A Simple Numerical Tool for Dynamic Soil-Structure Interaction Analyses Including Non-Linear Behaviour of Both Structure and Foundation

In this paper a simple model to take into account dynamic non-linear soil-structure interaction is presented: it consists of a 1 degree-of-freedom (dof) superstructure and a 3 dof macro-element foundation. Both the superstructure and the soil-foundation system exhibit a non-linear behaviour. In particular the superstructure is characterized by an elastic perfectly plastic behaviour, while the foundation macro-element encompasses the two sources of non-linearity that arise in the soil-foundation interface: a) the one due to the irreversible elastoplastic soil behaviour (material non-linearity) and b) the one due to possible foundation uplift (geometric non-linearity). The global model thus entails the following features: a) the coupling between the foundation and the superstructure when one or both of them enter into the non-linear range, b) the capability for the foundation and the superstructure to dissipate energy, c) a prediction of peak and residual displacements in both the superstructure and the foundation, d) the possibility to model the isolation effects for the structure due to the foundation non-linear behaviour and e) the possibility for the superstructure to reach a particular level of ductility demand. Therefore, the model can serve as a numerical tool for assessing performance-based design approaches that wish to take into account non-linear soil-structure interaction. This is illustrated through several case studies of bridge piers, in which a comparison between the results obtained by dynamic analyses performed with different base conditions (fixed base, elastic base, elastoplastic base with uplift) emphasizes the role of the non-linear soil-structure interaction in design

Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI

We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss of information. We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary. PROSUB uses the paradigm of recursive feature elimination (RFE) and progressively subsamples measurements during deep learning training, improving optimization stability. PROSUB also integrates a neural architecture search (NAS) paradigm, allowing the network architecture hyperparameters to respond to the subsampling process. We show PROSUB outperforms the winner of the MUDI MICCAI challenge, producing large improvements >18% MSE on the MUDI challenge sub-tasks and qualitative improvements on downstream processes useful for clinical applications. We also show the benefits of incorporating NAS and analyze the effect of PROSUB's components. As our method generalizes to other problems beyond MRI measurement selection-reconstruction, our code is https://github.com/sbb-gh/PROSU

Planck LFI flight model feed horns

this paper is part of the Prelaunch status LFI papers published on JINST: http://www.iop.org/EJ/journal/-page=extra.proc5/jinst The Low Frequency Instrument is optically interfaced with the ESA Planck telescope through 11 corrugated feed horns each connected to the Radiometer Chain Assembly (RCA). This paper describes the design, the manufacturing and the testing of the flight model feed horns. They have been designed to optimize the LFI optical interfaces taking into account the tight mechanical requirements imposed by the Planck focal plane layout. All the eleven units have been successfully tested and integrated with the Ortho Mode transducers.Comment: This is an author-created, un-copyedited version of an article accepted for publication in JINST. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at 10.1088/1748-0221/4/12/T1200

A 3D Conditional Diffusion Model for Image Quality Transfer -- An Application to Low-Field MRI

Low-field (LF) MRI scanners (<1T) are still prevalent in settings with limited resources or unreliable power supply. However, they often yield images with lower spatial resolution and contrast than high-field (HF) scanners. This quality disparity can result in inaccurate clinician interpretations. Image Quality Transfer (IQT) has been developed to enhance the quality of images by learning a mapping function between low and high-quality images. Existing IQT models often fail to restore high-frequency features, leading to blurry output. In this paper, we propose a 3D conditional diffusion model to improve 3D volumetric data, specifically LF MR images. Additionally, we incorporate a cross-batch mechanism into the self-attention and padding of our network, ensuring broader contextual awareness even under small 3D patches. Experiments on the publicly available Human Connectome Project (HCP) dataset for IQT and brain parcellation demonstrate that our model outperforms existing methods both quantitatively and qualitatively. The code is publicly available at \url{https://github.com/edshkim98/DiffusionIQT}

Mathematical models for the diffusion magnetic resonance signal abnormality in patients with prion diseases

AbstractIn clinical practice signal hyperintensity in the cortex and/or in the striatum on magnetic resonance (MR) diffusion-weighted images (DWIs) is a marker of sporadic Creutzfeldt–Jakob Disease (sCJD). MR diagnostic accuracy is greater than 90%, but the biophysical mechanisms underpinning the signal abnormality are unknown. The aim of this prospective study is to combine an advanced DWI protocol with new mathematical models of the microstructural changes occurring in prion disease patients to investigate the cause of MR signal alterations. This underpins the later development of more sensitive and specific image-based biomarkers. DWI data with a wide a range of echo times and diffusion weightings were acquired in 15 patients with suspected diagnosis of prion disease and in 4 healthy age-matched subjects. Clinical diagnosis of sCJD was made in nine patients, genetic CJD in one, rapidly progressive encephalopathy in three, and Gerstmann–Sträussler–Scheinker syndrome in two. Data were analysed with two bi-compartment models that represent different hypotheses about the histopathological alterations responsible for the DWI signal hyperintensity. A ROI-based analysis was performed in 13 grey matter areas located in affected and apparently unaffected regions from patients and healthy subjects. We provide for the first time non-invasive estimate of the restricted compartment radius, designed to reflect vacuole size, which is a key discriminator of sCJD subtypes. The estimated vacuole size in DWI hyperintense cortex was in the range between 3 and 10 µm that is compatible with neuropathology measurements. In DWI hyperintense grey matter of sCJD patients the two bi-compartment models outperform the classic mono-exponential ADC model. Both new models show that T2 relaxation times significantly increase, fast and slow diffusivities reduce, and the fraction of the compartment with slow/restricted diffusion increases compared to unaffected grey matter of patients and healthy subjects. Analysis of the raw DWI signal allows us to suggest the following acquisition parameters for optimized detection of CJD lesions: b = 3000 s/mm2 and TE = 103 ms. In conclusion, these results provide the first in vivo estimate of mean vacuole size, new insight on the mechanisms of DWI signal changes in prionopathies and open the way to designing an optimized acquisition protocol to improve early clinical diagnosis and subtyping of sCJD

Diffusion-Weighted MR Imaging to Evaluate Immediate Response to Irreversible Electroporation in a Rabbit VX2 Liver Tumor Model

PURPOSE: To evaluate the feasibility of diffusion-weighted imaging (DWI) in magnetic resonance imaging for quantitative measurement of responses following irreversible electroporation (IRE) in a rabbit liver tumor model. MATERIALS AND METHODS: Twelve rabbits underwent ultrasound-guided VX2 tumor implantation in the left medial and left lateral liver lobes. The tumors in the left medial lobe were treated with IRE, whereas those in the left lateral lobe served as internal controls. DWI was performed before and immediately after IRE. Tumors were then harvested for histopathologic staining. The apparent diffusion coefficient (ADC) and change in ADC (ΔADC) were calculated based on DWI. Tumor apoptosis index (AI) was assessed by terminal deoxynucleotidyl transferase dUTP nick-end labeling. These measurements from DWI and histopathology were compared between untreated and treated tumors. RESULTS: The ADC values, ΔADC, and AI showed statistically significant differences between treated and untreated tumors (P < .05 for all). ADC values were higher in treated tumors than in untreated tumors (1.08 × 10-3 mm2/s ± 0.15 vs 0.88 × 10-3 mm2/s ± 0.19; P = .042). CONCLUSIONS: DWI can be used to quantitatively evaluate treatment response in liver tumors immediately after IRE

Transcatheter Intraarterial Perfusion MRI Approaches to Differentiate Reversibly Electroporated Penumbra From Irreversibly Electroporated Zones in Rabbit Liver

RATIONALE AND OBJECTIVES: To investigate whether transcatheter intraarterial perfusion (TRIP) magnetic resonance imaging (MRI) can differentiate reversible electroporation (RE) zones from irreversible electroporation (IRE) zones immediately after IRE procedure in the rabbit liver. MATERIALS AND METHODS: All studies were approved by the institutional animal care and use committee and performed in accordance with institutional guidelines. A total of 13 healthy New Zealand White rabbits were used. After selective catheterization of the hepatic artery under X-ray fluoroscopy, we acquired TRIP-MRI at 20 minutes post-IRE using 3 mL of 5% intraarterial gadopentetate dimeglumine. Semi-quantitative (peak enhancement, PE; time to peak, TTP; wash-in slope, WIS; areas under the time-intensity curve, AUT, over 30, 60, 90, 120, 150, and 180 seconds after the initiation of enhancement) and quantitative (Ktrans, ve, and vp) TRIP-MRI parameters were calculated. The relationships between TRIP-MRI parameters and histological measurements and the differential ability of TRIP-MRI parameters was assessed. RESULTS: PE, AUT60, AUT90, AUT120, AUT150, AUT180, Ktrans, and ve were significantly higher in RE zones than in IRE zones (all P < 0.05), and AUC for these parameters ranged from 0.91(95% CI, 0.80, 1.00) to 0.99 (95% CI, 0.98, 1.00). There was no significant difference in AUC between any two parameters (Z, 0-1.47; P, 0.14-1.00). Hepatocyte apoptosis strongly correlated with PE, AUT60, AUT90, AUT120, AUT150, AUT180, Ktrans, and vp (the absolute value r, 0.6-0.7, all P < 0.0001). CONCLUSION: AUT150 or AUT180 could be a potential imaging biomarker to differentiate RE from IRE zones, and TRIP-MRI permits to differentiate RE from IRE zones immediately after IRE procedure in the rabbit liver

Prophylactic dendritic cell vaccination controls pancreatic cancer growth in a mouse model

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related deaths with high recurrence after surgery due to a paucity of effective post-surgical adjuvant treatments. DC vaccines can activate multiple anti-tumor immune responses but have not been explored for post-surgery PDAC recurrence. Intraperitoneal (IP) delivery may allow increased DC vaccine dosage and migration to lymph nodes. Here, we investigated the role of prophylactic DC vaccination controlling PDAC tumor growth with IP delivery as an administration route for DC vaccination. METHODS: DC vaccines were generated using ex vivo differentiation and maturation of bone marrow-derived precursors. Twenty mice were divided into four groups (n = 5) and treated with DC vaccines, unpulsed mature DCs, Panc02 lysates or no treatment. After tumor induction, mice underwent three magnetic resonance imaging scans to track tumor growth. Apparent diffusion coefficient (ADC), a quantitative magnetic resonance imaging measurement of tumor microstructure, was calculated. Survival was tracked. Tumor tissue was collected after death and stained with hematoxylin and eosin, Masson's trichrome, terminal deoxynucleotidyl transferase dUTP nick end labeling and anti-CD8 stains for histology. RESULTS: DC-vaccinated mice demonstrated stronger anti-tumor cytotoxicity compared with control groups on lactate dehydrogenase assay. DC vaccine mice also demonstrated decreased tumor volume, prolonged survival and increased ΔADC compared with control groups. On histology, the DC vaccine group had increased apoptosis, increased CD8+ T cells and decreased collagen. ΔADC negatively correlated with % collagen in tumor tissues. DISCUSSION: Prophylactic DC vaccination may inhibit PDAC tumor growth during recurrence and prolong survival. ΔADC may be a potential imaging biomarker that correlates with tumor histological features