arXiv subjects

Alberto Albiol

Publications and source records attributed to Alberto Albiol.

3 recordsLinked to original sources

arXiv2026

Real-Time Requirements and Transferability in Compton Imaging: From the Detector Chain to the Application

Compton cameras are proposed for tasks whose value decays with delay: verifying a range during irradiation, guiding an intervention, characterising an inaccessible volume, surveying a band no telescope covers. Whether a device can serve such a task is settled by the composition of its whole chain, while the literature that would answer the question is organised by stage -- so claims made at application level routinely rest on evidence obtained at component level. This review walks that chain, asking at each stage what binds first and what a reader can determine from the published text, and then asks what governs whether a capability transfers between groups and between application domains. The evidence is of two kinds: 83 full texts scored in context against defined markers, and, for five application domains measured alike, the size of the receiving literature, of the need it states, of the incumbent and of deployment commitment. Performance is reported at one operating point in 65 of 83 full texts, with count rate swept in none: the field reports values where transfer requires gradients. An accelerator is used in 39 works and a learned model in 31, while separability is discussed in 28, a memory footprint given in 9, and the cost of a precomputation or the inference time of a model in none. Across domains, neither the size of the receiving literature, nor of the incumbent, nor of the stated need orders the domains as deployment commitment does, while whether the incumbent can serve the task at all, and how many domain boundaries the output must cross, do so consistently. The distribution of publication is close to the inverse of the distribution of deployment commitment. We give the quantities a report must contain for a third party to judge whether a method fits an application it was not built for.

physics.med-ph↗

arXiv2026

Bounded-Latency Spherical-Histogram Reconstruction for Compton Cameras

Gamma-ray imaging with Compton cameras is computationally demanding because conventional reconstruction retains the list-mode acquisition inside the inversion loop: event-dependent cone/voxel interactions must be recomputed as the event count grows. We present a spherical-histogram framework in which each Compton event is encoded online into detector-centred angular histograms. Volumetric reconstruction is then performed from coherent histogram snapshots using a precomputed sparse projection operator. This turns the event stream into a bounded reconstruction state, decoupling event accumulation from iterative inversion. The method supports multi-view and multi-resolution operation, non-blocking acquisition, and iterative forward/backward reconstruction whose dominant cost depends.

physics.ins-det↗

arXiv2018

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

cs.CV↗