Image change detection using paradoxical theory for patient follow-up quantitation and therapy assessment

Beauty and Uncertainty as Transformative Factors: A Free Energy Principle Account of Aesthetic Diagnosis and Intervention in Gestalt Psychotherapy

Drawing from field theory, Gestalt therapy conceives psychological suffering and psychotherapy as two intentional field phenomena, where unprocessed and chaotic experiences seek the opportunity to emerge and be assimilated through the contact between the patient and the therapist (i.e., the intentionality of contacting). This therapeutic approach is based on the therapist’s aesthetic experience of his/her embodied presence in the flow of the healing process because (1) the perception of beauty can provide the therapist with feedback on the assimilation of unprocessed experiences; (2) the therapist’s attentional focus on intrinsic aesthetic diagnostic criteria can facilitate the modification of rigid psychopathological fields by supporting the openness to novel experiences. The aim of the present manuscript is to review recent evidence from psychophysiology, neuroaesthetic research, and neurocomputational models of cognition, such as the free energy principle (FEP), which support the notion of the therapeutic potential of aesthetic sensibility in Gestalt psychotherapy. Drawing from neuroimaging data, psychophysiology and recent neurocognitive accounts of aesthetic perception, we propose a novel interpretation of the sense of beauty as a self-generated reward motivating us to assimilate an ever-greater spectrum of sensory and affective states in our predictive representation of ourselves and the world and supporting the intentionality of contact. Expecting beauty, in the psychotherapeutic encounter, can help therapists tolerate uncertainty avoiding impulsive behaviours and to stay tuned to the process of change.

A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation

PURPOSE

The purpose of this study was to investigate the use of a probabilistic quad-tree graph (hidden Markov tree, HMT) to provide fast computation, robustness and an interpretational framework for multimodality image processing and to evaluate this framework for single gross tumor target (GTV) delineation from both positron emission tomography (PET) and computed tomography (CT) images.

METHODS

We exploited joint statistical dependencies between hidden states to handle the data stack using multi-observation, multi-resolution of HMT and Bayesian inference. This framework was applied to segmentation of lung tumors in PET/CT datasets taking into consideration simultaneously the CT and the PET image information. PET and CT images were considered using either the original voxels intensities, or after wavelet/contourlet enhancement. The Dice similarity coefficient (DSC), sensitivity (SE), positive predictive value (PPV) were used to assess the performance of the proposed approach on one simulated and 15 clinical PET/CT datasets of non-small cell lung cancer (NSCLC) cases. The surrogate of truth was a statistical consensus (obtained with the Simultaneous Truth and Performance Level Estimation algorithm) of three manual delineations performed by experts on fused PET/CT images. The proposed framework was applied to PET-only, CT-only and PET/CT datasets, and were compared to standard and improved fuzzy c-means (FCM) multimodal implementations.

RESULTS

A high agreement with the consensus of manual delineations was observed when using both PET and CT images. Contourlet-based HMT led to the best results with a DSC of 0.92 ± 0.11 compared to 0.89 ± 0.13 and 0.90 ± 0.12 for Intensity-based HMT and Wavelet-based HMT, respectively. Considering PET or CT only in the HMT led to much lower accuracy. Standard and improved FCM led to comparatively lower accuracy than HMT, even when considering multimodal implementations.

CONCLUSIONS

We evaluated the accuracy of the proposed HMT-based framework for PET/CT image segmentation. The proposed method reached good accuracy, especially with pre-processing in the contourlet domain.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

PURPOSE

The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application.

APPROACH

A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed.

FINDINGS

A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol.

CONCLUSIONS

Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

State-Of-The-Art and Recent Advances in Quantification for Therapeutic Follow-Up in Oncology Using PET

¹⁸F-fluoro-2-deoxyglucose (¹⁸F-FDG) positron emission tomography (PET) is an important tool in oncology. Its use has greatly progressed from initial diagnosis to staging and patient monitoring. The information derived from ¹⁸F-FDG-PET allowed the development of a wide range of PET quantitative analysis techniques ranging from simple semi-quantitative methods like the standardized uptake value (SUV) to “high order metrics” that require a segmentation step and additional image processing. In this review, these methods are discussed, focusing particularly on the available methodologies that can be used in clinical trials as well as their current applications in international consensus for PET interpretation in lymphoma and solid tumors.