Non-local spectroscopy of Andreev bound states

We experimentally investigate Andreev bound states (ABSs) in a carbon nanotube quantum dot (QD) connected to a superconducting Nb lead (S). A weakly coupled normal metal contact acts as a tunnel probe that measures the energy dispersion of the ABSs. Moreover we study the response of the ABS to non-local transport processes, namely Cooper pair splitting and elastic co-tunnelling, that are enabled by a second QD fabricated on the same nanotube on the opposite side of S. We find an appreciable non-local conductance with a rich structure, including a sign reversal at the ground state transition from the ABS singlet to a degenerate magnetic doublet. We describe our device by a simple rate equation model that captures the key features of our observations and demonstrates that the sign of the non-local conductance is a measure for the charge distribution of the ABS, given by the respective Bogoliubov-de Gennes amplitudes $u$ and $v$

Translation of two-photon microscopy to the clinic: multimodal multiphoton CARS tomography of in vivo human skin

Two-photon microscopes have been successfully translated into clinical imaging tools to obtain high-resolution optical biopsies for in vivo histology. We report on clinical multiphoton coherent anti-Stokes Raman spectroscopy (CARS) tomography based on two tunable ultrashort near-infrared laser beams for label-free in vivo multimodal skin imaging. The multiphoton biopsies were obtained with the compact tomograph “MPTflex-CARS” using a photonic crystal fiber, an optomechanical articulated arm, and a four-detector-360 deg measurement head. The multiphoton tomograph has been employed to patients in a hospital with diseased skin. The clinical study involved 16 subjects, 8 patients with atopic dermatitis, 4 patients with psoriasis vulgaris, and 4 volunteers served as control. Two-photon cellular autofluorescence lifetime, second harmonic generation (SHG) of collagen, and CARS of intratissue lipids/proteins have been detected with single-photon sensitivity, submicron spatial resolution, and picosecond temporal resolution. The most important signal was the autofluorescence from nicotinamide adenine dinucleotide [NAD(P)H]. The SHG signal from collagen was mainly used to detect the epidermal–dermal junction and to calculate the ratio elastin/collagen. The CARS/Raman signal provided add-on information. Based on this view on the disease-affected skin on a subcellular level, skin areas affected by dermatitis and by psoriasis could be clearly identified. Multimodal multiphoton tomographs may become important label-free clinical high-resolution imaging tools for in vivo skin histology to realize rapid early diagnosis as well as treatment control

In-vivo somatostatin-receptor expression in small cell lung cancer as a prognostic image biomarker and therapeutic target

Background: Given the dismal prognosis of small cell lung cancer (SCLC), novel therapeutic targets are urgently needed. We aimed to evaluate whether SSTR expression, as assessed by positron emission tomography (PET), can be applied as a prognostic image biomarker and determined subjects eligible for peptide receptor radionuclide therapy (PRRT). Methods: A total of 67 patients (26 females; age, 41–80 years) with advanced SCLC underwent SSTR-directed PET/computed tomography (somatostatin receptor imaging, SRI). SRI-avid tumor burden was quantified by maximum standardized uptake values (SUVmax) and tumor-to-liver ratios (T/L) of the most intense SCLC lesion. Scan findings were correlated with progression-free (PFS) and overall survival (OS). In addition, subjects eligible for SSTR-directed radioligand therapy were identified, and treatment outcome and toxicity profile were recorded. Results: On a patient basis, 36/67 (53.7%) subjects presented with mainly SSTR-positive SCLC lesions (>50% lesions positive); in 10/67 patients (14.9%), all lesions were positive. The median SUVmax was found to be 8.5, while the median T/L was 1.12. SRI-uptake was not associated with PFS or OS, respectively (SUVmax vs. PFS, ρ = 0.13 with p = 0.30 and vs. OS, ρ = 0.00 with p = 0.97; T/L vs. PFS, ρ = 0.07 with p = 0.58 and vs. OS, ρ = −0.05 with p = 0.70). PRRT was performed in 14 patients. One patient succumbed to treatment-independent infectious complications immediately after PRRT. In the remaining 13 subjects, disease control was achieved in 5/13 (38.5%) with a single patient achieving a partial response (stable disease in the remainder). In the sub-group of responding patients, PFS and OS were 357 days and 480 days, respectively. Conclusions: SSTR expression as detected by SRI is not predictive of outcome in patients with advanced SCLC. However, it might serve as a therapeutic target in selected patient

The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights

Standardizing convolutional filters that enhance specific structures and patterns in medical imaging enables reproducible radiomics analyses, improving consistency and reliability for enhanced clinical insights. Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking

Proximale Methoden in der medizinischen Bildrekonstruktion und in der nicht-glatten optimalen Steuerung von partiellen Differenzialgleichungen

Proximal methods are iterative optimization techniques for functionals, J = J1 + J2, consisting of a differentiable part J2 and a possibly nondifferentiable part J1. In this thesis proximal methods for finite- and infinite-dimensional optimization problems are discussed. In finite dimensions, they solve l1- and TV-minimization problems that are effectively applied to image reconstruction in magnetic resonance imaging (MRI). Convergence of these methods in this setting is proved. The proposed proximal scheme is compared to a split proximal scheme and it achieves a better signal-to-noise ratio. In addition, an application that uses parallel imaging is presented. In infinite dimensions, these methods are discussed to solve nonsmooth linear and bilinear elliptic and parabolic optimal control problems. In particular, fast convergence of these methods is proved. Furthermore, for benchmarking purposes, truncated proximal schemes are compared to an inexact semismooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of our proximal schemes that need less computation time than the semismooth Newton method in most cases. Results of numerical experiments are presented that successfully validate the theoretical estimates.Proximale Methoden sind iterative Optimierungsverfahren für Funktionale J = J1 +J2, die aus einem differenzierbaren Teil J2 und einem möglicherweise nichtdifferenzierbaren Teil bestehen. In dieser Arbeit werden proximale Methoden für endlich- und unendlichdimensionale Optimierungsprobleme diskutiert. In endlichen Dimensionen lösen diese `1- und TV-Minimierungsprobleme welche erfolgreich in der Bildrekonstruktion der Magnetresonanztomographie (MRT) angewendet wurden. Die Konvergenz dieser Methoden wurde in diesem Zusammenhang bewiesen. Die vorgestellten proximalen Methoden wurden mit einer geteilten proximalen Methode verglichen und konnten ein besseres Signal-Rausch-Verhältnis erzielen. Zusätzlich wurde eine Anwendung präsentiert, die parallele Bildgebung verwendet. Diese Methoden werden auch für unendlichdimensionale Probleme zur Lösung von nichtglatten linearen und bilinearen elliptischen und parabolischen optimalen Steuerungsproblemen diskutiert. Insbesondere wird die schnelle Konvergenz dieser Methoden bewiesen. Außerdem werden abgeschnittene proximale Methoden mit einem inexakten halbglatten Newtonverfahren verglichen. Die numerischen Ergebnisse demonstrieren die Effektivität der proximalen Methoden, welche im Vergleich zu den halbglatten Newtonverfahren in den meisten Fällen weniger Rechenzeit benötigen. Zusätzlich werden die theoretischen Abschätzungen bestätigt

Proximal Methods for Elliptic Optimal Control Problems with Sparsity Cost Functional

First-order proximal methods that solve linear and bilinear elliptic optimal control problems with a sparsity cost functional are discussed. In particular, fast convergence of these methods is proved. For benchmarking purposes, inexact proximal schemes are compared to an inexact semismooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of proximal schemes applied to infinite-dimensional elliptic optimal control problems and to validate the theoretical estimates