📢 New cardiovascular imaging research! 🫀✨ Check out this study on how image reconstruction choices affect hemodynamic parameters derived from 4D-flow CMR using compressed sensing (CS) — an advanced MRI technique that can speed up and improve blood flow assessment. 🔍📈 📌 Key insights: • 4D-flow CMR with compressed sensing can be applied clinically to assess stroke volume in both paediatric and adult patients. • Haemodynamic metrics (like ventricular kinetic energy and flow forces) are sensitive to the CS reconstruction settings, highlighting the need for careful validation across scanners and centres. • Important implications for standardizing advanced flow imaging in research and clinical practice. 💡 👉 A must-read for those involved in cardiac MRI, hemodynamics & quantitative imaging! ❤️ #Cardiology #CMR #4DFlow #CompressedSensing #ImagingInnovation #CardioTwitter #MedicalResearch #Hemodynamics #MagneticResonance #ClinicalImaging doi.org/10.1093/ehjimp/qyae1…

🌀【第63回 神奈川MRI技術研究会】 11月14日(金)18:30〜 WEB開催📡 テーマは「高速撮像」＆「中枢神経MRI検査」🧠 “速くて綺麗”を実現する技術と必見の実践講演をお届けします！ #MRI #放射線技師 #神奈川MRI #高速撮像 #CompressedSensing #DeepLearning #中枢神経MRI #技術研究会 #医療技術

🎓👏 Orgullo de Ingeniería en Biomedicina – UPS El trabajo de titulación de Santiago Villota, junto al Ph.D. Esteban Inga, fue publicado en Sensors (Q2, IF 3.5). ⚡ Reconstrucción de imágenes médicas de 30 min a ~7 min con #CompressedSensing 🧠 📖 mdpi.com/1424-8220/25/16/513…

Dear @GittaKutyniok, you might like the way #compressedsensing comes in in a fresh way.

Compressed Sensing for reduced scan time. Discover @RSNA 24 the latest version of the Helium-free MgB2 based MRI scanner, provides equal quality image and a scan time reduced up to 50%. . #MROpenEVO #MRI #scantime #compressedsensing #rsna #openmri #thebestmriexperience

Characterizing quantum systems is a fundamental task that enables the development of #quantumtechnologies. Various approaches, ranging from full #tomography to instances of classical shadows, have been proposed to this end. However, quantum states that are being prepared in practice often involve families of quantum states characterized by continuous parameters, such as the #timeevolution of a quantum state. In this work, we extend the foundations of quantum state tomography to #parametrizedquantumstates. We introduce a framework that unifies different notions of tomography and use it to establish a natural figure of merit for tomography of parametrized quantum states. Building on this, we provide an explicit algorithm that combines #signalprocessing techniques with a tomography scheme to recover an approximation to the parametrized quantum state equipped with explicit guarantees. Our algorithm uses techniques from #compressedsensing to exploit structure in the parameter dependence and operates with a plug and play nature, using the underlying tomography scheme as a black box. In an analogous fashion, we derive a figure of merit that applies to parametrized #quantumchannels. Substituting the state tomography scheme with a scheme for process tomography in our algorithm, we then obtain a protocol for tomography of parametrized quantum channels. We showcase our algorithm with two examples of shadow tomography of states time-evolved under an NMR Hamiltonian and a free fermionic Hamiltonian.

Comparison of Common Algorithms for Single-Pixel Imaging via Compressed Sensing mdpi.com/1424-8220/23/10/467… #compressedsensing #opticalsignalprocessing

@matsanta talked about "Continuous #generativemodels for nonlinear #inverseproblems”: a data-driven approach based on generative models. A.Felisi focused on #compressedsensing applied to inverse problems in “Compressed sensing for the sparse Radon transform”.

Semi-device-dependent blind #quantumtomography explores the regime between device-independent schemes and those that require complete knowledge of detectors. For this, new methods in #compressedsensing are developed. quantum-journal.org/papers/q…

Congratulations to Alberto @albeg91 for the work "Computational Based Time-Resolved Multispectral Fluorescence Microscopy" promoted as Editor's pick on APL Photonics! aip.scitation.org/doi/10.106… #singlepixelimaging, #compressedsensing, #datafusion

#highlycitedpaper Reference-Driven Compressed Sensing MR Image Reconstruction Using Deep Convolutional Neural Networks without Pre-Training mdpi.com/1424-8220/20/1/308 #referencedriven #compressedsensing #magneticresonanceimaging #deepimageprior #deeplearning

In Cambridge today for @NewtonInstitute workshop on Rich and Nonlinear Tomography in Radar, Astronomy and Geophysics. I'll be talking about Faraday tomography for @SKAO science - highlighting work led by @SymonSimeon & @miguel_carcamov on #GaussianProcesses #CompressedSensing 💫

Very happy to share our preprint with #GianlucaGiacchi & #BastienMilani setting a 1st milestone towards the analytical determination of optimal parameters in a space-variant #Lasso -relevant for #MRI & #compressedsensing @hessovalais @unil @Unibo #MatTech arxiv.org/abs/2301.09083

#mostviewspaper Multitrack Compressed Sensing for Faster Hyperspectral Imaging mdpi.com/1424-8220/21/15/503… #HyperspectralImaging #CompressedSensing

Rounding off the final session of @nam2022warwick: @miguel_carcamov talks about his work developing novel #compressedsensing reconstruction for faraday depth signals in the @mighteesurvey polarisation data ✨ @idsai_uom @UoMPhysics @SKAO

#OEA_highlight Single-pixel terahertz imaging: a review: oejournal.org/article/doi/10… Prof. #LucaRazzari from #InstitutNationaldelaRechercheScientifique @inrsciences #TerahertzImaging #SinglePixelImaging #CompressedSensing

We are recruiting for a Lecturer (Assistant Professor) in Mathematics & Data Science. The deadline to apply to join our fantastic deparment is fast approaching, so don't delay! #machinelearning #compressedsensing #inverseproblems #networkscience bath.ac.uk/jobs/Vacancy.aspx…

Congratulations to @jodrellbank @UoMPhysics PhD student @miguel_carcamov who is the recipient of a 2022✨Young Scientist Award✨ from the International Union of Radio Sciences @URSI_Radio for his work on #CompressedSensing Faraday Depth Reconstruction 📡🍾🎉

DL-CSNet: Dictionary Learning based Compressed Sensing Neural Network #TechRxiv #dictionarylearning #compressedsensing #NeuralNetworks #neuralnetwork techrxiv.org/articles/prepri…

#Compressedsensing allows to recover structured vectors and matrices from a few measurements - such as sparse vectors. Here, we uplift those mathematical techniques to #hierarchical measurement operators, exploring hierarchical isometry properties. sciencedirect.com/science/ar…