Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Getting the best outcomes from epilepsy surgery.

Neurosurgery is an underutilized treatment that can potentially cure drug-refractory epilepsy. Careful, multidisciplinary presurgical evaluation is vital for selecting patients and to ensure optimal outcomes. Advances in neuroimaging have improved diagnosis and guided surgical intervention. Invasive electroencephalography allows the evaluation of complex patients who would otherwise not be candidates for neurosurgery. We review the current state of the assessment and selection of patients and consider established and novel surgical procedures and associated outcome data. We aim to dispel myths that may inhibit physicians from referring and patients from considering neurosurgical intervention for drug-refractory focal epilepsies. Ann Neurol 2018;83:676-690

Multi Modality Brain Mapping System (MBMS) Using Artificial Intelligence and Pattern Recognition

A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image

Computer-Assisted Planning and Robotics in Epilepsy Surgery

Epilepsy is a severe and devastating condition that affects ~1% of the population. Around 30% of these patients are drug-refractory. Epilepsy surgery may provide a cure in selected individuals with drug-resistant focal epilepsy if the epileptogenic zone can be identified and safely resected or ablated. Stereoelectroencephalography (SEEG) is a diagnostic procedure that is performed to aid in the delineation of the seizure onset zone when non-invasive investigations are not sufficiently informative or discordant. Utilizing a multi-modal imaging platform, a novel computer-assisted planning (CAP) algorithm was adapted, applied and clinically validated for optimizing safe SEEG trajectory planning. In an initial retrospective validation study, 13 patients with 116 electrodes were enrolled and safety parameters between automated CAP trajectories and expert manual plans were compared. The automated CAP trajectories returned statistically significant improvements in all of the compared clinical metrics including overall risk score (CAP 0.57 +/- 0.39 (mean +/- SD) and manual 1.00 +/- 0.60, p < 0.001). Assessment of the inter-rater variability revealed there was no difference in external expert surgeon ratings. Both manual and CAP electrodes were rated as feasible in 42.8% (42/98) of cases. CAP was able to provide feasible electrodes in 19.4% (19/98), whereas manual planning was able to generate a feasible electrode in 26.5% (26/98) when the alternative generation method was not feasible. Based on the encouraging results from the retrospective analysis a prospective validation study including an additional 125 electrodes in 13 patients was then undertaken to compare CAP to expert manual plans from two neurosurgeons. The manual plans were performed separately and blindly from the CAP. Computer-generated trajectories were found to carry lower risks scores (absolute difference of 0.04 mm (95% CI = -0.42-0.01), p = 0.04) and were subsequently implanted in all cases without complication. The pipeline has been fully integrated into the clinical service and has now replaced manual SEEG planning at our institution. Further efforts were then focused on the distillation of optimal entry and target points for common SEEG trajectories and applying machine learning methods to develop an active learning algorithm to adapt to individual surgeon preferences. Thirty-two patients were prospectively enrolled in the study. The first 12 patients underwent prospective CAP planning and implantation following the pipeline outlined in the previous study. These patients were used as a training set and all of the 108 electrodes after successful implantation were normalized to atlas space to generate ‘spatial priors’, using a K-Nearest Neighbour (K-NN) classifier. A subsequent test set of 20 patients (210 electrodes) were then used to prospectively validate the spatial priors. From the test set, 78% (123/157) of the implanted trajectories passed through both the entry and target spatial priors defined from the training set. To improve the generalizability of the spatial priors to other neurosurgical centres undertaking SEEG and to take into account the potential for changing institutional practices, an active learning algorithm was implemented. The K-NN classifier was shown to dynamically learn and refine the spatial priors. The progressive refinement of CAP SEEG planning outlined in this and previous studies has culminated in an algorithm that not only optimizes the surgical heuristics and risk scores related to SEEG planning but can also learn from previous experience. Overall, safe and feasible trajectory schema were returning in 30% of the time required for manual SEEG planning. Computer-assisted planning was then applied to optimize laser interstitial thermal therapy (LITT) trajectory planning, which is a minimally invasive alternative to open mesial temporal resections, focal lesion ablation and anterior 2/3 corpus callosotomy. We describe and validate the first CAP algorithm for mesial temporal LITT ablations for epilepsy treatment. Twenty-five patients that had previously undergone LITT ablations at a single institution and with a median follow up of 2 years were included. Trajectory parameters for the CAP algorithm were derived from expert consensus to maximize distance from vasculature and ablation of the amygdalohippocampal complex, minimize collateral damage to adjacent brain structures whilst avoiding transgression of the ventricles and sulci. Trajectory parameters were also optimized to reduce the drilling angle to the skull and overall catheter length. Simulated cavities attributable to the CAP trajectories were calculated using a 5-15 mm ablation diameter. In comparison to manually planned and implemented LITT trajectories,CAP resulted in a significant increase in the percentage ablation of the amygdalohippocampal complex (manual 57.82 +/- 15.05% (mean +/- S.D.) and unablated medial hippocampal head depth (manual 4.45 +/- 1.58 mm (mean +/- S.D.), CAP 1.19 +/- 1.37 (mean +/- S.D.), p = 0.0001). As LITT ablation of the mesial temporal structures is a novel procedure there are no established standards for trajectory planning. A data-driven machine learning approach was, therefore, applied to identify hitherto unknown CAP trajectory parameter combinations. All possible combinations of planning parameters were calculated culminating in 720 unique combinations per patient. Linear regression and random forest machine learning algorithms were trained on half of the data set (3800 trajectories) and tested on the remaining unseen trajectories (3800 trajectories). The linear regression and random forest methods returned good predictive accuracies with both returning Pearson correlations of ρ = 0.7 and root mean squared errors of 0.13 and 0.12 respectively. The machine learning algorithm revealed that the optimal entry points were centred over the junction of the inferior occipital, middle temporal and middle occipital gyri. The optimal target points were anterior and medial translations of the centre of the amygdala. A large multicenter external validation study of 95 patients was then undertaken comparing the manually planned and implemented trajectories, CAP trajectories targeting the centre of the amygdala, the CAP parameters derived from expert consensus and the CAP trajectories utilizing the machine learning derived parameters. Three external blinded expert surgeons were then selected to undertake feasibility ratings and preference rankings of the trajectories. CAP generated trajectories result in a significant improvement in many of the planning metrics, notably the risk score (manual 1.3 +/- 0.1 (mean +/- S.D.), CAP 1.1 +/- 0.2 (mean +/- S.D.), p<0.000) and overall ablation of the amygdala (manual 45.3 +/- 22.2 % (mean +/- S.D.), CAP 64.2 +/- 20 % (mean +/- S.D.), p<0.000). Blinded external feasibility ratings revealed that manual trajectories were less preferable than CAP planned trajectories with an estimated probability of being ranked 4th (lowest) of 0.62. Traditional open corpus callosotomy requires a midline craniotomy, interhemispheric dissection and disconnection of the rostrum, genu and body of the corpus callosum. In cases where drop attacks persist a completion corpus callosotomy to disrupt the remaining fibres in the splenium is then performed. The emergence of LITT technology has raised the possibility of being able to undertake this procedure in a minimally invasive fashion and without the need for a craniotomy using two or three individual trajectories. Early case series have shown LITT anterior two-thirds corpus callosotomy to be safe and efficacious. Whole-brain probabilistic tractography connectomes were generated utilizing 3-Tesla multi-shell imaging data and constrained spherical deconvolution (CSD). Two independent blinded expert neurosurgeons with experience of performing the procedure using LITT then planned the trajectories in each patient following their current clinical practice. Automated trajectories returned a significant reduction in the risk score (manual 1.3 +/- 0.1 (mean +/- S.D.), CAP 1.1 +/- 0.1 (mean +/- S.D.), p<0.000). Finally, we investigate the different methods of surgical implantation for SEEG electrodes. As an initial study, a systematic review and meta-analysis of the literature to date were performed. This revealed a wide variety of implantation methods including traditional frame-based, frameless, robotic and custom-3D printed jigs were being used in clinical practice. Of concern, all comparative reports from institutions that had changed from one implantation method to another, such as following the introduction of robotic systems, did not undertake parallel-group comparisons. This suggests that patients may have been exposed to risks associated with learning curves and potential harms related to the new device until the efficacy was known. A pragmatic randomized control trial of a novel non-CE marked robotic trajectory guidance system (iSYS1) was then devised. Before clinical implantations began a series of pre-clinical investigations utilizing 3D printed phantom heads from previously implanted patients was performed to provide pilot data and also assess the surgical learning curve. The surgeons had comparatively little clinical experience with the new robotic device which replicates the introduction of such novel technologies to clinical practice. The study confirmed that the learning curve with the iSYS1 devices was minimal and the accuracies and workflow were similar to the conventional manual method. The randomized control trial represents the first of its kind for stereotactic neurosurgical procedures. Thirty-two patients were enrolled with 16 patients randomized to the iSYS1 intervention arm and 16 patients to the manual implantation arm. The intervention allocation was concealed from the patients. The surgical and research team could be not blinded. Trial management, independent data monitoring and trial steering committees were convened at four points doing the trial (after every 8 patients implanted). Based on the high level of accuracy required for both methods, the main distinguishing factor would be the time to achieve the alignment to the prespecified trajectory. The primary outcome for comparison, therefore, was the time for individual SEEG electrode implantation. Secondary outcomes included the implantation accuracy derived from the post-operative CT scan, infection, intracranial haemorrhage and neurological deficit rates. Overall, 32 patients (328 electrodes) completed the trial (16 in each intervention arm) and the baseline demographics were broadly similar between the two groups. The time for individual electrode implantation was significantly less with the iSYS1 device (median of 3.36 (95% CI 5.72 to 7.07) than for the PAD group (median of 9.06 minutes (95% CI 8.16 to 10.06), p=0.0001). Target point accuracy was significantly greater with the PAD (median of 1.58 mm (95% CI 1.38 to 1.82) compared to the iSYS1 (median of 1.16 mm (95% CI 1.01 to 1.33), p=0.004). The difference between the target point accuracies are not clinically significant for SEEG but may have implications for procedures such as deep brain stimulation that require higher placement accuracy. All of the electrodes achieved their respective intended anatomical targets. In 12 of 16 patients following robotic implantations, and 10 of 16 following manual PAD implantations a seizure onset zone was identified and resection recommended. The aforementioned systematic review and meta-analysis were updated to include additional studies published during the trial duration. In this context, the iSYS1 device entry and target point accuracies were similar to those reported in other published studies of robotic devices including the ROSA, Neuromate and iSYS1. The PAD accuracies, however, outperformed the previously published results for other frameless stereotaxy methods. In conclusion, the presented studies report the integration and validation of a complex clinical decision support software into the clinical neurosurgical workflow for SEEG planning. The stereotactic planning platform was further refined by integrating machine learning techniques and also extended towards optimisation of LITT trajectories for ablation of mesial temporal structures and corpus callosotomy. The platform was then used to seamlessly integrate with a novel trajectory planning software to effectively and safely guide the implantation of the SEEG electrodes. Through a single-blinded randomised control trial, the ISYS1 device was shown to reduce the time taken for individual electrode insertion. Taken together, this work presents and validates the first fully integrated stereotactic trajectory planning platform that can be used for both SEEG and LITT trajectory planning followed by surgical implantation through the use of a novel trajectory guidance system

\u3ci\u3e Phenytoin reduces 5-ala mediated fluorescence in glioblastoma cells \u3c/i\u3e

Glioblastoma multiforme (GBM) is a devastating form of cancer, and essentially all GBM tumors recur causing fatality. A new surgical technique, fluorescence-guided resection of GBM using 5-aminolevulinic acid (5-ala), improves the extent of resection and positively impacts the length and quality of patient survival. The fluorescence achieved in neoplastic tissue depends directly on the accumulation of porphyrins derived from the metabolism of the 5-ala prodrug within the cancer cell. However, 5-ala induced fluorescence has been reported to be inconsistent. In an effort to determine the cause of the inconsistent fluorescence, the authors investigated the effect of medications commonly prescribed to brain tumor patients on 5-ala induced fluorescence. A model was developed to quantify intracellular porphyrin accumulation using a U87MG GBM cell line constitutively expressing yellow fluorescent protein (YFP-U87). 5-ala mediated fluorescence within the cells was standardized to cell number via the fluorescence emission spectra ratio of porphyrin (405 nm) to YFP (525 nm). 5-ala induced accumulation of porphyrins was measured after treating YFP-U87 cells with phenytoin, dexamethasone, or desipramine for 3 days. After a 6 hour incubation with 5-ala, no significant difference in porphyrin accumulation was observed in cells treated with dexamethasone or desipramine. Phenytoin, however, significantly reduced the accumulation of fluorescent porphyrins within the YFP-U87 cell line by nearly 30% compared to the control. To optimize fluorescence during surgery and improve patient survival these results suggest that further investigations are warranted to determine the effects of commonly administered medications on 5-ala fluorescence-guided resection of GBM

Heating technology for malignant tumors: a review

The therapeutic application of heat is very effective in cancer treatment. Both hyperthermia, i.e., heating to 39-45 degrees C to induce sensitization to radiotherapy and chemotherapy, and thermal ablation, where temperatures beyond 50 degrees C destroy tumor cells directly are frequently applied in the clinic. Achievement of an effective treatment requires high quality heating equipment, precise thermal dosimetry, and adequate quality assurance. Several types of devices, antennas and heating or power delivery systems have been proposed and developed in recent decades. These vary considerably in technique, heating depth, ability to focus, and in the size of the heating focus. Clinically used heating techniques involve electromagnetic and ultrasonic heating, hyperthermic perfusion and conductive heating. Depending on clinical objectives and available technology, thermal therapies can be subdivided into three broad categories: local, locoregional, or whole body heating. Clinically used local heating techniques include interstitial hyperthermia and ablation, high intensity focused ultrasound (HIFU), scanned focused ultrasound (SFUS), electroporation, nanoparticle heating, intraluminal heating and superficial heating. Locoregional heating techniques include phased array systems, capacitive systems and isolated perfusion. Whole body techniques focus on prevention of heat loss supplemented with energy deposition in the body, e.g., by infrared radiation. This review presents an overview of clinical hyperthermia and ablation devices used for local, locoregional, and whole body therapy. Proven and experimental clinical applications of thermal ablation and hyperthermia are listed. Methods for temperature measurement and the role of treatment planning to control treatments are discussed briefly, as well as future perspectives for heating technology for the treatment of tumors

Laser stimulated dynamic thermal imaging system for tumor detection

Laser stimulated dynamic thermal imaging system for tumor detectionby Hongyu Meng Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2021 Professor Samuel Achilefu, Chair Recent advances in infrared sensor technology have enabled the rapid application of thermal imaging in materials science, security and medicine. Relying on the infrared characteristics of living systems, thermal imaging has been used to generate individual heat maps, detect inflammation and tumor. As an imaging system, thermal imaging has the advantages of portability, real-time, non-invasive, and non-contact. But the low specificity of thermal imaging hinders its wide clinical application. Unfortunately, label-free DTI is less able to fully capture thermal tissue heterogeneity in high resolution due partly to how thermal stimulation is applied. Current DTI methods apply thermal stimulation to a large area of tissue, which obscures the detection of the unique thermal characteristics of a small area in the thermally disturbed area. Super-resolution DTI grating can improve the spatial resolution, but the system setup is complex. For biological samples, the use of exogenous contrast agents can enhance contrast, but contrast agents increase regulatory hurdles in clinical trials. In this work, we have developed a focused dynamic laser stimulation imaging (FDTI) system to overcome these limitations. The system, which has high resolution, high speed and large field of view uses a short wavelength laser to stimulate small tissue area and a thermal camera to acquire data. We captured thermal images and videos, extracted features, and built classifiers to distinguish tumors from normal tissues. Data analysis showed that FDTI method achieved high accuracy (classifier surpassed 90%) with spatial resolution attaining 1 mm, which surpasses conventional thermal imaging and DTI. We next explored the ability of FDTI to detect early-stage tumors by scanning multiple areas that exhibited normal thermal images with conventional thermal imaging. A bioluminescence imaging (BLI) system was then used to locate the tumor, which was co-registered to the FDTI images to determine the position of the laser spot. By extracting features from the collected thermal images and videos and constructing the classifier, the FDTI system achieved an accuracy greater than 80% in detecting early tumors in different mouse tumor models. Subsequently, the FDTI system was optimized to improve its acquisition speed, automation and robustness. First, we analyzed the influencing factors of imaging and proposed new system hardware designs to improve the data acquisition speed. Then, to shorten the acquisition time from the software level, we tested and analyzed the performance of features at different stages during the acquisition process. We also designed and tested registration markers, including registration results of different features, feature robustness under interference, marker detection from the background, and marker performance in motion correction to improve the degree of automation of the system. Furthermore, we tested the performance of thermal imaging applications in other research fields, including brain tumor detection, nerve damage assessment, and whether temperature changes correlate with stroke. These results show that FDTI is a promising technique for enhancing contrast, improving spatial resolution, determining underlying tumor heterogeneity, and detecting tumors at stages when conventional thermal imaging is ineffective. This work lays a strong foundation for diverse applications and clinical translation of FDTI to address unmet needs of current thermal imaging technologies

Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome

[ES] El síndrome de dolor regional complejo (SDRC) es un trastorno de dolor crónico debilitante que suele afectar a una extremidad, y se caracteriza por su compleja e incomprendida fisiopatología subyacente, lo que supone un reto para su diagnóstico y tratamiento. Para evitar el deterioro de la calidad de vida de los pacientes, la consecución de un diagnóstico y tratamiento tempranos marca un punto de inflexión. Entre los diferentes tratamientos, los bloqueos simpáticos lumbares (BSLs) tienen como objetivo aliviar el dolor y reducir algunos signos simpáticos de la afección. Este procedimiento intervencionista se lleva a cabo inyectando anestesia local alrededor de los ganglios simpáticos y, hasta ahora, se realiza frecuentemente bajo el control de diferentes técnicas de imagen, como los ultrasonidos o la fluoroscopia. Dado que la termografía infrarroja (TIR) ha demostrado ser una herramienta eficaz para evaluar la temperatura de la piel, y teniendo en cuenta el efecto vasodilatador que presentan los anestésicos locales inyectados, se ha considerado el uso de la IRT para la evaluación de los BSLs. El objetivo de esta tesis es, estudiar la capacidad de la TIR como una técnica complementaria para la evaluación de la eficacia en la ejecución de los BSLs. Para cumplir este objetivo, se han realizado tres estudios implementando la TIR en pacientes diagnosticados de SDRC de miembros inferiores sometidos a BSLs. El primer estudio se centra en la viabilidad de la TIR como herramienta complementaria para la evaluación de la eficacia ejecución de los BSLs. Cuando se realizan los BSLs, la colocación correcta de la aguja es crítica para llevar realizar el procedimiento técnicamente correcto y, en consecuencia, para lograr los resultados clínicos deseados. Para verificar la posición de la aguja, tradicionalmente se han utilizado técnicas de imagen, sin embargo, los BSLs bajo control fluoroscópico no siempre aseguran su exacta ejecución. Por este motivo, se han aprovechado las alteraciones térmicas inducidas por los anestésicos locales y se han evaluado mediante la TIR. Así, cuando en las imágenes infrarrojas se observaron cambios térmicos en la planta del pie afectado tras la inyección de lidocaína, se consideró que el BSL era exitoso. El segundo estudio trata del análisis cuantitativo de los datos térmicos recogidos en el entorno clínico a partir de diferentes parámetros basados en las temperaturas extraídas de ambos pies. Según los resultados, para predecir adecuadamente los BSLs exitosos, se deberían analizar las temperaturas de las plantas de los pies durante los primeros cuatro minutos tras la inyección del anestésico local. Así, la aplicación de la TIR en el entorno clínico podría ser de gran ayuda para evaluar la eficacia de ejecución de los BSLs mediante la evaluación de las temperaturas de los pies en tiempo real. Por último, el tercer estudio aborda el análisis cuantitativo mediante la implementación de herramientas de machine learning (ML) para evaluar su capacidad de clasificar automáticamente los BSLs. En este estudio se han utilizado una serie de características térmicas extraídas de las imágenes infrarrojas para evaluar cuatro algoritmos de ML para tres momentos diferentes después del instante de referencia (inyección de lidocaína). Los resultados indican que los cuatro modelos evaluados presentan buenos rendimientos para clasificar automáticamente los BSLs entre exitosos y fallidos. Por lo tanto, la combinación de parámetros térmicos junto con de clasificación ML muestra ser eficaz para la clasificación automática de los procedimientos de BSLs. En conclusión, el uso de la TIR como técnica complementaria en la práctica clínica diaria para la evaluación de los BSLs ha demostrado ser totalmente eficaz. Dado que es un método objetivo y relativamente sencillo de implementar, puede permitir que los médicos especialistas en dolor identifiquen los bloqueos realizados fallidos y, en consecuencia, puedan revertir esta situación.[CA] La síndrome de dolor regional complex (SDRC) és un trastorn de dolor crònic debilitant que sol afectar una extremitat, i es caracteritza per la seua complexa i incompresa fisiopatologia subjacent, la qual cosa suposa un repte per al seu diagnòstic i tractament. Per a evitar la deterioració de la qualitat de vida dels pacients, la consecució d'un diagnòstic i tractament primerencs marca un punt d'inflexió. Entre els diferents tractaments , els bloquejos simpàtics lumbars (BSLs) tenen com a objectiu alleujar el dolor i reduir alguns signes simpàtics de l'afecció. Aquest procediment intervencionista es duu a terme injectant anestèsia local al voltant dels ganglis simpàtics i, fins ara, es realitza freqüentment sota el control de diferents tècniques d'imatge, com els ultrasons o la fluoroscopia. Atés que la termografia infraroja (TIR) ha demostrat ser una eina eficaç per a avaluar la temperatura de la pell, i tenint en compte l'efecte vasodilatador que presenten els anestèsics locals injectats, s'ha considerat l'ús de la TIR per a l'avaluació dels BSLs. L'objectiu d'aquesta tesi és, estudiar la capacitat de la TIR com una tècnica complementària per a l'avaluació de l'eficàcia en l'execució dels BSLs. Per a complir aquest objectiu, s'han realitzat tres estudis implementant la TIR en pacients diagnosticats de SDRC de membres inferiors sotmesos a BSLs. El primer estudi avalua la viabilitat de la TIR com a eina complementària per a l'analisi de l'eficàcia en l'execució dels BSLs. Quan es realitzen els BSLs, la col·locació correcta de l'agulla és crítica per a dur a terme el procediment tècnicament correcte i, en conseqüència, per a aconseguir els resultats clínics desitjats. Per a verificar la posició de l'agulla, tradicionalment s'han utilitzat tècniques d'imatge, no obstant això, els BSLs baix control fluoroscòpic no sempre asseguren la seua exacta execució. Per aquest motiu, s'han aprofitat les alteracions tèrmiques induïdes pels anestèsics locals i s'han avaluat mitjançant la TIR. Així, quan en les imatges infraroges es van observar canvis tèrmics en la planta del peu afectat després de la injecció de lidocaIna, es va considerar que el BSL era exitós. El segon estudi tracta de l'anàlisi quantitativa de les dades tèrmiques recollides en l'entorn clínic a partir de diferents paràmetres basats en les temperatures extretes d'ambdós peus. Segons els resultats, per a predir adequadament l'execució exitosa d'un BSL, s'haurien d'analitzar les temperatures de les plantes dels peus durant els primers quatre minuts després de la injecció de l'anestèsic local. Així, l'implementació de la TIR en l'entorn clínic podria ser de gran ajuda per a avaluar l'eficàcia d'execució dels BSLs mitjançant l'avaluació de les temperatures dels peus en temps real. El tercer estudi aborda l'anàlisi quantitativa mitjançant la implementació d'eines machine learning (ML) per a avaluar la seua capacitat de classificar automàticament els BSLs. En aquest estudi s'han utilitzat una sèrie de característiques tèrmiques extretes de les imatges infraroges per a avaluar quatre algorismes de ML per a tres moments diferents després de l'instant de referència (injecció de lidocaïna). Els resultats indiquen que els quatre models avaluats presenten bons rendiments per a classificar automàticament els BSLs en exitosos i fallits. Per tant, la combinació de paràmetres tèrmics juntament amb models de classificació ML mostra ser eficaç per a la classificació automàtica dels procediments de BSLs. En conclusió, l'ús de la TIR com a tècnica complementària en la pràctica clínica diària per a l'avaluació dels BSLs ha demostrat ser totalment eficaç. Atés que és un mètode objectiu i relativament senzill d'implementar, pot ajudar els metges especialistes en dolor a identificar els bloquejos realitzats fallits i, en conseqüència, puguen revertir aquesta situació.[EN] Complex regional pain syndrome (CRPS) is a debilitating chronic pain condition that usually affects one limb, and it is characterized by its misunderstood underlying pathophysiology, resulting in both challenging diagnosis and treatment. To avoid the patients' impairment quality of life, the achievement of both an early diagnosis and treatment marks a turning point. Among the different treatment approaches, lumbar sympathetic blocks (LSBs) are addressed to alleviate the pain and reduce some sympathetic signs of the condition. This interventional procedure is performed by injecting local anaesthetic around the sympathetic ganglia and, until now, it has been performed under different imaging techniques, including the ultrasound or the fluoroscopy approaches. Since infrared thermography (IRT) has proven to be a powerful tool to evaluate skin temperatures and taking into account the vasodilatory effects of the local anaesthetics injected in the LSB, the use of IRT has been considered for the LSBs assessment. Therefore, the purpose of this thesis is to evaluate the capability of IRT as a complementary assessment technique for the LSBs procedures performance. To fulfil this aim, three studies have been conducted implementing the IRT in patients diagnosed with lower limbs CRPS undergoing LSBs. The first study focuses on the feasibility of IRT as a complementary assessment tool for LSBs performance, that is, for the confirmation of the proper needle position. When LSBs are performed, the correct needle placement is critical to carry out the procedure technically correct and, consequently, to achieve the desired clinical outcomes. To verify the needle placement position, imaging techniques have traditionally been used, however, LSBs under radioscopic guidance do not always ensure an exact performance. For this reason, the thermal alterations induced by the local anaesthetics, have been exploited and assessed by means of IRT. Thus, the LSB procedure was considered successfully performed when thermal changes within the affected plantar foot were observed in the infrared images after the lidocaine injection. The second study deals with the quantitative analysis of the thermal data collected in the clinical setting through the evaluation of different temperature-based parameters extracted from both feet. According to the results, the proper LSB success prediction could be achieved in the first four minutes after the block through the evaluation of the feet skin temperatures. Therefore, the implementation of IRT in the clinical setting might be of great help in assessing the LSBs performance by evaluating the plantar feet temperatures in real time. Finally, the third study addresses the quantitative analysis by implementing machine learning (ML) tools to assess their capability to automatically classify LSBs. In this study, a set of thermal features retrieved from the infrared images have been used to evaluate four ML algorithms for three different moments after the baseline time (lidocaine injection). The results indicate that all four models evaluated present good performance metrics to automatically classify LSBs into successful and failed. Therefore, combining infrared features with ML classification models shows to be effective for the LSBs procedures automatic classification. In conclusion, the use of IRT as a complementary technique in daily clinical practice for LSBs assessment has been evidenced entirely effective. Since IRT is an objective method and it is not very demanding to perform, it is of great help for pain physicians to identify failed procedures, and consequently, it allow them to reverse this situation.Cañada Soriano, M. (2022). Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181699TESI

Pain Mitigation in Cattle Following Soft Tissue Surgery

Pain mitigation for surgical procedures is a topic of concern for the public, producers, and veterinarians. The objective of this study was to determine the efficacy of meloxicam for pain mitigation in adult lactating dairy cattle following a right-side laparotomy with omentopexy. Twenty-four dairy cattle (mean age: 2.51 +/- 0.54 years) were enrolled. Cattle were assigned blocks based on parity, days in milk, milk yield, and pregnancy status, and randomly allocated to groups Meloxicam (MEL) or placebo treated control (CON). The study had two phases; sham (day 0-14) and surgery (day 15-28). On day 0, cattle were prepared for surgery. Injectable meloxicam (MEL) or saline placebo (CON) was administered (dose: 0.5 mg/kg) 5 minutes before simulated surgery (restraint for 30 minutes). On day 15, the surgical procedure was performed. Meloxicam or saline were administered prior to surgery. A right flank laparotomy, brief abdominal exploration, and omentopexy was performed on all animals. Blood was collected via jugular catheter at hours 0, 2, 4, 8, 12, 24, 36, 48, 60, & 72 during both phases for cortisol, and at hours 0, 2, 4, 8, 12, 24, 48, 72, 96, 120, 144, & 168 for haptoglobin, PGE2, and fibrinogen. Mechanical nociceptive threshold (MNT) was measured using an algometer and collected at hours 0, 1, 4, & 8 after sham and hours 0, 1, 2, 4, 8, 12, 24, 36, 48, 60, & 72 after surgery. Infrared thermography (IRT) was taken of the incision site at hours 0, 1, 4, & 8 hours after sham and 0, 2, 4, 8, 12, 24, 36, 48, 60, & 72 after surgery. PGE2 concentrations displayed a treatment by time interaction where concentrations were higher in the CON animals (P = 0.003). Total cortisol concentrations were significantly increased in CON 4 hours post-operatively (P=0.004). Haptoglobin was significantly increased in CON 72 and 96 hours post-operatively (P\u3c 0.001). There was no difference for fibrinogen (P=0.43), MNT (P=0.24) or IRT (P=0.68). This study indicates using meloxicam significantly reduces biomarkers of inflammation and indirect measures of pain and suggests meloxicam is effective in mitigating post-operative pain in adult lactating dairy cattle