With the advent of ever more intense and focused X-ray sources, including in laboratories, at synchrotrons, and at X-ray free electron lasers, radiation-induced sample change and damage are becoming increasingly...

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : 25(1)
• Pub. Date : 2025
• Page :
• Keyword :

A central spin qubit interacting coherently with an ensemble of proximal spins can be used to engineer entangled collective states or a multiqubit register. Making full use of this many-body platform requires tuning the interaction between the central spin and its spin register. GaAs quantum dots offer a model realization of the central spin system where an electron qubit interacts with multiple ensembles of ∼104 nuclear spins. In this work, we demonstrate tuning of the interaction between the electron qubit and the nuclear many-body system in a GaAs quantum dot. The homogeneity of the GaAs system allows us to perform high-precision and isotopically selective nuclear sideband spectroscopy, which reveals the single-nucleus electronic Knight field. Together with time-resolved spectroscopy of the nuclear field, this fully characterizes the electron-nuclear interaction for control. An algorithmic feedback sequence selects the nuclear polarization precisely, which adjusts the electron-nuclear exchange interaction via the electronic g-factor anisotropy. This allows us to tune directly the activation rate of a collective nuclear excitation (magnon) and the coherence time of the electron qubit. Our method is applicable to similar central-spin systems and enables the programmable tuning of coherent interactions in the many-body regime. Published by the American Physical Society 2025

• Book : 15(2)
• Pub. Date : 2025
• Page :
• Keyword :

Abstract Introduction: This systematic review evaluates various studies on deep learning algorithms for generating synthetic CT images from MRI data, focusing on challenges in image quality and accuracy in current synthetic CT generation methods. Magnetic resonance imaging (MRI) is increasingly important in clinical settings due to its detailed visualization and noninvasive nature, making it a valuable tool for advancing patient care and identifying new areas for research. Materials and Methods: In this study we conducted a thorough search across several databases to identify studies published between January 2009 and January 2024 on using deep learning to generate synthetic CT (sCT) images from MRI for radiotherapy. The review focused on peer-reviewed, English-language studies and excluded unpublished, non-English, and irrelevant studies. Data on deep learning methods, input modalities, and anatomical sites were extracted and analyzed using a result-based synthesis approach. The review categorized 84 studies by anatomical site, following PRISMA guidelines for summarizing the findings. Results: The U-Net model is the most frequently used deep learning model for generating synthetic CT images from MRI data, with 34 articles highlighting its effectiveness in capturing fine details, Conditional GANs are also widely used, while Cycle-GANs and Pix2pix are effective in image translation tasks. Significant differences in performance metrics, such as MAE and PSNR, were observed across anatomical regions and models, highlighting the variability in accuracy among different deep learning approaches. Conclusion: This review underscores the need for continued refinement and standardization in deep learning approaches for medical imaging to address variability in performance metrics across anatomical regions and models.

• Book : 31(1)
• Pub. Date : 2025
• Page : pp.20-38
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

Abstract We present the analysis of Lyα haloes around faint quasars at z ∼ 4 and z ∼ 6. We use 20 and 162 quasars at z ∼ 4 and z ∼ 6, taken by slit spectroscopy, and detect Lyα haloes around 12 and 26 of these quasars, respectively. The average absolute magnitudes of the detected quasars are 〈M1450〉 = −23.84 mag at z ∼ 4 and 〈M1450〉 = −23.68 mag at z ∼ 6, which are comparable at z ∼ 4 and 3 mag fainter at z ∼ 6 than those of previous studies. The median surface brightness profiles are found to be consistent with an exponential curve, showing a hint of flattening within a radius of 5 kpc. The Lyα haloes around these faint quasars are systematically fainter than those around bright quasars in the previous studies. We confirm the previous results that the Lyα halo luminosity depends on both the ionizing and Lyα peak luminosities of quasars at z ∼ 4, and also find that a similar correlation holds even at z ∼ 6. While the observed Lyα halo luminosity is overall attributed to recombination emission from the optically thin gas clouds in the CGM, its luminosity dependences can be consistently explained by the partial contributions of recombination radiation from the optically thick clouds, which is thought to originate from the CGM centre, and the scattered Lyα photons, which is resonantly trapped at the CGM centre and escaping outside of the haloes.

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :