A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

Navigation system based in motion tracking sensor for percutaneous renal access

Tese de Doutoramento em Engenharia BiomédicaMinimally-invasive kidney interventions are daily performed to diagnose and treat several renal diseases. Percutaneous renal access (PRA) is an essential but challenging stage for most of these procedures, since its outcome is directly linked to the physician’s ability to precisely visualize and reach the anatomical target. Nowadays, PRA is always guided with medical imaging assistance, most frequently using X-ray based imaging (e.g. fluoroscopy). Thus, radiation on the surgical theater represents a major risk to the medical team, where its exclusion from PRA has a direct impact diminishing the dose exposure on both patients and physicians. To solve the referred problems this thesis aims to develop a new hardware/software framework to intuitively and safely guide the surgeon during PRA planning and puncturing. In terms of surgical planning, a set of methodologies were developed to increase the certainty of reaching a specific target inside the kidney. The most relevant abdominal structures for PRA were automatically clustered into different 3D volumes. For that, primitive volumes were merged as a local optimization problem using the minimum description length principle and image statistical properties. A multi-volume Ray Cast method was then used to highlight each segmented volume. Results show that it is possible to detect all abdominal structures surrounding the kidney, with the ability to correctly estimate a virtual trajectory. Concerning the percutaneous puncturing stage, either an electromagnetic or optical solution were developed and tested in multiple in vitro, in vivo and ex vivo trials. The optical tracking solution aids in establishing the desired puncture site and choosing the best virtual puncture trajectory. However, this system required a line of sight to different optical markers placed at the needle base, limiting the accuracy when tracking inside the human body. Results show that the needle tip can deflect from its initial straight line trajectory with an error higher than 3 mm. Moreover, a complex registration procedure and initial setup is needed. On the other hand, a real-time electromagnetic tracking was developed. Hereto, a catheter was inserted trans-urethrally towards the renal target. This catheter has a position and orientation electromagnetic sensor on its tip that function as a real-time target locator. Then, a needle integrating a similar sensor is used. From the data provided by both sensors, one computes a virtual puncture trajectory, which is displayed in a 3D visualization software. In vivo tests showed a median renal and ureteral puncture times of 19 and 51 seconds, respectively (range 14 to 45 and 45 to 67 seconds). Such results represent a puncture time improvement between 75% and 85% when comparing to state of the art methods. 3D sound and vibrotactile feedback were also developed to provide additional information about the needle orientation. By using these kind of feedback, it was verified that the surgeon tends to follow a virtual puncture trajectory with a reduced amount of deviations from the ideal trajectory, being able to anticipate any movement even without looking to a monitor. Best results show that 3D sound sources were correctly identified 79.2 ± 8.1% of times with an average angulation error of 10.4º degrees. Vibration sources were accurately identified 91.1 ± 3.6% of times with an average angulation error of 8.0º degrees. Additionally to the EMT framework, three circular ultrasound transducers were built with a needle working channel. One explored different manufacture fabrication setups in terms of the piezoelectric materials, transducer construction, single vs. multi array configurations, backing and matching material design. The A-scan signals retrieved from each transducer were filtered and processed to automatically detect reflected echoes and to alert the surgeon when undesirable anatomical structures are in between the puncture path. The transducers were mapped in a water tank and tested in a study involving 45 phantoms. Results showed that the beam cross-sectional area oscillates around the ceramics radius and it was possible to automatically detect echo signals in phantoms with length higher than 80 mm. Hereupon, it is expected that the introduction of the proposed system on the PRA procedure, will allow to guide the surgeon through the optimal path towards the precise kidney target, increasing surgeon’s confidence and reducing complications (e.g. organ perforation) during PRA. Moreover, the developed framework has the potential to make the PRA free of radiation for both patient and surgeon and to broad the use of PRA to less specialized surgeons.Intervenções renais minimamente invasivas são realizadas diariamente para o tratamento e diagnóstico de várias doenças renais. O acesso renal percutâneo (ARP) é uma etapa essencial e desafiante na maior parte destes procedimentos. O seu resultado encontra-se diretamente relacionado com a capacidade do cirurgião visualizar e atingir com precisão o alvo anatómico. Hoje em dia, o ARP é sempre guiado com recurso a sistemas imagiológicos, na maior parte das vezes baseados em raios-X (p.e. a fluoroscopia). A radiação destes sistemas nas salas cirúrgicas representa um grande risco para a equipa médica, aonde a sua remoção levará a um impacto direto na diminuição da dose exposta aos pacientes e cirurgiões. De modo a resolver os problemas existentes, esta tese tem como objetivo o desenvolvimento de uma framework de hardware/software que permita, de forma intuitiva e segura, guiar o cirurgião durante o planeamento e punção do ARP. Em termos de planeamento, foi desenvolvido um conjunto de metodologias de modo a aumentar a eficácia com que o alvo anatómico é alcançado. As estruturas abdominais mais relevantes para o procedimento de ARP, foram automaticamente agrupadas em volumes 3D, através de um problema de optimização global com base no princípio de “minimum description length” e propriedades estatísticas da imagem. Por fim, um procedimento de Ray Cast, com múltiplas funções de transferência, foi utilizado para enfatizar as estruturas segmentadas. Os resultados mostram que é possível detetar todas as estruturas abdominais envolventes ao rim, com a capacidade para estimar corretamente uma trajetória virtual. No que diz respeito à fase de punção percutânea, foram testadas duas soluções de deteção de movimento (ótica e eletromagnética) em múltiplos ensaios in vitro, in vivo e ex vivo. A solução baseada em sensores óticos ajudou no cálculo do melhor ponto de punção e na definição da melhor trajetória a seguir. Contudo, este sistema necessita de uma linha de visão com diferentes marcadores óticos acoplados à base da agulha, limitando a precisão com que a agulha é detetada no interior do corpo humano. Os resultados indicam que a agulha pode sofrer deflexões à medida que vai sendo inserida, com erros superiores a 3 mm. Por outro lado, foi desenvolvida e testada uma solução com base em sensores eletromagnéticos. Para tal, um cateter que integra um sensor de posição e orientação na sua ponta, foi colocado por via trans-uretral junto do alvo renal. De seguida, uma agulha, integrando um sensor semelhante, é utilizada para a punção percutânea. A partir da diferença espacial de ambos os sensores, é possível gerar uma trajetória de punção virtual. A mediana do tempo necessário para puncionar o rim e ureter, segundo esta trajetória, foi de 19 e 51 segundos, respetivamente (variações de 14 a 45 e 45 a 67 segundos). Estes resultados representam uma melhoria do tempo de punção entre 75% e 85%, quando comparados com o estado da arte dos métodos atuais. Além do feedback visual, som 3D e feedback vibratório foram explorados de modo a fornecer informações complementares da posição da agulha. Verificou-se que com este tipo de feedback, o cirurgião tende a seguir uma trajetória de punção com desvios mínimos, sendo igualmente capaz de antecipar qualquer movimento, mesmo sem olhar para o monitor. Fontes de som e vibração podem ser corretamente detetadas em 79,2 ± 8,1% e 91,1 ± 3,6%, com erros médios de angulação de 10.4º e 8.0 graus, respetivamente. Adicionalmente ao sistema de navegação, foram também produzidos três transdutores de ultrassom circulares com um canal de trabalho para a agulha. Para tal, foram exploradas diferentes configurações de fabricação em termos de materiais piezoelétricos, transdutores multi-array ou singulares e espessura/material de layers de suporte. Os sinais originados em cada transdutor foram filtrados e processados de modo a detetar de forma automática os ecos refletidos, e assim, alertar o cirurgião quando existem variações anatómicas ao longo do caminho de punção. Os transdutores foram mapeados num tanque de água e testados em 45 phantoms. Os resultados mostraram que o feixe de área em corte transversal oscila em torno do raio de cerâmica, e que os ecos refletidos são detetados em phantoms com comprimentos superiores a 80 mm. Desta forma, é expectável que a introdução deste novo sistema a nível do ARP permitirá conduzir o cirurgião ao longo do caminho de punção ideal, aumentado a confiança do cirurgião e reduzindo possíveis complicações (p.e. a perfuração dos órgãos). Além disso, de realçar que este sistema apresenta o potencial de tornar o ARP livre de radiação e alarga-lo a cirurgiões menos especializados.The present work was only possible thanks to the support by the Portuguese Science and Technology Foundation through the PhD grant with reference SFRH/BD/74276/2010 funded by FCT/MEC (PIDDAC) and by Fundo Europeu de Desenvolvimento Regional (FEDER), Programa COMPETE - Programa Operacional Factores de Competitividade (POFC) do QREN

A non-invasive diagnostic system for early assessment of acute renal transplant rejection.

Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

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

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed