Multisphere phantom and analysis algorithm for PET image quality assessment

Phantom and methodology for comparison of small lesion detectability in PET

PURPOSE

The main goal of this work is to describe a phantom design, data acquisition and data analysis methodology enabling comparison of small lesion detectability between PET imaging systems and reconstruction algorithms. Several methods are currently available to characterize intrinsic and image quality performance, but none focus exclusively on small lesion detectability.

METHODS

We previously developed a small-lesion detection phantom and described initial results using a head-size phantom. Unlike most fillable nuclear medicine phantoms, this phantom offers a semi-realistic heterogenous background and wall-less contrast features. In this work, the methodology is extended to include (a) the use of both head- and body-sized phantoms and (b) a multi-scan data collection and analysis method. We present an example use case of the phantom and detection estimation methodology, comparing the small-lesion detection performance across four commercial PET/CT systems.

RESULTS

Repeat acquisitions of the phantom enabled estimation of model observer performance and surrogates of detectability. As anticipated, estimated detectability increased with the square root of system sensitivity and TOF offered marked improvement in detectability, especially for the body sized object. The proposed approach characterizing detectability at different times during the decay of the phantom enabled comparison of small lesion detectability at matched activity concentrations (and scan durations) across different scanners.

CONCLUSION

The proposed approach offers a reproducible tool for evaluating relative tradeoffs of system performance on small lesion detectability.

Which cell death modality wins the contest for photodynamic therapy of cancer?

Photodynamic therapy (PDT) was discovered more than 100 years ago. Since then, many protocols and agents for PDT have been proposed for the treatment of several types of cancer. Traditionally, cell death induced by PDT was categorized into three types: apoptosis, cell death associated with autophagy, and necrosis. However, with the discovery of several other regulated cell death modalities in recent years, it has become clear that this is a rather simple understanding of the mechanisms of action of PDT. New observations revealed that cancer cells exposed to PDT can pass through various non-conventional cell death pathways, such as paraptosis, parthanatos, mitotic catastrophe, pyroptosis, necroptosis, and ferroptosis. Nowadays, immunogenic cell death (ICD) has become one of the most promising ways to eradicate tumor cells by activation of the T-cell adaptive immune response and induction of long-term immunological memory. ICD can be triggered by many anti-cancer treatment methods, including PDT. In this review, we critically discuss recent findings on the non-conventional cell death mechanisms triggered by PDT. Next, we emphasize the role and contribution of ICD in these PDT-induced non-conventional cell death modalities. Finally, we discuss the obstacles and propose several areas of research that will help to overcome these challenges and lead to the development of highly effective anti-cancer therapy based on PDT.

Optical coherence angiography for pre-treatment assessment and treatment monitoring following photodynamic therapy: a basal cell carcinoma patient study

Microvascular networks of human basal cell carcinomas (BCC) and surrounding skin were assessed with optical coherence angiography (OCA) in conjunction with photodynamic therapy (PDT). OCA images were collected and analyzed in 31 lesions pre-treatment, and immediately/24 hours/3-12 months post-treatment. Pre-treatment OCA enabled differentiation between prevalent subtypes of BCC (nodular and superficial) and nodular-with-necrotic-core BCC subtypes with a diagnostic accuracy of 78%; this can facilitate more accurate biopsy reducing sampling error and better therapy regimen selection. Post-treatment OCA images at 24 hours were 98% predictive of eventual outcome. Additional findings highlight the importance of pre-treatment necrotic core, vascular metrics associated with hypertrophic scar formation, and early microvascular changes necessary in both tumorous and peri-tumorous regions to ensure treatment success.

Rhenium Complexes with Red-Light-Induced Anticancer Activity

Rhenium(I) pyridocarbazole complexes with photoinduced antiproliferative activity are reported. The substitutionally inert complexes induce cell death by singlet oxygen generation upon irradiation with red light (λ ≥ 620 nm), while only weak background cytotoxicity is observed in the dark. Due to their ability to inhibit protein kinases (nanomolar IC₅₀ values against Pim1 at 10 μM ATP), this class of rhenium complexes point into the direction of dual function antiproliferative therapy with a single drug in which photodynamic therapy is combined with the inhibition of cancer related protein kinases.

Imaging a photodynamic therapy photosensitizer in vivo with a time-gated fluorescence tomography system

We report the tomographic imaging of a photodynamic therapy (PDT) photosensitizer, 2-(1-hexyloxyethyl)-2-devinyl pyropheophorbide-a (HPPH) in vivo with time-domain fluorescence diffuse optical tomography (TD-FDOT). Simultaneous reconstruction of fluorescence yield and lifetime of HPPH was performed before and after PDT. The methodology was validated in phantom experiments, and depth-resolved in vivo imaging was achieved through simultaneous three-dimensional (3-D) mappings of fluorescence yield and lifetime contrasts. The tomographic images of a human head-and-neck xenograft in a mouse confirmed the preferential uptake and retention of HPPH by the tumor 24-h post-injection. HPPH-mediated PDT induced significant changes in fluorescence yield and lifetime. This pilot study demonstrates that TD-FDOT may be a good imaging modality for assessing photosensitizer distributions in deep tissue during PDT monitoring.

Artificial Neural Network-Based System for PET Volume Segmentation

Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.