Maximum A Posteriori Research Articles

Persymmetric adaptive subspace detection in compound Gaussian sea clutter with generalized inverse Gaussian texture

This paper considers the detection problem of subspace targets in compound Gaussian sea clutter with generalized inverse Gaussian (GIG) texture. Based on the Wald test, the gradient test, and the Durbin test, we propose three novel subspace detectors. And the criterion above are first applied to the detection problem in compound Gaussian (CG) sea clutter with GIG texture. In the first step, assuming that the speckle covariance matrix (CM) and the texture component are unknown to obtain the test statistics of the proposed detectors. In the second step, we exploit the persymmetric property of speckle CM and the maximum a posteriori (MAP) criterion to obtain the estimation of speckle CM and the GIG texture parameters. Moreover, the constant false alarm rate (CFAR) properties of the three proposed detectors have been proven with respect to the speckle CM and the scale parameter of the texture component. We verify the detection performance of the three proposed detectors by the numerical experiments both in simulated data and real sea clutter data, and the simulated results show the proposed detectors are robust in the case of limited training and mismatched signals.

Thalamo-cortical circuits associated with trait- and state-repetitive negative thinking in major depressive disorder

BackgroundRepetitive negative thinking (RNT), often referred to as rumination in the mood disorders literature, is a symptom dimension associated with poor prognosis and suicide in major depressive disorder (MDD). Given the transdiagnostic nature of RNT, this study aimed to evaluate the hypothesis that neurobiological substrates of RNT in MDD may share the brain mechanisms underlying obsessions, particularly those involving cortico-striatal-thalamic-cortical (CSTC) circuits. MethodsThirty-nine individuals with MDD underwent RNT induction during fMRI. Trait-RNT was measured by the Ruminative Response Scale (RRS) and state-RNT was measured by a visual analogue scale. We employed a connectome-wide association analysis examining the association between RNT intensity with striatal and thalamic connectivity. ResultsA greater RRS score was associated with hyperconnectivity of the right mediodorsal thalamus with prefrontal cortex, including lateral orbitofrontal cortex, along with Wernicke's area and posterior default mode network nodes (t = 4.66–6.70). A greater state-RNT score was associated with hyperconnectivity of the right laterodorsal thalamus with bilateral primary sensory and motor cortices, supplementary motor area, and Broca's area (t = 4.51–6.57). Unexpectedly, there were no significant findings related to the striatum. ConclusionsThe present results suggest RNT in MDD is subserved by abnormal connectivity between right thalamic nuclei and cortical regions involved in both visceral and higher order cognitive processing. Emerging deep-brain neuromodulation methods may be useful to establish causal relationships between dysfunction of right thalamic-cortical circuits and RNT in MDD.

Automatic peak annotation and area estimation of glycan map peaks directly from chromatograms

AbstractThe present bottleneck in biosimilar bioprocess development has become evaluation of analytical results, due to recent advances in analytics, such as automated sample preparation and development of high‐throughput methods. Currently automated chromatogram integration and annotation is only efficient for simple chromatograms. In an ever more competitive field of biosimilars, this represents a serious drawback because chromatographic analytical methods that provide some of the most valuable physicochemical quality attributes of the product also require careful chromatogram integration and annotation. This work focuses on the glycan mapping analytical method as utilized in development of monoclonal antibody biosimilars, evaluating more than 2000 chromatograms spanning the life cycle of multiple biosimilar development projects. It proposes a modified workflow by implementing automatic machine learning algorithms to determine the proportion of specific relevant glycan species in a sample directly from the chromatogram. Data preparation and analysis is performed using a pipeline approach. Pipeline is a modular design of data processing where signal “travels” through various active modules in a series. Each module performs a specific function or transformation on the signal and propagates the transformed signal to the next module. The pipeline is designed in a way that modules can be independently improved and exchanged. Module functions currently implemented are chromatogram resampling by spline interpolation, baseline removal by asymmetric least squares, peak alignment using parametric time warping, and quantification of the relative proportion of a glycan species using partial least squares regression. Hyper‐parameters of the pipeline are then optimized using the Nelder–Mead method. The approach stands out for its ability to accommodate a broad landscape of samples, covering multiple different proteins in different stages of biosimilar development, analyzed using different adaptations of the glycan map analytical method. The pipeline presents an intuitive, flexible, and creatively simple method design capable of providing reliable results for a wide range of glycan species essential for biosimilar development. It enables transparent, faster, and less subjective evaluation of analytic raw data (from sample to result). Furthermore, our automated approach maintained an accuracy comparable with manual integration thus demonstrating its readiness for implementation in the conservative and highly regulated environment. The presented methodology reduces the cost and time of biosimilar development and should be applicable for any chromatogram‐based analytical method.

On the ‘cognitive map debate’ in insect navigation

In a historical account recently published in this journal Dhein argues that the current debate whether insects like bees and ants use cognitive maps (centralized map hypothesis) or other means of navigation (decentralized network hypothesis) largely reflects the classical debate between American experimental psychologists à la Tolman and German ethologists à la Lorenz, respectively. In this dichotomy we, i.e., the proponents of the network hypothesis, are inappropriately placed on the Lorenzian line. In particular, we argue that in contrast to Dhein's claim our concepts are not based on merely instinctive or peripheral modes of information processing. In general, on the one side our approaches have largely been motivated by the early biocybernetics way of thinking. On the other side they are deeply rooted in studies on the insect's behavioral ecology, i.e., in the ecological setting within which the navigational strategies have evolved and within which the animal now operates. Following such a bottom-up approach we are not “anti-cognitive map researchers” but argue that the results we have obtained in ants, and also the results of some decisive experiments in bees, can be explained and simulated without the need of invoking metric maps.

Perception-guided defocus blur detection based on SVD feature

Defocus blur detection aims to identify out-of-focus regions in a single image. Although defocus blur detection has attracted more and more attention in recent years, it still faces some challenges. In particular, the in-focus regions with low contrast are easily misidentified as out-of-focus regions. To address this problem, a perception-guided defocus blur detection method is proposed to estimate defocus blur amounts at edge locations from a single image based on the singular value decomposition (SVD) features. Inspired by the fact that blurring the clear region produces significant changes in SVD domain as compared with re-blurring the blurred one, new SVD features are extracted based on re-blurred singular value difference (RESVD) of the corresponding local gradient patches. Then, perceptual weight based on Just Noticeable Blur (JNB) is introduced to guide the sparse blur map estimation obtained with the SVD features. Finally, the full defocus blur map is constructed from the sparse defocus blur map by the Matting Laplace algorithm. Visual evaluations are conducted on the CUHK, DUT and CTCUG datasets, employing mean absolute error (MAE) and Fβ-measure as quantitative evaluation metrics. The experimental results demonstrate that the proposed defocus blur detection method is superior to 13 state-of-the-art methods. On the DUT dataset, the proposed method yields a high Fβ-measure (0.802) with a low MAE (0.081) compared to other methods (Fβ≤0.799, MAE ≥0.099). On the CUHK and CTCUG datasets, our method achieves the best balance between Fβ-measure and MAE. Furthermore, our method results in better visual effects than other methods regarding realism and quality.

A cross-sectional survey exploring HIV and HCV prevalence among men who purchase sex in Dnipro, Ukraine

BackgroundHIV programming in Ukraine largely targets “key population” groups. Men who purchase sex are not directly reached. The aim of our study was to explore the prevalence of sexually transmitted and blood-borne infections (STBBIs) among men who purchase sex from female sex workers.MethodsFollowing geographic mapping and population size estimation at each “hotspot”, we conducted a cross-sectional bio-behavioural survey with men who purchase sex between September 2017 and March 2018 in Dnipro, Ukraine. Eligibility criteria included purchasing sex services at a “hotspot” and being ≥ 18 years. Participants completed a structured questionnaire, followed by HIV/HCV rapid testing and a dried blood spot (DBS) sample collection for confirmatory serology.ResultsThe study enrolled 370 participants. The median age was 32 (interquartile range [IQR] = 27–38) and the median age of first purchase of sexual services was 22 (IQR = 19–27). Over half (56%) of participants reported ever testing for HIV; four participants (2%, N = 206) reported having tested positive for HIV, with three out of the four reporting being on ART. Forty percent of participants had ever tested for HCV, with three (2%, N = 142) having ever tested positive for HCV. In DBS testing, nine participants (2.4%) tested positive for HIV and 24 (6.5%) tested positive for ever having an HCV infection.ConclusionPrevalence of HIV and HCV in this population was high. Given high rates of study enrolment and testing, efforts should be made to reach men who purchase sex with expanded STBBI programming.

Programmatic mapping and estimating the population size of female sex workers, men who have sex with men, people who inject drugs and transgender populations in Kenya

Introduction Effective coverage of Human Immunodeficiency Virus prevention services for Key Populations (KPs) including female sex workers (FSWs), men who have sex with men (MSM), people who inject drugs (PWID) and transgender (TG) people necessitates periodic validation of physical venues and size estimates. Kenya conducted a robust size estimation of KPs in 2012 and a repeat mapping and size estimation exercise was conducted in 2018 to update KP Size Estimates and sub-typologies within each County for calculation of realistic program indicators. Methods A prospective mixed methods programmatic mapping approach adopted comprised two steps. The first step involved consolidating and documenting all known venues where KPs congregate while the second step included visiting and validating these venues confirming their active status. Data were collected in 34 out of 47 Counties in Kenya between January and March 2018. Data collected included estimated number of KPs (range), venue typology and timing of operation of each venue. Results We estimated a total number of 167,940 (129,271 to 206,609) FSWs; 32,580 (24,704 to 40,455) MSM; 16,063 (12,426 to 19,691) PWIDs; 10,951 (8,160 to 13,742) and 4,305 (2,826 to 5,783) transgender people congregating at 10,250, 1,729, 401 and 1,218 venues respectively. Majority of the venues for FSW (81%), MSM (64%) and transgender people (67%) were bars with and without lodging, PWIDs were mostly found on streets and injecting dens (70%). Around 9% of FSW and MSM and11% of PWIDs were below the age of 18 years. Conclusion This study provided information on young KPs, female PWIDs, MSWs and for the first time, TG people in Kenya. The exercise updated size estimates of KPs by typology and provided new evidence for resource allocation, planning of interventions and targets. Programmatic mapping continues to be a useful approach supporting programs to achieve high levels of coverage and prioritize resources.

RME-GAN: A Learning Framework for Radio Map Estimation Based on Conditional Generative Adversarial Network

RME-GAN: A Learning Framework for Radio Map Estimation Based on Conditional Generative Adversarial Network

Partial NMF-based hyperspectral unmixing methods for linear mixing models addressing intra-class variability

Linear Mixing Models (LMMs) are the most popular ones used in the linear hyperspectral unmixing field. However, several of them do not take into account available additional information and intra-class variability. In this investigation, two informed LMMs, with their related partial unmixing algorithms, which simultaneously address intra-class variability and exploit some available information, are introduced here for hyperspectral unmixing. More precisely, partial, by means of some available known endmember spectra, iterative and multiplicative Nonnegative Matrix Factorization (NMF) algorithms are designed to unmix hyperspectral data by considering the two informed LMMs that deal with intra-class variability. These two unmixing algorithms based on partial NMF, specifically designed to address intra-class variability, can be applied for automated detection, precise mapping, and accurate estimation of areas containing materials with known spectra in hyperspectral data. Experiments, based on synthetic and real data, are carried out, and obtained results are compared to those achieved by some literature methods. These experiments clearly demonstrate the effectiveness of the two proposed informed LMMs and their associated partial NMF-based unmixing algorithms.

A blind convolutional deep autoencoder for spectral unmixing of hyperspectral images over waterbodies

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and public health. Automatic identification based on remote sensing hyperspectral image analysis provides a valuable mechanism for extracting the spectral signatures of harmful algal blooms and their respective percentage in a region of interest. This paper proposes a new model called a non-symmetrical autoencoder for spectral unmixing to perform endmember extraction and fractional abundance estimation. The model is assessed in benchmark datasets, such as Jasper Ridge and Samson. Additionally, a case study of the HSI2 image acquired by NASA over Lake Erie in 2017 is conducted for extracting optical water types. The results using the proposed model for the benchmark datasets improve unmixing performance, as indicated by the spectral angle distance compared to five baseline algorithms. Improved results were obtained for various metrics. In the Samson dataset, the proposed model outperformed other methods for water (0.060) and soil (0.025) endmember extraction. Moreover, the proposed method exhibited superior performance in terms of mean spectral angle distance compared to the other five baseline algorithms. The non-symmetrical autoencoder for the spectral unmixing approach achieved better results for abundance map estimation, with a root mean square error of 0.091 for water and 0.187 for soil, compared to the ground truth. For the Jasper Ridge dataset, the non-symmetrical autoencoder for the spectral unmixing model excelled in the tree (0.039) and road (0.068) endmember extraction and also demonstrated improved results for water abundance maps (0.1121). The proposed model can identify the presence of chlorophyll-a in waterbodies. Chlorophyll-a is an essential indicator of the presence of the different concentrations of macrophytes and cyanobacteria. The non-symmetrical autoencoder for spectral unmixing achieves a value of 0.307 for the spectral angle distance metric compared to a reference ground truth spectral signature of chlorophyll-a. The source code for the proposed model, as implemented in this manuscript, can be found at https://github.com/EstefaniaAlfaro/autoencoder_owt_spectral.git.

A new fast and efficient dehazing and defogging algorithm for single remote sensing images

Information about the earth’s surface is difficult to capture in remote sensing images because bad weather greatly curtails visibility and diminishes visual contrast in the images. For the purpose of military survey and aerial surveillance, these images are crucial for providing information. It is quintessential to eradicate bad weather conditions like haze and fog from remote sensing images. Recently, numerous deweathering initiatives and endeavors have been undertaken to alleviate these limitations. These contemporary deweathering approaches, however, are inadequate to recover dense haze images. This work presented a novel visibility restoration approach based on segmentation and unsharp mask guided filtering method. It consists of the following steps to restore the scene’s radiance: First, a segmentation method is employed to determine atmospheric light to quantify contrast and color. Estimation of the transmission map utilizing the dark channel prior is then performed to precisely determine the proximity between objects. The resultant output image contains halo artifacts and inconsistencies in the structure. A guided filter method based on unsharp masking is employed to optimize the transmission map in order to solve this issue. The experimental results demonstrate that the proposed indicators ensures high uniformity regarding qualitative and quantitative evaluation using six performance metrics: mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), universal image quality index (UQI), fog-aware density evaluator (FADE), dehazing algorithm index (DHQI), standard deviation (CC), and blind contrast enhancement assessment (e and r).

Increased Resting-State Functional Connectivity in Patients With Autoimmune Addison Disease.

Individuals with autoimmune Addison disease (AAD) take replacement medication for the lack of adrenal-derived glucocorticoid (GC) and mineralocorticoid hormones from diagnosis. The brain is highly sensitive to these hormones, but the consequence of having AAD for brain health has not been widely addressed. The present study compared resting-state functional connectivity (rs-fc) of the brain between individuals with AAD and healthy controls. Fifty-seven patients with AAD (33 female) and 69 healthy controls (39 female), aged 19 to 43 years were scanned with 3-T magnetic resonance imaging (MRI). Independent component and subsequent dual regression analyses revealed that individuals with AAD had stronger rs-fc compared to controls in 3 networks: the bilateral orbitofrontal cortex (OFC), the left medial visual and left posterior default mode network. A higher GC replacement dose was associated with stronger rs-fc in a small part of the left OFC in patients. We did not find any clear associations between rs-fc and executive functions or mental fatigue. Our results suggest that having AAD affects the baseline functional organization of the brain and that current treatment strategies of AAD may be one risk factor.

Are Minimizers of the Onsager–Machlup Functional Strong Posterior Modes?

Are Minimizers of the Onsager–Machlup Functional Strong Posterior Modes?

Nonlinear multispectral tomographic absorption deflection spectroscopy based on Bayesian estimation for spatially resolved multiparameter measurement in methane flame exhaust

In aerospace propulsion systems with high temperature, pressure, and distortion, inhomogeneous thermophysical gradients can cause line-of-sight deflection and spectral integral deviation. This introduces nonlinearity and measurement error into spectral models, impacting multiparameter field measurements. To address this, we developed a nonlinear multispectral tomographic absorption deflection spectroscopy (MTADS) technique, based on Bayesian inference, for temperature and gas concentration prediction. Our forward model considers inhomogeneous refractive index gradient and laser deflection, synthesising multispectral laser absorption signal through a gas mixing equation and multipoint ray tracing. Simulation results reveal inhomogeneous parameter distribution can cause non-Gaussian and non-zero mean deviations of the spectral absorption signal. The inverse model increases spectral lines to expand measurement signals under sparse optical arrangements. Combining the measured laser absorption signal with Bayesian statistical information of the estimated parameter field improves measurement robustness and accuracy. The maximum a posteriori (MAP) estimation introduces priors such as experiment samples, computational fluid dynamics data, and diffusive transport-induced spatial smoothing. A proof-of-concept based on methane diffusion flame data demonstrates the effectiveness of MTADS, enabling quantitative flame topology and parameter distribution, more accurate 2D temperature, and concentration imaging. These measurements are crucial for revealing chemical reaction dynamics and monitoring combustion systems.

Model resolution-based deconvolution for improved quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) utilizes the relationship between the measured local field and the unknown susceptibility map to perform dipole deconvolution. The aim of this work is to introduce and systematically evaluate the model resolution-based deconvolution for improved estimation of the susceptibility map obtained using the thresholded k-space division (TKD). A two-step approach has been proposed, wherein the first step involves the TKD susceptibility map computation and the second step involves the correction of this susceptibility map using the model-resolution matrix. The TKD-estimated susceptibility map can be expressed as the weighted average of the true susceptibility map, where the weights are determined by the rows of the model-resolution matrix, and hence a deconvolution of the TKD susceptibility map using the model-resolution matrix yields a better approximation to the true susceptibility map. The model resolution-based deconvolution is realized using closed-form, iterative, and sparsity-regularized implementations. The proposed approach was compared with L2 regularization, TKD, rescaled TKD in superfast dipole inversion, the modulated closed-form method, and iterative dipole inversion, as well as sparsity-regularized dipole inversion. It was observed that the proposed approach showed a substantial reduction in the streaking artifacts across 94 test volumes considered in this study. The proposed approach also showed better error reduction and edge preservation compared with other approaches. The proposed model resolution-based deconvolution compensates for the truncation of zero coefficients in the dipole kernel at the magic angle and hence provides a closer approximation to the true susceptibility map compared with other direct methods.

Depth map estimation with 3DFFT for two-dimensional to three-dimensional stereoscopic conversion based on image registration

Depth map estimation with 3DFFT for two-dimensional to three-dimensional stereoscopic conversion based on image registration

Blind Image Deconvolution Using Variational Deep Image Prior.

Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization. While deep-learning-based methods have simplified the optimization by end-to-end training, they fail to generalize well to blurs unseen in the training dataset. Thus, training image-specific models is important for higher generalization. Deep image prior (DIP) provides an approach to optimize the weights of a randomly initialized network with a single degraded image by maximum a posteriori (MAP), which shows that the architecture of a network can serve as the hand-crafted image prior. Unlike conventional hand-crafted image priors, which are obtained through statistical methods, finding a suitable network architecture is challenging due to the unclear relationship between images and their corresponding architectures. As a result, the network architecture cannot provide enough constraint for the latent sharp image. This paper proposes a new variational deep image prior (VDIP) for blind image deconvolution, which exploits additive hand-crafted image priors on latent sharp images and approximates a distribution for each pixel to avoid suboptimal solutions. Our mathematical analysis shows that the proposed method can better constrain the optimization. The experimental results further demonstrate that the generated images have better quality than that of the original DIP on benchmark datasets.

Temporal Convolutional Network-Based Signal Detection for Magnetotactic Bacteria Communication System.

Molecular communication (MC) aims to use signaling molecules as information carriers to achieve communication between biological entities. However, MC systems severely suffer from inter symbol interference (ISI) and external noise, making it virtually difficult to obtain accurate mathematical models. Specifically, the mathematically intractable channel state information (CSI) of MC motivates the deep learning (DL) based signal detection methods. In this paper, a modified temporal convolutional network (TCN) is proposed for signal detection for a special MC communication system which uses magnetotactic bacteria (MTB) as information carriers. Results show that the TCN-based detector demonstrates the best overall performance. In particular, it achieves better bit error rate (BER) performance than sub-optimal maximum a posteriori (MAP) and deep neural network (DNN) based detectors. However, it behaves similarly to the bidirectional long short term memory (BiLSTM) based detector that has been previously proposed and performs worse than the optimal MAP detector. When both BER performance and computational complexity are taken into account, the proposed TCN-based detector outperforms BiLSTM-based detectors. Furthermore, in terms of robustness evaluation, the proposed TCN-based detector outperforms all other DL-based detectors.

Develop and Evaluate a Dose Calculation Strategy Using Electron Density Maps from Spectral CT

The conventional method of estimating relative electron density using Hounsfield Units (HUs) is prone to errors resulting from various factors such as energy spectrum, exposure, scanner/patient conditions, etc. Specific calibration is needed for each acquisition protocol. To overcome these limitations, dual energy CT has been extensively researched for its accuracy in dose calculation using tube potential switching techniques. A dual layer design offers a different approach to acquire spectral images using single data acquisitions. This study aims to develop and evaluate a dose calculation method using electron density maps generated directly from a dual layer detector scanner. A phantom with tissue equivalent inserts was scanned using different scanner configurations on a dual layer detector scanner. The electron density of 17 inserts ranged from 0.668 - 5.663 × 1023 m-23. The energy dependent attenuation curves were generated and translated into values of Compton and photoelectric components, which were used to calculate ED values. The ED values were compared to normal values provided by the vendor for each insert. The generated ED maps were normalized to the ED of water and used as the input for dose calculations without CT images. Dosimetry plans were generated on the phantom for two different field sizes (10 × 10 cm2 and 3 × 3 cm2) at gantry angles of 0 and 90 degrees using a 6 MV Monte Carlo engine. The dose distributions were compared between the conventional HU to ED calibration approach with CT images and the direct calculation using the calculated ED map. The results showed that compared to the conventional HU to ED map, the ED map generated from spectral CT had a relative ED that was about 0.02 lower and was more uniform, with smaller standard deviations. The ED map was closer to the nominal value in low-density regions, while the HU converted ED map was closer to the nominal value in high-density regions. The dose distributions between the two ED approaches were almost identical, with a maximum deviation of around 1% for both field sizes at deeper depths. In conclusion, a dual layer detector scanner can provide an accurate estimation of ED maps. We showed that the dose calculation using the generated ED map is highly accurate using a phantom. This method provides an alternative strategy for dose calculation that eliminates the need for HU to ED calibration and enables the use of the ED map directly.

New Advantage in Stereotactic Treatment of Lung and Pancreatic Cancer. Performance of Ultra-High Energy Electron (VHEE) Therapy Adjuvanted to the FLASH Effect: Clinical Implications and Treatment Plans Analysis

Very High-Energy Electron (VHEE) beams delivered at ultra-high dose rates and hence profiting from the FLASH effect, may be a viable alternative to conventional treatment plans for the treatment of deep-seated tumors. Experimental data support the evidence of a considerable normal tissue sparing effect when treatments are delivered with dose rates much larger (100 times or more) compared to the conventional ones. Lung cancer and pancreatic cancer are considered the two biggest cancer killers. We urgently need more research in these areas, more awareness that support improvement in treatment strategies. To evaluate the potential of FLASH VHEE irradiation in these two clinical situations, we investigated the achievable sparing of healthy tissues and critical dose-limiting structures, with the goal of performing a higher dose prescription. The study on the potential of VHEE for the stereotactic treatment of pancreatic and lung lesions was carried out on two clinical cases treated with Volumetric Modulated Arc Therapy (VMAT) techniques at University Hospital Campus Bio-Medico of Rome. The Planning Target Volume (PTV) was identified and the constraints on the Organs at Risk (OAR) and details on the irradiation approach were defined. The VHEE plan was designed to optimize the dose delivery to best activate the modelled FLASH effect based on the current experimental knowledge. In particular the impact on a dose threshold to activate the effect was studied. The VHEE treatment plan was based on an accurate Monte Carlo simulation of the electrons interactions and the results achievable with different FLASH effect models were studied. The simulation allowed the estimation of dose maps, which were used as input to an optimization algorithm that modified the fluence of each beam to meet treatment prescriptions in terms of dose to PTV. At the end the VHEE DVH plans were compared to VMAT plans. The results demonstrated that FLASH therapy with VHEE beams of 70-130 MeV, could represent a promising alternative to standard radiotherapy allowing a comparable sparing of the healthy tissues. In the case of pancreatic cancer, the Dose Volume Histograms (DVH) showed how such a technique can be effective in sparing the duodenum. In case of lung cancers, the result showed how pulmonary tissue sparing can lead to a substantial reduction of pulmonary toxicity in comparison with the VMAT technique. In the case of pancreatic cancer and assuming a non-negligible contribution from the FLASH effect, the DVH showed how the duodenum healthy tissue sparing could allow a higher dose to be prescribed at the target while keeping the constraints respected, improving the therapeutic ratio. In the case of lung cancer, the advantages of the technique are additionally increased by the significant benefit that could be related to the treatment delivery time reduction (<1s) and to the corresponding advantage coming from a reduced organ movement that translates in a lower risk of lung toxicity.