Longitudinal Segmentation Research Articles

Developing an alternative calorimeter solution for the future Muon Collider: The Crilin design

The Crilin calorimeter represents a novel approach in the development of electromagnetic calorimeters for future colliders, especially for a Muon Collider. This paper details the design and performance of the innovative semi-homogeneous Crilin calorimeter, highlighting its capabilities in mitigating beam-induced backgrounds (BIB) while maintaining excellent time resolution (less than 50 ps), longitudinal segmentation, and fine granularity. These performances are achieved throughout a series of stackable and interchangeable lead fluoride (PbF2) crystal matrices readout by surface-mount UV-extended Silicon Photo-multipliers. Simulated and experimental results demonstrate the Crilin design’s potential to work as an efficient and cost-effective alternative to traditional electromagnetic calorimeters. Crilin radiation tolerance is discussed, as measured in several irradiation campaigns, as well as timing performances during a beam test at CERN-H2 with 120 GeV electrons for the latest prototype, Proto-1. Additionally, a description of the results from a recent beam test conducted at the LNF Beam Test Facility with 450 MeV electrons is provided, aiming to measure light yield losses due to irradiation.

Performance of the electromagnetic and hadronic prototype segments of the ALICE Forward Calorimeter

We present the performance of a full-length prototype of the ALICE Forward Calorimeter (FoCal). The detector is composed of a silicon-tungsten electromagnetic sampling calorimeter with longitudinal and transverse segmentation (FoCal-E) of about 20X 0 and a hadronic copper-scintillating-fiber calorimeter (FoCal-H) of about 5λ int. The data were taken in various test beam campaigns between 2021 and 2023 at the CERN PS and SPS beam lines with hadron beams up to energies of 350 GeV, and electron beams up to 300 GeV. Regarding FoCal-E, we report a comprehensive analysis of its response to minimum ionizing particles across all pad layers, employing various operational modes including different pre-amplifier and bias voltage settings. The longitudinal shower profile of electromagnetic showers is measured with a layer-wise segmentation of 1X 0. As a projection to the performance of the final detector in electromagnetic showers, we demonstrate linearity in the full energy range, and show that the energy resolution fulfills the requirements for the physics needs. Additionally, the performance to separate two-showers events was studied by quantifying the transverse shower width. Regarding FoCal-H, we report a detailed analysis of the response to hadron beams between 60 and 350 GeV. The results are compared to simulations obtained with a Geant4 model of the test beam setup, which in particular for FoCal-E are in good agreement with the data. The energy resolution of FoCal-E was found to be lower than 3% at energies larger than 100 GeV. The response of FoCal-H to hadron beams was found to be linear, albeit with a significant intercept that is about factor 2 larger than in simulations. Its resolution, which is non-Gaussian and generally larger than in simulations, was quantified using the FWHM, and decreases from about 16% at 100 GeV to about 11% at 350 GeV. The discrepancy to simulations, which is particularly evident at low hadron energies, needs to be further investigated.

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. To inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality.

Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer's disease. However, image quality may vary in MRI. Poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images. MRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. To compare our proposed model with a baseline single-modality model in the full dataset with mixed high/poor image quality, we evaluated the ability of derived volume/thickness measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and Amyloid- cognitively unimpaired (A-CU) groups, as well as the stability of these measurements in longitudinal data. The multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. The multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.

Quantifying chronic lesion expansion in multiple sclerosis: Exploring imaging markers for longitudinal assessment

ObjectivesGradual expansion of multiple sclerosis lesions over time is known to have a significant impact on disease progression. However, accurately quantifying the volume changes in chronic lesions presents challenges due to their slow rate of progression and the need for longitudinal segmentation. Our study addresses this by estimating the expansion of chronic lesions using data collected over a 1–2 year period and exploring imaging markers that do not require longitudinal lesion segmentation. MethodsPre- and post-gadolinium 3D-T1, 3D FLAIR and diffusion tensor images were acquired from 42 patients with MS. Lesion expansion, stratified by the severity of tissue damage as measured by mean diffusivity change, was analysed between baseline and 48 months (Progressive Volume/Severity Index, PVSI). Central brain atrophy (CBA) and the degree of tissue loss inside chronic lesions (measured by the change of T1 intensity and mean diffusivity (MD)) were used as surrogate markers. ResultsCBA measured after 2 years of follow-up estimated lesion expansion at 4 years with a high degree of accuracy (r = 0.82, p < 0.001, ROC area under the curve 0.92, sensitivity of 94 %, specificity of 85 %). Increased MD within chronic lesions measured over 2 years was strongly associated with future expansion (r = 0.77, p < 0.001, ROC area under the curve 0.87, sensitivity of 81 % and specificity of 81 %). In contrast, change in lesion T1 hypointensity poorly explained future PVSI (best sensitivity and specificity 60 % and 59 % respectively). InterpretationCBA and, to a lesser extent, the change in MD within chronic MS lesions, measured over a period of 2 years, can provide a reliable and sensitive estimate of the extent and severity of chronic lesion expansion.

Differential atrophy along the longitudinal hippocampal axis in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the hippocampus. Since hippocampal studies have highlighted a differential subregional regulation along its longitudinal axis, a more detailed analysis addressing subregional changes along the longitudinal hippocampal axis has the potential to provide new relevant biomarkers. This study included structural brain MRI data of 583 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitively normal (CN) subjects, mild cognitively impaired (MCI) subjects and AD patients were conveniently selected considering the age and sex match between clinical groups. Structural MRI acquisitions were pre-processed and analysed with a new longitudinal axis segmentation method, dividing the hippocampus in three subdivisions (anterior, intermediate, and posterior). When normalizing the volume of hippocampal sub-divisions to total hippocampus, the posterior hippocampus negatively correlates with age only in CN subjects (r= -.31). The longitudinal ratio of hippocampal atrophy (anterior sub-division divided by the posterior one) shows a significant increase with age only in CN (r= .25). Overall, in AD, the posterior hippocampus is predominantly atrophied early on. Consequently, the anterior/posterior hippocampal ratio is an AD differentiating metric at early disease stages with potential for diagnostic and prognostic applications.

Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression

Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multiple independent scans. To accurately segment the hippocampal morphology from longitudinal 3T T1-weighted MR images, we propose a diffeomorphic geodesic guided deep learning method called the GeoLongSeg to mitigate the longitudinal variabilities that unrelated to diseases by enhancing intra-individual morphological consistency. Specifically, we integrate geodesic shape regression, an evolutional model that estimates smooth deformation process of anatomical shapes, into a two-stage segmentation network. We adopt a 3D U-Net in the first-stage network with an enhanced attention mechanism for independent segmentation. Then, a hippocampal shape evolutional trajectory is estimated by geodesic shape regression and fed into the second network to refine the independent segmentation. We verify that GeoLongSeg outperforms other four state-of-the-art segmentation pipelines in longitudinal morphological consistency evaluated by test–retest reliability, variance ratio and atrophy trajectories. When assessing hippocampal atrophy in longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), results based on GeoLongSeg exhibit spatial and temporal local atrophy in bilateral hippocampi of dementia patients. These features derived from GeoLongSeg segmentation exhibit the greatest discriminatory capability compared to the outcomes of other methods in distinguishing between patients and normal controls. Overall, GeoLongSeg provides an accurate and efficient segmentation network for extracting hippocampal morphology from longitudinal MR images, which assist precise atrophy measurement of the hippocampus in early stage of dementia.

Cognitive resilience at 3 years after stroke depends on pre‐ and post‐stroke factors

AbstractBackgroundThe incidence of dementia in stroke survivors is greater than in stroke‐free people. However, some stroke survivors show cognitive resilience, withstanding the detrimental effects of stroke. In this study, we explored the differences between cognitively‐impaired (CI) and cognitively‐normal (CN) survivors in comparison with healthy controls (HC).MethodParticipants were sampled from the Cognition And Neocortical Volume After Stroke (CANVAS) study. Sociodemographic, clinical, neuropsychological, cognitive, neuroimaging, sleep, and physical activity data were obtained for 93 stroke participants at 3‐months and 3‐years post‐stroke and for 35 HC at equivalent timepoints. Stroke participants were classified as CN (n = 71) or CI (n = 22) at 3‐years based on mood scores, clinical interviews, and z‐scores of performance in five cognitive domains: attention, memory, language, executive and visuospatial functions. Structural brain volumes were estimated from T1‐weighted MRI using longitudinal FreeSurfer segmentation. Linear random‐intercept mixed‐effects modelling was used to compare between groups and timepoints accounting for differences in age, sex, years‐of‐education, and total‐intracranial volume.ResultThe stroke group overall had higher incidence of atrial fibrillation, scored higher on anxiety and depression, and had lower sleep efficiency compared to HC. Stroke survivors had thinner cortex, less cerebellar grey‐matter (GM), and larger choroid plexuses, and greater enlargement of the ventricles, at both timepoints. The CI survivors had more comorbidities, were older, were less active at both timepoints, and had lower years‐of‐education compared to both CN survivors and HC. They had more vascular risk factors and APOE‐ε4 allele carriage status compared to controls.Only the CI group showed deficits in ipsilesional cortical GM, hippocampal and thalamic volumes, and reduced total brain volume at 3‐months compared to controls. The CI survivors had larger stroke lesions compared to CN survivors and showed a greater increase in white matter hyperintensities between timepoints.At 3 months in CN survivors, cognitive performance in assessed domains was significantly lower, except for language. While performance in the CN survivors improved over time especially in language and visuospatial function, cognitive scores remained lower in the CI survivors, with a decline in memory scores.ConclusionPost‐stroke cognitive resilience appears dependent on clinical, sociodemographic, and neuroimaging factors, which assist in post‐stroke prognostication.

Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection

In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented. The method is based on an U-Net like convolutional neural network (CNN) for automated 2D slice-based-segmentation of brain MRI volumes. The individual modalities are encoded in separate downsampling branches without weight sharing, to leverage the specific features. Skip connections input feature maps to multi-scale feature fusion (MSFF) blocks at every decoder stage of the network. Those are followed by multi-scale feature upsampling (MSFU) blocks which use the information about lesion shape and location. The CNN is evaluated on two publicly available datasets: The ISBI 2015 longitudinal MS lesion segmentation challenge dataset containing 19 subjects and the MICCAI 2016 MSSEG challenge dataset containing 15 subjects from various scanners. The proposed multi-input 2D architecture is among the top performing approaches in the ISBI challenge, to which open-access papers are available, is able to outperform state-of-the-art 3D approaches without additional post-processing, can be adapted to other scanners quickly, is robust against scanner variability and can be deployed for inference even on a standard laptop without a dedicated GPU.

Seismotectonic role of transversal structures in the Plio-Quaternary evolution of the external Marche Apennines (Italy)

We defined the seismotectonic setting of the external Marche Apennines (central Italy) by integrating geomorphological, structural and seismological data. The area is characterized by strong historical earthquakes, but the geometries and kinematics of the seismogenic sources have not previously been well defined. Plio-Quaternary Apennine compressional NW–SE structures are segmented by NE–SW-oriented transversal faults, the origin and role of which are still debated. We characterized the geometry, kinematics and activity of the four main transversal faults to better define their seismogenic potential. These high-angle, deeply rooted transversal fault systems have been mapped and separate the external Apennine sector into blocks and sub-blocks with different structural and evolutionary features. The integrated dataset showed that some inherited fault segments have recently been reactivated, displacing Quaternary deposits. The spatial distribution of seismicity indicates that some clusters of hypocentres are located within the basement. Stress field analysis using available focal mechanism solutions confirms the prevalence of left-lateral kinematics on roughly SW–NE-oriented structures. The transversal structures thus contribute to the longitudinal segmentation of the Apennine structures and, more generally, are involved in the northern part of the Adria plate kinematics towards the north and NNW. The assessment of seismic hazards and planning to mitigate risks in populated areas close to the Adriatic coast should consider these potentially active faults evidenced by instrumental seismicity and important historical earthquakes.

Evaluating automated longitudinal tumor measurements for glioblastoma response assessment.

Automated tumor segmentation tools for glioblastoma show promising performance. To apply these tools for automated response assessment, longitudinal segmentation, and tumor measurement, consistency is critical. This study aimed to determine whether BraTumIA and HD-GLIO are suited for this task. We evaluated two segmentation tools with respect to automated response assessment on the single-center retrospective LUMIERE dataset with 80 patients and a total of 502 post-operative time points. Volumetry and automated bi-dimensional measurements were compared with expert measurements following the Response Assessment in Neuro-Oncology (RANO) guidelines. The longitudinal trend agreement between the expert and methods was evaluated, and the RANO progression thresholds were tested against the expert-derived time-to-progression (TTP). The TTP and overall survival (OS) correlation was used to check the progression thresholds. We evaluated the automated detection and influence of non-measurable lesions. The tumor volume trend agreement calculated between segmentation volumes and the expert bi-dimensional measurements was high (HD-GLIO: 81.1%, BraTumIA: 79.7%). BraTumIA achieved the closest match to the expert TTP using the recommended RANO progression threshold. HD-GLIO-derived tumor volumes reached the highest correlation between TTP and OS (0.55). Both tools failed at an accurate lesion count across time. Manual false-positive removal and restricting to a maximum number of measurable lesions had no beneficial effect. Expert supervision and manual corrections are still necessary when applying the tested automated segmentation tools for automated response assessment. The longitudinal consistency of current segmentation tools needs further improvement. Validation of volumetric and bi-dimensional progression thresholds with multi-center studies is required to move toward volumetry-based response assessment.

Beam test, simulation, and performance evaluation of PbF2 and PWO-UF crystals with SiPM readout for a semi-homogeneous calorimeter prototype with longitudinal segmentation

Crilin (Crystal Calorimeter with Longitudinal Information) is a semi-homogeneous, longitudinally segmented electromagnetic calorimeter based on high-Z, ultra-fast crystals with UV-extended SiPM readout. The Crilin design has been proposed as a candidate solution for both a future Muon Collider barrel ECAL and for the Small Angle Calorimeter of the HIKE experiment. As a part of the Crilin development program, we have carried out beam tests of small (10 × 10 × 40 mm3) lead fluoride (PbF2) and ultra-fast lead tungstate (PbWO4, PWO-UF) crystals with 120 GeV electrons at the CERN SPS to study the light yield, timing response, and systematics of light collection with a proposed readout scheme. For a single crystal of PbF2, corresponding to a single Crilin cell, a time resolution of better than 25 ps is obtained for&gt;3 GeV of deposited energy. For a single cell of PWO-UF, a time resolution of better than 45 ps is obtained for the same range of deposited energy. This timing performance fully satisfies the design requirements for the Muon Collider and HIKE experiments. Further optimizations of the readout scheme and crystal surface preparation are expected to bring further improvements.

Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20–300 GeV/c

The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly read out by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data.

Cerebrospinal fluid soluble TREM2 is associated with accelerated AD‐related longitudinal neurodegeneration in preclinical Alzheimer’s disease

AbstractBackgroundCerebrospinal fluid (CSF) concentration of soluble TREM2 (sTREM2), a potential biomarker for microglial activation, is associated with attenuated longitudinal neurodegeneration and cognitive decline in Alzheimer’s disease (AD), but data in early disease are lacking. This study’s purpose was to use longitudinal volumetric imaging to assess the association of sTREM2 with age‐ and preclinical AD‐related grey matter (GM) changes.MethodCognitively unimpaired participants (N = 384; amyloid‐positive N = 82) from the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin ADRC clinical core studies with baseline CSF biomarker and subsequent longitudinal T1‐weighted magnetic resonance imaging data were analyzed. CSF sTREM2 and phosphorylated‐tau181/amyloid‐beta1‐42 ratio (pTau/Aβ42) were measured using the NeuroToolKit panel of robust prototype assays (Roche Diagnostics International Ltd, Rotkreuz, Switzerland). T1‐weighted images were longitudinally registered to intra‐subject templates and segmented to create 58 GM regions of interest (ROIs) via the CAT12 longitudinal segmentation pipeline. Linear mixed‐effects models (random participant intercepts and age slopes) testing a three‐way interaction between time‐varying age, sTREM2, and pTau/Aβ42 to predict regional grey matter changes with all potential two‐way interactions and simple effects (adjusted for gender, years of education, intracranial volume, and head coil) were tested. In the event of a non‐significant three‐way interaction, the three‐way interaction was dropped and the model was reinterpreted. Statistical significance was considered at p &lt; .05, uncorrected for multiple comparisons.ResultAge had a negative effect on regional GM volume across the brain and showed widespread interactions with pTau/Aβ42, indicative of accelerated decline with AD pathology. Negative three‐way interactions between sTREM2, pTau/Aβ42, and age were evident in the angular, supramarginal, lingual, and middle occipital gyri, predicting accelerated AD‐related longitudinal neurodegeneration with higher sTREM2 concentration. Negative two‐way interactions between pTau/Aβ42 and sTREM2 were evident in the supplementary motor cortex and superior frontal gyrus, indicating worse effects of AD‐pathology with higher sTREM2, regardless of age.ConclusionOverall, higher sTREM2 may be associated with accelerated AD‐related neurodegeneration over time in the context of preclinical AD, particularly in posterior ROIs. Higher sTREM2 and underlying microglial activation may denote individuals at higher risk of experiencing the deleterious effects of early AD pathology on the brain.

Longitudinal deep network for consistent OCT layer segmentation.

Retinal layer thickness is an important bio-marker for people with multiple sclerosis (PwMS). In clinical practice, retinal layer thickness changes in optical coherence tomography (OCT) are widely used for monitoring multiple sclerosis (MS) progression. Recent developments in automated retinal layer segmentation algorithms allow cohort-level retina thinning to be observed in a large study of PwMS. However, variability in these results make it difficult to identify patient-level trends; this prevents patient specific disease monitoring and treatment planning using OCT. Deep learning based retinal layer segmentation algorithms have achieved state-of-the-art accuracy, but the segmentation is performed on each individual scan without utilizing longitudinal information, which can be important in reducing segmentation error and reveal subtle changes in retinal layers. In this paper, we propose a longitudinal OCT segmentation network which achieves more accurate and consistent layer thickness measurements for PwMS.

R&amp;D status for an innovative crystal calorimeter for the future Muon Collider

The measurement of physics processes at new energy frontier experiments requires excellent spatial, time, and energy resolutions to resolve the structure of collimated high-energy jets. Calorimeters, as other detectors, must face this increasing performance demand. In a future TeV-scale Muon Collider, the beam-induced background (BIB) represents the main challenge in the design of the detectors and of the event reconstruction algorithms and can pose serious limitations to the physics performance. However, it is possible to reduce the BIB impact on the Muon Collider calorimeter by exploiting some of its characteristics and by ensuring high granularity, excellent timing, longitudinal segmentation and good energy resolution. The proposed R&amp;D is an innovative semi-homogeneous electromagnetic calorimeter based on stackable modules of lead fluoride crystals (PbF2) readout by surface-mount UV-extended Silicon Photomultipliers (SiPMs): the Crilin calorimeter (CRystal calorImeter with Longitudinal INformation). The calorimeter should operate in a very harsh radiation environment, withstanding yearly a neutron flux of 1014 n1MeV /cm2 and a dose of 100 krad. In this paper, the radiation tolerance measured in several irradiation campaigns and the timing performances evaluated during a test beam at CERN-H2 with 120-GeV electron are discussed. A description of the latest prototype Proto-1, that will be shortly tested, is also provided.

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.

Commissioning of new FHCal at BM@N experiment

The BM@N (Baryonic Matter at Nuclotron) is the fixed target experiment at NICA-Nuclotron (JINR, Dubna, Russia) accelerator complex. The main goal of the experiment is studying the properties of dense nuclear matter produced in ion-ion collisions at the energies of up to 4 AGeV. New Forward Hadron Calorimeter (FHCal) with modular structure and a beam hole in the center has been developed and constructed to measure the collision centrality after the BM@N upgrade. The transverse and longitudinal segmentation of the FHCal allows to perform calibration of the calorimeter with cosmic muons.FHCal modules have lead/scintillator sampling structure with longitudinal segmentation. Light signals from the sections are collected with MPPCs, amplified and read-out by ADC boards. Fast analog signals are collected for trigger system.The status of development and construction of the new FHCal calorimeter for the BM@N experiment is presented. Performance of FHCal front-end and readout systems is discussed.

Commissioning of the readout chain of the CBM Projectile Spectator Detector at FAIR

The forward hadron calorimeter Projectile Spectator Detector (PSD) will be used at the CBM experiment at FAIR to measure the nucleus-nucleus collision centrality and orientation of the reaction plane. The PSD is a sampling lead/scintillator calorimeter with modular transverse structure and longitudinal segmentation of modules. Light readout from all sections of each module is provided by Hamamatsu MicroPixel Photon Counters installed at the rear of the module. The full chain of the Front-End and readout electronics of one PSD module — mPSD has been assembled, integrated into mCBM (mini-CBM) trigger-less data acquisition system and tested at mCBM test runs in Summer 2021. The PSD Front-End and readout electronics integration into mCBM within the common synchronized CBM data transport system is discussed.

Mechanical Design of an Electromagnetic Calorimeter Prototype for a Future Muon Collider

Measurement of physics processes at new energy frontier experiments requires excellent spatial, time, and energy resolutions to resolve the structure of collimated high-energy jets. In a future Muon Collider, beam-induced backgrounds (BIB) represent the main challenge in the design of the detectors and of the event reconstruction algorithms. The technology and the design of the calorimeters should be chosen to reduce the effect of the BIB, while keeping good physics performance. Several requirements can be inferred: (i) high granularity to reduce the overlap of BIB particles in the same calorimeter cell; (ii) excellent timing (of the order of 100 ps) to reduce the out-of-time component of the BIB; (iii) longitudinal segmentation to distinguish the signal showers from the fake showers produced by the BIB. Moreover, the calorimeter should operate in a very harsh radiation environment, withstanding yearly a neutron flux of 1014 n1MeV/cm2 and a dose of 100 krad. Our proposal consists of a semi-homogeneous electromagnetic calorimeter based on Lead Fluoride Crystals (PbF2) readout by surface-mount UV-extended Silicon Photomultipliers (SiPMs): the Crilin calorimeter. In this paper, we report the mechanical design for the development of a small-scale prototype, consisting of 2 layers of 3 × 3 crystals.