Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue

[¹⁸F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques

Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)

Automatic segmentation of wall structures from cardiac images

One important topic in medical image analysis is segmenting wall structures from different cardiac medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). This task is typically done by radiologists either manually or semi-automatically, which is a very time-consuming process. To reduce the laborious human efforts, automatic methods have become popular in this research. In this thesis, features insensitive to data variations are explored to segment the ventricles from CT images and extract the left atrium from MR images. As applications, the segmentation results are used to facilitate cardiac disease analysis. Specifically, 1. An automatic method is proposed to extract the ventricles from CT images by integrating surface decomposition with contour evolution techniques. In particular, the ventricles are first identified on a surface extracted from patient-specific image data. Then, the contour evolution is employed to refine the identified ventricles. The proposed method is robust to variations of ventricle shapes, volume coverages, and image quality. 2. A variational region-growing method is proposed to segment the left atrium from MR images. Because of the localized property of this formulation, the proposed method is insensitive to data variabilities that are hard to handle by globalized methods. 3. In applications, a geometrical computational framework is proposed to estimate the myocardial mass at risk caused by stenoses. In addition, the segmentation of the left atrium is used to identify scars for MR images of post-ablation.PhDCommittee Chair: Yezzi, Anthony; Committee Co-Chair: Tannenbaum, Allen; Committee Member: Egerstedt, Magnus ; Committee Member: Fedele, Francesco ; Committee Member: Stillman, Arthur; Committee Member: Vela,Patrici

Differentiation of Alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach

Nous avons utilisé l'imagerie TEP avec les traceurs F18-FDG et AV45 en conjonction avec les méthodes de classification du domaine du "Machine Learning". Les images ont été acquises en mode dynamique, une image toutes les 5 minutes. Les données ont été transformées par Analyse en Composantes Principales et Analyse en Composantes Indépendantes. Les images proviennent de trois sources différentes: la base de données ADNI (Alzheimer's Disease Neuroimaging Initiative) et deux protocoles réalisés au sein du centre TEP de l'hôpital Purpan. Pour évaluer la performance de la classification nous avons eu recours à la méthode de validation croisée LOOCV (Leave One Out Cross Validation). Nous donnons une comparaison entre les deux méthodes de classification les plus utilisées, SVM (Support Vector Machine) et les réseaux de neurones artificiels (ANN). La combinaison donnant le meilleur taux de classification semble être SVM et le traceur AV45. Cependant les confusions les plus importantes sont entre les patients MCI et les sujets normaux. Les patients Alzheimer se distinguent relativement mieux puisqu'ils sont retrouvés souvent à plus de 90%. Nous avons évalué la généralisation de telles méthodes de classification en réalisant l'apprentissage sur un ensemble de données et la classification sur un autre ensemble. Nous avons pu atteindre une spécificité de 100% et une sensibilité supérieure à 81%. La méthode SVM semble avoir une meilleure sensibilité que les réseaux de neurones. L'intérêt d'un tel travail est de pouvoir aider à terme au diagnostic de la maladie d'Alzheimer.We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease

The relationship between local geomagnetic activity, Tibetan Buddhist meditation and psychic awareness: preliminary study

The proposal is that the local environment, as well as the person‟s state of consciousness, affects the ability to consciously use psychic awareness. For the past three decades research in parapsychology has found evidence that psychic awareness may be related to fluctuations in geomagnetic activity (GMA). Most of this research has used global measures of GMA, but recently Ryan found that certain classes of local GMA appeared to enhance receptive psi, whilst other classes had an inhibitory effect. He demonstrated that these patterns accounted well for the previously reported relationships between psi performance, global GMA and local sidereal time. We are running a long-term study with a special population, which will hopefully help to elucidate this effect. This research also extends previous studies with Yogis and Tibetan Buddhist monks in India, seeing whether the finding, that more years of meditation practice is related to psi-hitting, remains true for Western Tibetan Buddhist meditation practitioners, and further exploring which aspects of meditation are most psi-conducive. A clairvoyance/precognition free-response design is being used in which the participant, after a 15-minute meditation period, attempts to correctly choose a video clip selected at random by a computer. The computer program (PreCOG) chooses a target set at random from a pool of 25 sets, and a target video clip at random from the 4-clip set. Each participant completes a minimum of eight sessions each year, which enables both a within-subjects and a between-subjects design to assess whether or not local GMA is related to psi awareness. This design has both a randomized double-blind and inbuilt fraud control. Participants are all meditators residing at Kagyu Samye Ling Tibetan centre who have practiced meditation for at least 10 years, up to a maximum of 30 participants. There are three psychological tests being correlated with the psi scores at present: the Meditation Attainment Questionnaire is a measure of the level of meditation attainment achieved by each participant; the Stroop test is a measure of focus of attention; and the Temporal Lobe Questionnaire assesses temporal lobe lability, which may be an indicator of the degree to which the person is affected by GMA. Geomagnetic field measurements sampled each second are being supplied by the British Geological Survey's Eskdalemuir observatory, which is located 2 miles from Samye Ling Tibetan centre. There are three formal hypotheses: 1) Years of meditation practice will be positively associated with performance on the psi task; 2) Psi performance for sessions conducted during “high band” 1 (0.2 – 0.5 Hz) GMA will be greater than psi performance for sessions conducted during “low band” 1 GMA; 3) Psi performance for sessions conducted during “high band” 3 (0.025 – 0.1 Hz) will be less than psi performance conducted during “low band” 3 GMA. In addition, as this is a preliminary study, a number of exploratory analyses, such as correlations with the psychological tests, are planned in order to enable optimum design for the controlled studies to follow