBC) represents a rare and aggressive form of cancer with negative prognosis and high rate of recurrence. The purpose of this retrospective multi-center study was to evaluate the effect of IBC on overall and disease-free survival. Furthermore we analyzed the influence of hormone and Her2 receptor expression on inflammatory breast cancer cells on the clinical outcome of patients. METHODS: This retrospective German multi-center study included 11,780 patients with primary breast cancer recruited from 1992 to 2008. In this sub-group analysis we focused on 70 patients with IBC. RESULTS: Despite the relatively small sample size, we could confirm the aggressiveness of inflammatory breast cancer and the different clinical behavior of IBC subtypes. It could be demonstrated that the lack of expression of hormone receptors on tumor cells is associated with a more aggressive clinical course and decreased overall and disease-free survival. Higher incidence of Her2 overexpression, that is typically associated with poor prognostic outcome among women with non-IBC tumors, seems however to have no prognostic significance. CONCLUSIONS: This BRENDA sub-group analysis, on a German cohort of breast cancer patients confirmed the negative outcome of IBC and the different clinical behavior of IBC subtypes. The best management of IBC requires intensive coordination and cooperation between various clinical disciplines involved in the treatment of IBC patients. Moreover there is a need to identify IBC-specific targeted therapies to improve the curing prospects of this subtype of cancer.

Targeting IL-23 by employing a p40 peptide-based vaccine ameliorates murine allergic skin and airway inflammation.

BACKGROUND: Studies have found that the IL-23/Th17 pathway plays an important role in the pathogenesis of atopic dermatitis (AD) and severe and steroid-resistant asthma. Targeting IL-23/Th17 pathway with monoclonal antibodies (mAb) has been successful in the reduction of skin and airway inflammation in animal models. However, the mAb has a short half-life, requiring repeated administrations. For the long-term suppression of IL-23/Th17 pathway, we have previously developed an IL-23p40 peptide-based virus-like particle vaccine, which induces long-lasting autoantibodies to IL-23. OBJECTIVE: We sought to evaluate the effects of this IL-23p40 peptide-based vaccine on the down-regulation of allergic skin and airway inflammation in mice. METHODS: Mice were subcutaneously injected three times with the IL-23p40 vaccine, or the vaccine carrier protein or saline as controls. Two weeks later, mice were epicutaneously sensitized with ovalbumin four times at a 2-week interval. One week after the final sensitization, mice were nasally administrated with ovalbumin daily for 3 days. One day later, bronchoalveolar lavage fluids (BALF), sera, lung and skin tissues were obtained and analysed. RESULTS: Mice immunized with the vaccine produced high levels of IgG antibodies to IL-23, p40 and IL-12 that in vitro inhibited IL-23-dependent IL-17 production. The numbers of total cells, neutrophils, and eosinophils in BALF were significantly reduced in the vaccine group, compared with controls. The levels of IL-13, IL-5, IL-23 and, IL-17 in BALF and levels of serum ovalbumin-specific IgE, IgG1, and total IgE were also significantly decreased. Histological analysis showed less inflammation of the lung and skin tissues in the vaccine group, compared with controls. CONCLUSION AND CLINICAL RELEVANCE: Administration of an IL-23p40 peptide-based vaccine down-regulates allergic skin and airway inflammation, suggesting that this strategy may be a potential therapeutic approach in the treatment of AD and asthma.

Arterial wall MRI characteristics are associated with elevated serum markers of inflammation in humans.

Inflammation contributes to atherosclerosis, but assessment in humans is largely restricted to measurement of markers in blood. We determined whether MRI properties of large arteries were associated with markers of inflammation in serum. Double inversion recovery, fast spin-echo images of the common carotid arteries and infrarenal aorta were obtained at 1.5 T both before and after gadolinium-DTPA (0.1 mmol/kg) in 52 subjects > or =40 years of age, 17 of whom had no risk factors for atherosclerosis and thus served as controls. Twenty-two study participants had increases in wall thickness (14), T2-weighted signal intensity (11), and/or contrast enhancement values (7) that were >2 standard deviations (SDs) from control group mean values. Ten subjects in this group had evidence of focal plaques in the carotids (5) and/or aorta (6). Compared with the remaining 30 subjects, these 22 had significantly higher levels of interleukin-6 (3.53 +/- 2.46 vs. 1.97 +/- 1.37 pg/mL, P = 0.004), C-reactive protein (0.56 +/- 0.98 vs. 0.30 +/- 0.52 mg/dL, P = 0.019), vascular cell adhesion molecule-1 (572 +/- 153 vs. 471 +/- 130 ng/mL, P = 0.012), and intercellular adhesion molecule-1 (244 +/- 80 vs. 202 +/- 45 ng/mL, P = 0.015), and nonsignificant differences in levels of E-selectin (46.1 +/- 18.9 vs. 42.3 +/- 11.3 ng/mL, P = 0.369). Thus, MRI characteristics of the aorta and carotid arteries were associated with elevated serum markers of inflammation, frequently in the absence of definite atheroma. MRI of large arteries may provide a new approach to investigate the contribution of inflammation to atherogenesis.

Activity of extracts and isolated naphthoquinones from Kigelia pinnata against Plasmodium falciparum.

Four naphthoquinoids from Kigelia pinnata rootbark were assessed in vitro against chloroquine-sensitive (T9-96) and -resistant (K1) Plasmodium falciparum strains and for cytotoxicity using KB cells. 2-(1-Hydroxyethyl)naphtho[2,3-b]furan-4,9-dione possessed good activity against both strains [IC(50) values 627 nM (K1), 718 nM (T9-96)]. Isopinnatal, kigelinol, and isokigelinol exhibited lower activity against both strains.

Remembrance of repasts past: culinary reminiscence.

If food acquisition, preparation and eating over a lifetime have personal and collective meaning, can the memory of these events contribute to residents' well being? This innovative program shows you how.

Women, subjective depression, and water exercise.

Nearly one fourth (N = 21) of the women participants of a water exercise program (N = 88) reported feeling depressed at the time of enrollment. All the depressed women reported improvement after 8 weeks or more in the program. The subjectively depressed participants differed significantly from other class members in physical problems, lifestyle habits, and emotional concerns they hoped to address and in motivation, perceived helpful conversation with peers, and learning from the classes. Subjective symptoms are discussed in light of current information on depressive disorders. The findings contributed to the growing body of research linking exercise with the treatment and prevention of depression. Recommendations for the conduct of similar exercise programs that will be responsive to the needs of subjectively depressed women are offered.