Algebraic and analytic reconstruction methods for dynamic tomography.

In this work, we discuss algebraic and analytic approaches for dynamic tomography. We present a framework of dynamic tomography for both algebraic and analytic approaches. We finally present numerical experiments

Is abdominal compression useful in lung stereotactic body radiation therapy? A 4DCT and dosimetric lobe-dependent study

International audiencePurpose : To determine the usefulness of abdominal compression in lung stereotactic body radiation therapy (SBRT) depending on lobe tumor location.Materials and methods : Twenty-seven non-small cell lung cancer patients were immobilized in the Stereotactic Body Frame™ (Elekta). Eighteen tumors were located in an upper lobe, one in the middle lobe and nine in a lower lobe (one patient had two lesions). All patients underwent two four-dimensional computed tomography (4DCT) scans, with and without abdominal compression. Three-dimensional tumor motion amplitude was determined using manual landmark annotation. We also determined the internal target volume (ITV) and the influence of abdominal compression on lung dose-volume histograms. Results : The mean reduction of tumor motion amplitude was 3.5 mm (p = 0.009) for lower lobe tumors and 0.8 mm (p = 0.026) for upper/middle lobe locations. Compression increased tumor motion in 5 cases. Mean ITV reduction was 3.6 cm3 (p = 0.039) for lower lobe and 0.2 cm3 (p = 0.048) for upper/middle lobe lesions. Dosimetric gain of the compression for lung sparing was not clinically relevant. Conclusions : The most significant impact of abdominal compression was obtained in patients with lower lobe tumors. However, minor or negative effects of compression were reported for other patients and lung sparing was not substantially improved. At our institute, patients with upper or middle lobe lesions are now systematically treated without compression and the usefulness of compression for lower lobe tumors is evaluated on an individual basis

Material Decomposition in Spectral CT using deep learning: A Sim2Real transfer approach

The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomography is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been proposed for addressing these issues. However, learning algorithms require large amounts of experimental data sets. We propose a deep learning strategy for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach where the knowledge that we learn from synthetic data is transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms are generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specific materials (soft tissue and bone). These volumes are projected into sinogram space in order to simulate photon counting data, taking into account the energy response of the scanner. We compared projection- and image-based decomposition approaches where the network is trained to decompose the materials either in the projection or in the image domain. The proposed Sim2Real transfer strategies are compared to a regularized Gauss-Newton (RGN) method on synthetic data, experimental phantom data and human thorax data

Data Driven Cone Beam CT Motion Management for Radiotherapy Application

The ability to identify respiratory motion is crucial during radiation therapy treatment. In our study we introduced a novel data driven method based on principal component analysis (PCA) to extract a signal related to respiratory motion from cone beam CT projection data. Projection data acquired on cone beam CT devices normally has two motion component information within it, (1) respiratory induced motion and (2) detector rotational induced motion. Our novel approach for extracting a respiratory induced motion signal from projection data was based on computing PCA for different sections of the data set independently, and introducing a technique of combining the extracted signal from each section in a manner to represent the respiratory signal from the entire data set. We tested our method using simulation data set from XCAT software and a real patient data set. The respiratory signal extracted with the XCAT simulation yielded comparable result when compared to the ground truth respiratory signal. Initial results for the real patient data set are encouraging but show need for further refinements

Djelovanje karboksilnih kiselina na crveno obojenje cvjetova žutinice

Eight carboxylic acids were fed at 1-10000 pM to the flower pastes from fresh dyer’s saffron (Carthamus tinctorius L.) capitula and their effects on the red colouration investigated. Glyoxyllic acid, glycolic acid, oxaloacetic acid, 2-oxoglutaric acid and gluconic acid were found to be positive stimulators for the reaction, while succinic acid, malic acid and citric acid inhibited the colour change.Svojim ranijim istraživanjima autori su pokazali da usitnjeni cvjetovi žutinice (Carthamus tinctorius L.) poprime crvenu boju ako na njih djeluju otopine aminokiselina. Učinkovitost pojedine aminokiseline ovisi o tipu: kisele amino- kiseline bile su najučinkovitije, neutralne nešto manje, one s aromatičnim skupinama ili skupinama sa sumporom još manje, dok su bazične aminokiseline bile najmanje učinkovite. Za objašnjenje ovih rezultata bilo je potrebno da se ustanove još drugi metaboliti koji biokatalitički utječu na promjenu boje cvjetova. U ovom prilogu autori objavljuju podatke o promjenama boje nakon primjene karboksilnih kiselina. U tu svrhu istražili su djelovanje osam karboksilnih kiselina u koncentracijama od 1 - 10000 pM na kašu od zdrobljenih cvjetova žutinice (Carthamus tinctorius L.). Postignuti rezultati su pokazali da glioksilna, glikolna, oksalooc- tena, 2-oksoglutarna i glukonska kiselina pospješuju reakciju stvaranja crvenog bojila, dok jantarna, jabučna i limunska kiselina tu reakciju inhibiraju. U radu se navode točni podaci o materijalu, metodama rada i rezultatima koji su prikazani u preglednoj tabeli. Rezultate autori kratko komentiraju uz razmišljanja da bi kemijske strukture karboksilnih kiselina mogle biti uže povezane s procesima promjene bojila, iako je sam mehanizam reakcije zasad još nepoznat

Prediction of image noise contributions in proton computed tomography and comparison to measurements

We present a method to accurately predict image noise in proton computed tomography (pCT) using data generated from a Monte Carlo simulation and a patient or object model that may be generated from a prior x-ray CT image. This enables noise prediction for arbitrary beam fluence settings and, therefore, the application of fluence-modulated pCT (FMpCT), which can achieve prescribed noise targets and may significantly reduce the integral patient dose. We extended an existing Monte Carlo simulation of a prototype pCT scanner to include effects of quenching in the energy detector scintillators and constructed a beam model from experimental tracking data. Simulated noise predictions were compared to experimental data both in the projection domain and in the reconstructed image. Noise prediction agreement between simulated and experimental data in terms of the root-mean-square (RMS) error was better than 7% for a homogeneous water phantom and a sensitometry phantom with tubular inserts. For an anthropomorphic head phantom, modeling the anatomy of a five-year-old child, the RMS error was better than 9% in three evaluated slices. We were able to reproduce subtle noise features near heterogeneities. To demonstrate the feasibility of Monte Carlo simulated noise maps for fluence modulation, we calculated a fluence profile that yields a homogeneous noise level in the image. Unlike for bow-tie filters in x-ray CT this does not require constant fluence at the detector and the shape of the fluence profile is fundamentally different. Using an improved Monte Carlo simulation, we demonstrated the feasibility of using simulated data for accurate image noise prediction for pCT. We believe that the agreement with experimental data is sufficient to enable the future optimization of FMpCT fluence plans to achieve prescribed noise targets in a fluence-modulated acquisition

RLIP76, a Glutathione-Conjugate Transporter, Plays a Major Role in the Pathogenesis of Metabolic Syndrome

PURPOSE: Characteristic hypoglycemia, hypotriglyceridemia, hypocholesterolemia, lower body mass, and fat as well as pronounced insulin-sensitivity of RLIP76⁻/⁻ mice suggested to us the possibility that elevation of RLIP76 in response to stress could itself elicit metabolic syndrome (MSy). Indeed, if it were required for MSy, drugs used to treat MSy should have no effect on RLIP76⁻/⁻ mice. RESEARCH DESIGN AND METHODS: Blood glucose (BG) and lipid measurements were performed in RLIP76⁺/⁺ and RLIP76⁻/⁻ mice, using Ascensia Elite Glucometer® for glucose and ID Labs kits for cholesterol and triglycerides assays. The ultimate effectors of gluconeogenesis are the three enzymes: PEPCK, F-1,6-BPase, and G6Pase, and their expression is regulated by PPARγ and AMPK. The activity of these enzymes was tested by protocols standardized by us. Expressions of RLIP76, PPARα, PPARγ, HMGCR, pJNK, pAkt, and AMPK were performed by Western-blot and tissue staining. RESULTS: The concomitant activation of AMPK and PPARγ by inhibiting transport activity of RLIP76, despite inhibited activity of key glucocorticoid-regulated hepatic gluconeogenic enzymes like PEPCK, G6Pase and F-1,6-BP in RLIP76⁻/⁻ mice, is a salient finding of our studies. The decrease in RLIP76 protein expression by rosiglitazone and metformin is associated with an up-regulation of PPARγ and AMPK. CONCLUSIONS/SIGNIFICANCE: All four drugs, rosiglitazone, metformin, gemfibrozil and atorvastatin failed to affect glucose and lipid metabolism in RLIP76⁻/⁻ mice. Studies confirmed a model in which RLIP76 plays a central role in the pathogenesis of MSy and RLIP76 loss causes profound and global alterations of MSy signaling functions. RLIP76 is a novel target for single-molecule therapeutics for metabolic syndrome

Constraining nucleon effective masses with flow and stopping observables from the Sπ\piRIT experiment

Properties of the nuclear equation of state (EoS) can be probed by measuring the dynamical properties of nucleus-nucleus collisions. In this study, we present the directed flow ($v_1$), elliptic flow ($v_2$) and stopping (VarXZ) measured in fixed target Sn + Sn collisions at 270 AMeV with the S$\pi$RIT Time Projection Chamber. We perform Bayesian analyses in which EoS parameters are varied simultaneously within the Improved Quantum Molecular Dynamics-Skyrme (ImQMD-Sky) transport code to obtain a multivariate correlated constraint. The varied parameters include symmetry energy, $S_0$, and slope of the symmetry energy, $L$, at saturation density, isoscalar effective mass, $m_{s}^*/m_{N}$, isovector effective mass, $m_{v}^{*}/m_{N}$ and the in-medium cross-section enhancement factor $\eta$. We find that the flow and VarXZ observables are sensitive to the splitting of proton and neutron effective masses and the in-medium cross-section. Comparisons of ImQMD-Sky predictions to the S$\pi$RIT data suggest a narrow range of preferred values for $m_{s}^*/m_{N}$, $m_{v}^{*}/m_{N}$ and $\eta$