arXiv subjects

Volkmar Schulz

Publications and source records attributed to Volkmar Schulz.

At least 19 recordsLinked to original sources

arXiv2026

PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstruction

Positron Emission Tomography (PET) image reconstruction is inherently challenged by Poisson noise and physical degradation factors, which are further exacerbated in limited-angle acquisitions. While deep learning methods demonstrate promising performance, their generalization to unseen clinical data distributions remains limited without extensive retraining. We propose PET-Adapter, a test-time domain adaptation framework for generative PET reconstruction models pretrained solely on phantom data. Our method enables adaptation to clinical datasets with varying anatomies, tracers, and scanner configurations without requiring paired ground truth. PET-Adapter introduces layer-wise low-rank anatomical conditioning during adaptation and Ordered Subset Expectation Maximization-based warm-starting that initializes the generation from physics-informed reconstructions, reducing diffusion steps from 50 to 2 without compromising quality. Experiments across multiple clinical datasets demonstrate superior 3D reconstruction performance in both full-angle and limited-angle settings, highlighting the clinical feasibility and computational efficiency of the proposed approach.

cs.CV↗

arXiv2026

Training-Free Stimulus Encoding for Retinal Implants via Sparse Projected Gradient Descent

Retinal implants aim to restore functional vision despite photoreceptor degeneration, yet are fundamentally constrained by low resolution electrode arrays and patient-specific perceptual distortions. Most deployed encoders rely on task-agnostic downsampling and linear brightness-to-amplitude mappings, which are suboptimal under realistic perceptual models. While global inverse problems have been formulated as neural networks, such approaches can be fast at inference, and can achieve high reconstruction fidelity, but require training and have limited generalizability to arbitrary inputs. We cast stimulus encoding as a constrained sparse least-squares problem under a linearized perceptual forward model. Our key observation is that the resulting perception matrix can be highly sparse, depending on patient and implant configuration. Building on this, we apply an efficient projected residual norm steepest descent solver that exploits sparsity and supports stimulus bounds via projection. In silico experiments across four simulated patients and implant resolutions from $15\times15$ to $100\times100$ electrodes demonstrate improved reconstruction fidelity, with up to $+0.265$ SSIM increase, $+12.4\,\mathrm{dB}$ PSNR, and $81.4\%$ MAE reduction on Fashion-MNIST compared to Lanczos downsampling.

eess.IV↗

arXiv2025

Diffusion-based Sinogram Interpolation for Limited Angle PET

Accurate PET imaging increasingly requires methods that support unconstrained detector layouts from walk-through designs to long-axial rings where gaps and open sides lead to severely undersampled sinograms. Instead of constraining the hardware to form complete cylinders, we propose treating the missing lines-of-responses as a learnable prior. Data-driven approaches, particularly generative models, offer a promising pathway to recover this missing information. In this work, we explore the use of conditional diffusion models to interpolate sparsely sampled sinograms, paving the way for novel, cost-efficient, and patient-friendly PET geometries in real clinical settings.

cs.LG↗

arXiv2025

Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework

Computer-generated holography (CGH) enables applications in holographic augmented reality (AR), 3D displays, systems neuroscience, and optical trapping. The fundamental challenge in CGH is solving the inverse problem of phase retrieval from intensity measurements. Physics-inspired neural networks (PINNs), especially Gerchberg-Saxton-based PINNs (GS-PINNs), have advanced phase retrieval capabilities. However, their performance strongly depends on forward models (FMs) and their hyperparameters (FMHs), limiting generalization, complicating benchmarking, and hindering hardware optimization. We present a systematic sensitivity analysis framework based on Saltelli's extension of Sobol's method to quantify FMH impacts on GS-PINN performance. Our analysis demonstrates that SLM pixel-resolution is the primary factor affecting neural network sensitivity, followed by pixel-pitch, propagation distance, and wavelength. Free space propagation forward models demonstrate superior neural network performance compared to Fourier holography, providing enhanced parameterization and generalization. We introduce a composite evaluation metric combining performance consistency, generalization capability, and hyperparameter perturbation resilience, establishing a unified benchmarking standard across CGH configurations. Our research connects physics-inspired deep learning theory with practical CGH implementations through concrete guidelines for forward model selection, neural network architecture, and performance evaluation. Our contributions advance the development of robust, interpretable, and generalizable neural networks for diverse holographic applications, supporting evidence-based decisions in CGH research and implementation.

cs.CV↗

arXiv2025

Rethinking Timing Residuals: Advancing PET Detectors with Explicit TOF Corrections

PET is a functional imaging method that visualizes metabolic processes. TOF information can be derived from coincident detector signals and incorporated into image reconstruction to enhance the SNR. PET detectors are typically assessed by their CTR, but timing performance is degraded by various factors. Research on timing calibration seeks to mitigate these degradations and restore accurate timing information. While many calibration methods use analytical approaches, machine learning techniques have recently gained attention due to their flexibility. We developed a residual physics-based calibration approach that combines prior domain knowledge with the power of machine learning models. This approach begins with an initial analytical calibration addressing first-order skews. The remaining deviations, regarded as residual effects, are used to train machine learning models to eliminate higher-order skews. The key advantage is that the experimenter guides the learning process through the definition of timing residuals. In earlier studies, we developed models that directly predicted the expected time difference, which offered corrections only implicitly (implicit correction models). In this study, we introduce a new definition for timing residuals, enabling us to train models that directly predict correction values (explicit correction models). The explicit correction approach significantly simplifies data acquisition, improves linearity, and enhances timing performance from $371 \pm 6$ ps to $281 \pm 5$ ps for coincidences from 430 keV to 590 keV. Additionally, the new definition reduces model size, making it suitable for high-throughput applications like PET scanners. Experiments were conducted using two detector stacks composed of $4 \times 4$ LYSO:Ce,Ca crystals ($3.8\times 3.8\times 20$ mm$^{3}$) coupled to $4 \times 4$ Broadcom NUV-MT SiPMs and digitized with the TOFPET2 ASIC.

physics.ins-det↗

arXiv2023

Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a Universal bioMedical PreTrained model (UMedPT) on a multi-task database including tomographic, microscopic, and X-ray images, with various labelling strategies such as classification, segmentation, and object detection. The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models. For tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required not more than 50% of the original training data. In an external independent validation imaging features extracted using UMedPT proved to be a new standard for cross-center transferability.

cs.CV↗

arXiv2023

Development and validation of a measurement-driven inter crystal scatter recovery algorithm with in-system calibration

In PET a high percentage of gamma photons being detected undergo Compton scattering in the scintillator. Scintillator blocks are often built from optically isolated crystals. Depending on the angle of incidence and the scintillator geometry this might lead to inter crystal scatter (ICS) events, where energy is deposited in two or more crystals in the detector, which common positioning and reconstruction algorithms cannot resolve. Therefore, ICS events worsen the spatial resolution and the signal-to-noise ratio in the reconstructed image. We want to address this challenge by recovering individual crystals from ICS events with their corresponding energy deposits. This information could ultimately be fed into an image reconstruction framework. In this work, we established an algorithm based on a detector that couples a readout channel to each crystal (one-to-one coupling), which combines a measurement-driven calibration and a fitting routine to achieve the recovery of crystal interactions from measured light patterns. Using Geant4 simulations, we validated and optimized this approach by comparing the recovered events to the simulation ground truth. We showed that, with the best performing algorithm versions, all correct crystals could be identified for 95-97% of the simulated events and the crystal energies as well as the event energy sum could be recovered adequately. For the event energy sum a deviation of less than 5% could be achieved for 96% of all events. Overall, the developed ICS recovery algorithm was successfully validated for one-to-one coupled detector. Future application for other detector configurations should be possible and will be investigated. Additionally, using the new crystal interaction information to determine the most likely first interaction crystal is being examined to improve efficiency and signal-to-noise ratio in the PET reconstruction.

physics.med-ph↗

arXiv2023

Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning -- A Residual Physics Approach

Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal detection to computation, potentially offer significant benefits. This work presents a novel and versatile approach to detector optimization using machine learning (ML) and residual physics. We apply the concept to positron emission tomography (PET), intending to improve the coincidence time resolution (CTR). PET visualizes metabolic processes in the body by detecting photons with scintillation detectors. Improved CTR performance offers the advantage of reducing radioactive dose exposure for patients. Modern PET detectors with sophisticated concepts and read-out topologies represent complex physical and electronic systems requiring dedicated calibration techniques. Traditional methods primarily depend on analytical formulations successfully describing the main detector characteristics. However, when accounting for higher-order effects, additional complexities arise matching theoretical models to experimental reality. Our work addresses this challenge by combining traditional calibration with AI and residual physics, presenting a highly promising approach. We present a residual physics-based strategy using gradient tree boosting and physics-guided data generation. The explainable AI framework SHapley Additive exPlanations (SHAP) was used to identify known physical effects with learned patterns. In addition, the models were tested against basic physical laws. We were able to improve the CTR significantly (more than 20%) for clinically relevant detectors of 19 mm height, reaching CTRs of 185 ps (450-550 keV).

cs.LG↗

arXiv2023

A Finely Segmented Semi-Monolithic Detector tailored for High Resolution PET

Preclinical research and organ-dedicated applications require high-resolution positron emission tomography (PET) detectors to visualize small structures and understand biological processes at a finer level of detail. Current commercial systems often employ finely pixelated or monolithic scintillators, each with its limitations. We present a semi-monolithic detector, tailored for high-resolution PET applications, and merging concepts of monolithic and pixelated crystals. The detector features slabs measuring (24 x 10 x 1) sq. mm, coupled to a 12 x 12 readout channel photosensor with 4 mm pitch. The slabs are grouped in two arrays of 44 slabs each to achieve a higher optical photon density. We employ a fan beam collimator for fast calibration to train machine-learning-based positioning models for all three dimensions, including slab identification and depth-of-interaction (DOI), utilizing gradient tree boosting (GTB). Energy calculation was based on a position-dependent energy calibration. Using an analytical timing calibration, time skews were corrected for coincidence timing resolution (CTR) estimation. Leveraging machine-learning-based calibration in all three dimensions, we achieved high detector spatial resolution: down to 1.18 mm full width at half maximum (FWHM) detector spatial resolution and 0.75 mm mean absolute error (MAE) in the planar-monolithic direction along the slabs, and 2.14 mm FWHM and 1.03 mm MAE for depth-of-interaction (DOI) at an energy window of (435-585) keV. Correct slab interaction identification exceeded 80%, alongside an energy resolution of 13.8% and a CTR of 450 ps FWHM. Therewith, the introduced finely segmented, high-resolution slab detector demonstrates an appealing performance suitable for high-resolution PET applications. The current benchtop-based detector calibration routine allows these detectors to be used in PET systems.

physics.med-ph↗

arXiv2023

Study experimental time resolution limits of recent ASICs at Weeroc with different SiPMs and scintillators

Medical applications, such as Positron Emission Tomography (PET), and space applications, such as Light Detection and Ranging (LIDAR), are in need of highly specialized ASICs. Weeroc, in collaboration with different partners, is highly involved in developing a new generation of front-end ASICs. In the context of a joined LIDAR project among Weeroc, CNES, and Airbus, Weeroc is working on the development of Liroc, an ASIC for space LIDAR application. Weeroc is also working on advancing ASICs for medical applications with Radioroc under development and intended to be used for PET applications. This study experimentally evaluates the time resolution limits of these ASICs in different configurations, with some of the most recent silicon photomultiplier (SiPM) technologies available on the market, coupled to different scintillation crystals. The best single-photon time resolution (SPTR) was achieved using FBK NUV-HD SiPMs with an FWHM of 90 ps with Liroc and 73 ps with Radioroc. Furthermore, the coincidence time resolution (CTR) of Radioroc was studied with different crystal sizes. Using a large LYSO:Ce,Ca crystal of (3 x 3 x 20 mm3) with Broadcom Near UltraViolet-Metal in Trench (NUV-MT) yields a CTR of 127 ps (FWHM). The best CTR of Radioroc was determined to 83 ps (FWHM) with Broadcom NUV-MT SiPMs coupled to LYSO:Ce,Ca (2 x 2 x 3 mm3) from Taiwan Applied Crystal (TAC).

physics.ins-det↗

arXiv2023

Near-field coded-mask technique and its potential for proton therapy monitoring

Objective. Prompt-gamma imaging encompasses several approaches for online monitoring of beam range or deposited dose distribution in proton therapy. We test one of the imaging techniques - a coded mask approach - both experimentally and via simulations. Approach. Two imaging setups have been investigated experimentally. Each of them comprised a structured tungsten collimator in a form of a MURA mask and a LYSO:Ce scintillation detector of fine granularity. The setups differed in the detector dimensions and the operation mode (1D or 2D imaging). A series of measurements with radioactive sources have been conducted, testing the setups' performance of near-field gamma imaging. Additionally, Monte Carlo simulations of a larger setup of the same type were conducted, investigating its performance with a realistic gamma source distribution occurring during proton therapy. Main results. The images of point-like sources reconstructed from two smallscale prototypes' data using the MLEM algorithm constitute the experimental proof of principle for the near-field coded-mask imaging modality, both in the 1D and the 2D mode. Their precision allowed us to calibrate out certain systematic offsets appearing due to the misalignment of setup elements. The simulation of the full-scale setup yielded a mean distal falloff retrieval precision of 0.72 mm in the studies for beam energy range 89.5-107.9 MeV and with 1x10^8 protons (typical number for single distal spots). The implemented algorithm of image reconstruction is relatively fast - a typical procedure needs several seconds. Significance. Coded-mask imaging appears a valid option for proton therapy monitoring. The results of simulations let us conclude that the proposed fullscale setup is competitive to the knife-edge-shaped and the multiparalell slit cameras investigated by other groups.

physics.med-ph↗

arXiv2022

A Semi-Monolithic Detector providing intrinsic DOI-encoding and sub-200 ps CRT TOF-Capabilities for Clinical PET Applications

Current clinical PET systems utilize detectors where the scintillator typically contains single elements of 3 - 6 mm width and about 20 mm height. While providing good TOF performance, this design limits the spatial resolution and causes radial astigmatism as the DOI remains unknown. We propose an alternative, aiming to combine the advantages of current detectors with the DOI capabilities shown for monolithic concepts, based on semi-monolithic scintillators (slabs). An array of 8 monolithic LYSO slabs of dimensions 3.9 x 32 x 19 mm3 was read out by a 64-channel photosensor containing digital SiPMs. The position estimation in the detector's monolithic and DOI direction was based on a calibration with a fan beam collimator and the machine learning technique gradient tree boosting (GTB). We achieved a positioning performance in terms of mean absolute error (MAE) of 1.44 mm and 2.12 mm for DOI considering a wide energy window of 300 - 700 keV. The energy resolution was determined to be 11.3%. We established both an analytical and machine-learning-based timing calibration approach and applied them for a first-photon trigger. The analytical timing calibration corrects for electronic and optical time skews leading to 240 ps coincidence resolving time (CRT) for a pair of slab-detectors. The CRT was significantly improved by utilizing GTB to predict the time difference based on specific training data and applied on top of the analytical calibration. We achieved 209 ps for the wide energy window and 198 ps for a narrow selection around the photopeak (411 - 561 keV). To maintain the detector's sensitivity, no filters were applied to the data during processing. Overall, the semi-monolithic detector provides attractive performance characteristics. Especially, a good CRT can be achieved while introducing DOI capabilities to the detector, making the concept suitable for clinical PET scanners.

physics.ins-det↗

arXiv2022

Evaluation of semi-monolithic scintillators with integrated RF shielding material for a higher integration of PET/MRI systems

The integration of PET into MRI to form a hybrid system requires often compromises for both subsystems. For example, the integration might come at the cost of a reduced PET detector height or a reduced MRI examination volume diameter. Here, we propose a so-called shared-volume concept to use the volume required for both subsystems more efficiently, in which the MR transparency of the scintillator is exploited by integrating the RF shielding into the scintillator of the PET detector. Semi-monolithic scintillator prototypes (PVC slabs) of 7 mm and 12 mm height with integrated copper foil between every and every other slab, respectively, were investigated to evaluate the shielding effectiveness (SE). The SE was measured with probes on a test bench system. In addition, the PET detector performance was evaluated by determining the positioning using gradient tree boosting, energy and timing resolution using digital SiPM arrays (DPC3200, PDPC) in three LYSO scintillator configurations: ESR separation (Slab$\mathrm{_{ESR}}$), ESR with copper foil in between (Slab$\mathrm{_{ESR+Cu}}$), and purely copper foil separation (Slab$\mathrm{_{Cu}}$). The prototype with shielding between each slab and 12 mm height showed the highest SE with 31 dB (mean) in combination with a supporting frame. While Slab$\mathrm{_{Cu}}$ was best for positioning, followed by Slab$\mathrm{_{ESR}}$ and Slab$\mathrm{_{ESR+Cu}}$, the Slab$\mathrm{_{ESR}}$ had the best energy resolution (10.6 %), followed by Slab$\mathrm{_{ESR+Cu}}$ (11.2 %) and Slab$\mathrm{_{Cu}}$ (12.6 %). For the timing resolution, Slab$\mathrm{_{ESR}}$ and Slab$\mathrm{_{Cu}}$ achieved 279 ps and 288 ps, respectively. Slab$\mathrm{_{ESR+Cu}}$ performed worst (293 ps). The scintillator-based RF shielding shows good RF shielding with similar PET performance, demonstrating the potential for more effective integration of PET detectors into MRI systems.

physics.ins-det↗

arXiv2022

Image prediction of disease progression by style-based manifold extrapolation

Disease-modifying management aims to prevent deterioration and progression of the disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is often hampered by the failure to recognize the presymptomatic disease and limited understanding of disease development. We present a generic solution for this problem by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation optimization approach. To this end, we combined a regularized generative adversarial network (GAN) and a latent nearest neighbor algorithm for joint optimization to generate plausible images of future time points. We evaluated our method on osteoarthritis (OA) data from a multi-center longitudinal study (the Osteoarthritis Initiative, OAI). With presymptomatic baseline data, our model is generative and significantly outperforms the end-to-end learning model in discriminating the progressive cohort. Two experiments were performed with seven experienced radiologists. When no synthetic follow-up radiographs were provided, our model performed better than all seven radiologists. In cases where the synthetic follow-ups generated by our model were available, the specificity and sensitivity of all readers in discriminating progressors increased from $72.3\%$ to $88.6\%$ and from $42.1\%$ to $51.6\%$, respectively. Our results open up a new possibility of using model-based morphology and risk prediction to make predictions about future disease occurrence, as demonstrated in the example of OA.

eess.IV↗

arXiv2021

Evaluation Of The PETsys TOFPET2 ASIC In Multi-Channel Coincidence Experiments

Aiming to measure the difference in arrival times of two coincident gamma-photons with an accuracy in the order of 200 ps, time-of-flight positron emission tomography systems commonly employ silicon photomultipliers (SiPMs) and high-resolution digitization electronics, application specific integrated circuits (ASICs). This work evaluates the performance of the TOFPET2 ASIC, released by PETsys Electronics S.A. in 2017, dependent on its configuration parameters in multi-channel coincidence measurements. SiPM arrays fabricated by different vendors (KETEK, SensL, Hamamatsu, Broadcom) were tested in combination with the ASIC. Scintillator arrays featuring different reflector designs and different configurations of the TOFPET2 ASIC software parameters were evaluated. The benchtop setup used is provided with the TOFPET2 ASIC evaluation kit by PETsys Electronics S.A.. Compared to existing studies featuring the TOFPET2 ASIC, multi-channel performance results dependent on a larger set of ASIC configuration parameters were obtained that have not been reported to this extend so far. The ASIC shows promising CRTs down to 219.9 ps in combination with two Hamamatsu S14161-3050-HS-08 SiPM arrays (128 channels read out, energy resolution 13.08 %) and 216.1 ps in combination with two Broadcom AFBR-S4N44P643S SiPM arrays (32 channels read out, energy resolution 9.46 %). The length of the trigger delay of the dark count suppression scheme has an impact on the ASIC performance. The integrator gain configuration has been investigated and allows an improvement of the energy resolution. Measuring up to the time-of-flight performance of state-of-the-art positron emission tomography (ToF-PET) systems while providing a uniform and stable readout for multiple channels at the same time, the TOFPET2 ASIC is treated as promising candidate for the integration in future ToF-PET systems.

physics.med-ph↗

arXiv2020

Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization

Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts. We let six experienced radiologists rate the interpretability of saliency maps in datasets of X-rays, computed tomography, and magnetic resonance imaging scans. Significant improvements were found for our adversarial models, which could be further improved by the application of dual batch normalization. Contrary to previous research on adversarially trained models, we found that the accuracy of such models was equal to standard models when sufficiently large datasets and dual batch norm training were used. To ensure transferability, we additionally validated our results on an external test set of 22,433 X-rays. These findings elucidate that different paths for adversarial and real images are needed during training to achieve state of the art results with superior clinical interpretability.

cs.LG↗

arXiv2019

Investigation of the Power Consumption of the PETsys TOFPET2 ASIC

In state-of-the-art positron emission (PET) tomography systems, application-specific integrated circuits (ASICs)are commonly used to precisely digitize the signals of analog silicon photo-multipliers (SiPMs). However, when operating PET electronics in a magnetic resonance (MR) system, one faces the challenge of mutual interference of these imaging techniques. To prevent signal deterioration along long analog signal lines, PET electronics with a low power consumption digitizing the signals close to the SiPMs are preferred. In this study, we evaluate the power consumption of the TOFPET2 ASIC. Its power consumption ranges from 3.6 to 7.2 mW/channel as a function of the input stage impedance and discriminator noise settings. We present an analytical model allowing to compute the power consumption of a given ASIC configuration. The configured input stage impedance and discriminator noise have an impact on the coincidence resolution time, energy resolution, and photon trigger level. Since the TOFPET2 ASIC delivers state-of-the-art performance with a power consumption similar or even lower than other ASICs typically used for PET applications, it is a favorable candidate to digitize the signals of SiPMs in future simultaneous PET/MR systems.

physics.ins-det↗

arXiv2019

Comments on the NEMA NU 4-2008 Standard on Performance Measurement of Small Animal Positron Emission Tomographs

The National Electrical Manufacturers Association's (NEMA) NU 4-2008 standard specifies methodology for evaluating the performance of small-animal PET scanners. The standard's goal is to enable comparison of different PET scanners over a wide range of technologies and geometries used. In this work, we discuss if the NEMA standard meets these goals and we point out potential flaws and improvements to the standard. For the evaluation of spatial resolution, the NEMA standard mandates the use of filtered backprojection reconstruction. This reconstruction method can introduce star-like artifacts for detectors with an anisotropic spatial resolution, usually caused by parallax error. These artifacts can then cause a strong dependence of the resulting spatial resolution on the size of the projection window in image space, whose size is not fully specified in the NEMA standard. We show that the standard's equations for the estimation of the random rate for PET systems with intrinsic radioactivity are circular and not satisfiable. We compare random rates determined by a method based on the standard to to random rates obtained using a delayed coincidence window and two methods based on the singles rates. We discuss to what extend the NEMA standard's protocol for the evaluation of the sensitivity is unclear and ambiguous, which can lead to unclear interpretations of publicizeded results. The standard's definition of the recovery coefficients in the image quality phantom includes the maximum activity in a region of interest, which causes a positive correlation of noise and recovery coefficients. This leads to an unintended trade-off between desired uniformity, which is negatively correlated with variance (i.e. noise), and recovery. With this work, we want to start a discussion on possible improvements in a next version of the NEMA NU-4 standard.

physics.med-ph↗