Background: Small bowel malperfusion (SBM) can cause high morbidity and severe surgical consequences. However, there is no standardized objective measuring tool for the quantification of SBM. Indocyanine green (ICG) imaging can be used for visualization, but lacks standardization and objectivity. Hyperspectral imaging (HSI) as a newly emerging technology in medicine might present advantages over conventional ICG fluorescence or in combination with it. Methods: HSI baseline data from physiological small bowel, avascular small bowel and small bowel after intravenous application of ICG was recorded in a total number of 54 in-vivo pig models. Visualizations of avascular small bowel after mesotomy were compared between HSI only (1), ICG-augmented HSI (IA-HSI) (2), clinical evaluation through the eyes of the surgeon (3) and conventional ICG imaging (4). The primary research focus was the localization of resection borders as suggested by each of the four methods. Distances between these borders were measured and histological samples were obtained from the regions in between in order to quantify necrotic changes 6 h after mesotomy for every region. Results: StO₂ images (1) were capable of visualizing areas of physiological perfusion and areas of clearly impaired perfusion. However, exact borders where physiological perfusion started to decrease could not be clearly identified. Instead, IA-HSI (2) suggested a sharp-resection line where StO₂ values started to decrease. Clinical evaluation (3) suggested a resection line 23 mm (±7 mm) and conventional ICG imaging (4) even suggested a resection line 53 mm (±13 mm) closer towards the malperfused region. Histopathological evaluation of the region that was sufficiently perfused only according to conventional ICG (R3) already revealed a significant increase in pre-necrotic changes in 27% (±9%) of surface area. Therefore, conventional ICG seems less sensitive than IA-HSI with regards to detection of insufficient tissue perfusion. Conclusions: In this experimental animal study, IA-HSI (2) was superior for the visualization of segmental SBM compared to conventional HSI imaging (1), clinical evaluation (3) or conventional ICG imaging (4) regarding histopathological safety. ICG application caused visual artifacts in the StO₂ values of the HSI camera as values significantly increase. This is caused by optical properties of systemic ICG and does not resemble a true increase in oxygenation levels. However, this empirical finding can be used to visualize segmental SBM utilizing ICG as contrast agent in an approach for IA-HSI. Clinical applicability and relevance will have to be explored in clinical trials. Level of evidence: Not applicable. Translational animal science. Original article.

Introduction: Oncologic esophagectomy is a two-cavity procedure with considerable morbidity and mortality. Complex anatomy and the proximity to major vessels constitute a risk for massive intraoperative hemorrhage. Currently, there is no conclusive consensus on the ideal anesthesiologic countermeasure in case of such immense blood loss. The objective of this work was to identify the most promising anesthesiologic management in case of intraoperative hemorrhage with regards to tissue perfusion of the gastric conduit during esophagectomy using hyperspectral imaging. Material and methods: An established live porcine model (n=32) for esophagectomy was used with gastric conduit formation and simulation of a linear stapled side-to-side esophagogastrostomy. After a standardized procedure of controlled blood loss of about 1 l per pig, the four experimental groups (n=8 each) differed in anesthesiologic intervention, that is, (I) permissive hypotension, (II) catecholamine therapy using noradrenaline, (III) crystalloid volume supplementation, and (IV) combined crystalloid volume supplementation with noradrenaline therapy. Hyperspectral imaging tissue oxygenation (StO₂) of the gastric conduit was evaluated and correlated with systemic perfusion parameters. Measurements were conducted before (T0) and after (T1) laparotomy, after hemorrhage (T2), and 60 min (T3) and 120 min (T4) after anesthesiologic intervention. Results: StO₂ values of the gastric conduit showed significantly different results between the four experimental groups, with 63.3% (±7.6%) after permissive hypotension (I), 45.9% (±6.4%) after catecholamine therapy (II), 70.5% (±6.1%) after crystalloid volume supplementation (III), and 69.0% (±3.7%) after combined therapy (IV). StO₂ values correlated strongly with systemic lactate values (r=−0.67; CI −0.77 to −0.54), which is an established prognostic factor. Conclusion: Crystalloid volume supplementation (III) yields the highest StO₂ values and lowest systemic lactate values and therefore appears to be the superior primary treatment strategy after hemorrhage during esophagectomy with regards to microcirculatory tissue oxygenation of the gastric conduit.

Plastic-based additive manufacturing processes are becoming increasingly popular in the production of structural parts. Based on the idea of lightweight design and the aim of extending the functionality of additive structures, the production of additively manufactured foam structures has emerged as a new field of application. The optical characterisation of these structures is of particular importance for process adjustments and the identification of (unwanted) changes in the foam structure. The degree of foaming and the fineness of a foam structure are of interest at this point. In this context, only the part of a structure dominated by foam pores is considered a foam structure. So far, there are no sophisticated methods for such an optical characterisation. Therefore, in this work, microscope images of manufactured as well as artificially created additively manufactured foam structures were evaluated. On these images, the features porosity, pore size, pore amount and a measure for the textural change were determined in order to obtain information about changes within an additively manufactured foam structure. It is shown that additive structures show changing pore shapes depending on the orientation of the cutting plane, although there are no changes in the foaming behaviour. Therefore, caution is required when identifying changes within the foam structure. It was also found that, owing to the additive process, the total porosity is already set in the slicing process and remains constant even if the degree of foaming of individual tracks is changed. Therefore, the degree of foaming cannot be determined on the basis of the total porosity, but it can be assessed on the basis of the formation of large networks of process-related pores.

Ventricular tachycardia (VT) can be one cause of sudden cardiac death affecting 4.25 million persons per year worldwide. A curative treatment is catheter ablation in order to inactivate the abnormally triggering regions. To facilitate and expedite the localization during the ablation procedure, we present two novel localization techniques based on convolutional neural networks (CNNs). In contrast to existing methods, e.g. using ECG imaging, our approaches were designed to be independent of the patient-specific geometries and directly applicable to surface ECG signals, while also delivering a binary transmural position. One method outputs ranked alternative solutions. Results can be visualized either on a generic or patient geometry. The CNNs were trained on a data set containing only simulated data and evaluated both on simulated and clinical test data. On simulated data, the median test error was below 3mm. The median localization error on the clinical data was as low as 32mm. The transmural position was correctly detected in up to 82% of all clinical cases. Using the ranked alternative solutions, the top-3 median error dropped to 20mm on clinical data. These results demonstrate a proof of principle to utilize CNNs to localize the activation source without the intrinsic need of patient-specific geometrical information. Furthermore, delivering multiple solutions can help the physician to find the real activation source amongst more than one possible locations. With further optimization, these methods have a high potential to speed up clinical interventions. Consequently they could decrease procedural risk and improve VT patients' outcomes.

SignificanceHyperspectral imaging is a noninvasive, cost-effective modality with transformative clinical potential. Its adoption is limited by the lack of accurate and efficient methods that relate spectra to tissue parameters, essential for both AI training and validation of imaging methods, as gold standard Monte Carlo (MC) simulations remain prohibitively computationally expensive.AimWe aim to develop a scalable and accurate method for generating realistic tissue reflectance spectra in support of AI development and validation in biomedical imaging.ApproachWe trained a general-purpose neural surrogate model on >50 million MC simulations based on a flexible multilayer tissue model. We validated our model against >5000 open surgery in vivo hyperspectral images, annotated with 23 tissue classes for stratified performance analysis. In addition, we qualitatively evaluated clinical potential by testing whether surrogate-generated spectra enable recovery of organ-specific oxygenation dynamics in a controlled porcine aortic clamping experiment.ResultsThe surrogate model achieved accuracy matching MC simulations with 5–10 million photons while delivering inference five orders of magnitude faster. Across 140 million human tissue spectra, it improved spectral recall by 13–48 percentage points over existing surrogate models. Scaling analyses revealed a power law relationship between training dataset size and test error, enabling the prediction of training data requirements for target accuracy. Our porcine study suggests that the synthetic data generated with the surrogate model is suitable for recovering organ-specific stO2 trajectories.ConclusionNeural surrogate models can achieve MC-level accuracy and in vivo realism at negligible inference cost, enabling large-scale, compute-efficient data generation for biomedical optics and robust AI development for clinical applications.