A Clinical Prototype Transrectal Diffuse Optical Tomography (TRDOT) System for In vivo Monitoring of Photothermal Therapy (PTT) of Focal Prostate Cancer

Enhanced model iteration algorithm with graph neural network for diffuse optical tomography

Diffuse optical tomography (DOT) employs near-infrared light to reveal the optical parameters of biological tissues. Due to the strong scattering of photons in tissues and the limited surface measurements, DOT reconstruction is severely ill-posed. The Levenberg-Marquardt (LM) is a popular iteration method for DOT, however, it is computationally expensive and its reconstruction accuracy needs improvement. In this study, we propose a neural model based iteration algorithm which combines the graph neural network with Levenberg-Marquardt (GNNLM), which utilizes a graph data structure to represent the finite element mesh. In order to verify the performance of the graph neural network, two GNN variants, namely graph convolutional neural network (GCN) and graph attention neural network (GAT) were employed in the experiments. The results showed that GCNLM performs best in the simulation experiments within the training data distribution. However, GATLM exhibits superior performance in the simulation experiments outside the training data distribution and real experiments with breast-like phantoms. It demonstrated that the GATLM trained with simulation data can generalize well to situations outside the training data distribution without transfer training. This offers the possibility to provide more accurate absorption coefficient distributions in clinical practice.

One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance- These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

Photodynamic and Photothermal Therapies: Synergy Opportunities for Nanomedicine

Tumoricidal photodynamic (PDT) and photothermal (PTT) therapies harness light to eliminate cancer cells with spatiotemporal precision by either generating reactive oxygen species or increasing temperature. Great strides have been made in understanding biological effects of PDT and PTT at the cellular, vascular and tumor microenvironmental levels, as well as translating both modalities in the clinic. Emerging evidence suggests that PDT and PTT may synergize due to their different mechanisms of action, and their nonoverlapping toxicity profiles make such combination potentially efficacious. Moreover, PDT/PTT combinations have gained momentum in recent years due to the development of multimodal nanoplatforms that simultaneously incorporate photodynamically- and photothermally active agents. In this review, we discuss how combining PDT and PTT can address the limitations of each modality alone and enhance treatment safety and efficacy. We provide an overview of recent literature featuring dual PDT/PTT nanoparticles and analyze the strengths and limitations of various nanoparticle design strategies. We also detail how treatment sequence and dose may affect cellular states, tumor pathophysiology and drug delivery, ultimately shaping the treatment response. Lastly, we analyze common experimental design pitfalls that complicate preclinical assessment of PDT/PTT combinations and propose rational guidelines to elucidate the mechanisms underlying PDT/PTT interactions.

A Model-Based Iterative Learning Approach for Diffuse Optical Tomography

Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a 'model-based' learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration.

Effects of patient-specific treatment planning on eligibility for photodynamic therapy of deep tissue abscess cavities: retrospective Monte Carlo simulation study

SIGNIFICANCE

Antimicrobial photodynamic therapy (PDT) effectively kills bacterial strains found in deep tissue abscess cavities. PDT response hinges on multiple factors, including light dose, which depends on patient optical properties.

AIM

Computed tomography images for 60 abscess drainage subjects were segmented and used for Monte Carlo (MC) simulation. We evaluated effects of optical properties and abscess morphology on PDT eligibility and generated treatment plans.

APPROACH

A range of abscess wall absorptions (μa  ,  wall) and intra-cavity Intralipid concentrations were simulated. At each combination, the threshold optical power and optimal Intralipid concentration were found for a fluence rate target, with subjects being eligible for PDT if the target was attainable with <2000  mW of source light. Further simulations were performed with absorption within the cavity (μa  ,  cavity).

RESULTS

Patient-specific treatment planning substantially increased the number of subjects expected to achieve an efficacious light dose for antimicrobial PDT, especially with Intralipid modification. The threshold optical power and optimal Intralipid concentration increased with increasing μa  ,  wall (p  <  0.001). PDT eligibility improved with patient-specific treatment planning (p  <  0.0001). With μa  ,  wall  =  0.2  cm  -  1, eligibility increased from 42% to 92%. Increasing μa  ,  cavity reduced PDT eligibility (p  <  0.0001); modifying the delivered optical power had the greatest impact in this case.

CONCLUSIONS

MC-based treatment planning greatly increases eligibility for PDT of abscess cavities.

Preclinical evaluation of a clinical prototype transrectal diffuse optical tomography system for monitoring photothermal therapy of focal prostate cancer

SIGNIFICANCE

Our work demonstrates in preclinical models that continuous-wave transrectal diffuse optical tomography (TRDOT) can be used to accurately monitor photothermal therapy (PTT) and, in particular, the progression of the photocoagulation boundary toward the rectum. When used in patients, this should prevent rectal damage during PTT, thereby achieving maximum treatment efficacy while ensuring safety, using a technology platform suitable for wide dissemination.

AIM

We aim to validate that TRDOT measurements analyzed using a shape-based image-reconstruction algorithm (SBDOT) allow localization of the photocoagulation boundary during PTT within ±1  mm toward the rectum in the transverse plane.

APPROACH

TRDOT measurements were performed in tissue-simulating phantoms, ex vivo tissues, and an in vivo canine prostate model. The accuracy and sensitivity of reconstructing the size and location of the coagulation zone were determined, based on changes in the tissue absorption and reduced scattering coefficients upon photocoagulation. The reconstruction also yields the native and coagulated tissue optical properties.

RESULTS

The TRDOT measurements and SBDOT reconstruction algorithm were confirmed to perform sufficiently well for clinical translation in PTT monitoring, recovering the location of the coagulation boundary within ±1  mm compared to the true value as determined by direct visualization postexcision and/or MRI.

CONCLUSIONS

Implementing previously described TRDOT instrumentation and SBDOT image reconstruction in different tissue models confirms the potential for clinincal translation, including required refinements of the system and reconstruction algorithm.

Optical property recovery with spatially-resolved diffuse reflectance at short source-detector separations using a compact fiber-optic probe

We describe a compact fiber-optic probe (2 mm outside diameter) that utilizes spatially-resolved diffuse reflectance for tissue optical property recovery. Validation was performed in phantoms containing Intralipid 20% as scatterer, and methylene blue (MB), MnTPPS, and/or India ink as absorbers. Over a range of conditions, the reduced scattering coefficient was recovered with a root mean square error (RMSE) of 0.86-2.7 cm^-1 (average error = 3.8%). MB concentration was recovered with RMSE = 0.26-0.52 µM (average error = 15.0%), which did not vary with inclusion of MnTPPS (p=0.65). This system will be utilized to determine optical properties in human abscesses, in order to generate treatment plans for photodynamic therapy.

Effects of the Latex of Synadenium grantii Hook F. (Euphorbiaceae) on a Preclinical Model of Canine Prostate Cancer

Prostatic cancer (PC) stands out in terms of its occurrence, pathophysiology, and unfavorable prognostics in humans and dogs. Natural drugs bear an integrative potential for conventional antineoplastic treatments. In this context, the bioproducts of Synadenium grantii have been empirically used in different parts of Brazil for the integrative treatment of prostate cancer in humans. However, there is no availability of scientific evidence of the antitumor effects of S. grantii. Therefore, this study aimed to investigate the bioactive compounds in the latex of S. grantii using the high-resolution mass spectrophotometry (HRMS) and to evaluate its cytotoxic effects on primary canine PC cell cultures. Four fragments of phorbol ester were identified as potential bioactive compounds using the HRMS. With the help of an MTT ([3-(4,5-dimethyldiazol-2-yl)-2,5 diphenyltetrazolium bromide]) assay, two canine prostatic carcinoma cell lines (PC 1 and PC2) showed a decrease in the tumor cell count, with an Inhibitory concentration 50 (IC₅₀)of 0.8469 and 0.6068 mg/ml, respectively, for PC1 and PC2. In conclusion, the latex of S. grantii contains phorbol esters in its composition, and its aqueous solution has a cytotoxic effect on canine metastatic PC cells in vitro.

A Comparison of Two Probes to Determine Rectum Optical Properties

Tissue optical properties are crucial for determining the light dose delivered to the tumor. Two probes are compared: the two-catheter probe is based on transmittance measurement between one point source and one isotropic detector inside parallel catheters spaced at 0.5 cm along a 1-inch diameter transparent cylinder; and a 1-inch trans-rectal diffuse optical tomography (DOT) probe designed for prostate measurements, using a multiple fiber-array with source-detector separations of 1.4-10 mm. The two-catheter probe uses an empirical model for primary and scatter light fluence rates in the cylindrical cavity condition for anal PDT to determine optical properties along the source catheter using dual motors to move the source and detector along the catheters. The DOT probe uses finite element method (FEM) to obtain distribution of optical properties in 3D. Validations for the two probes were performed in liquid and solid phantoms. For each method, validation was performed in tissue-mimicking liquid phantoms for a range of known optical properties (μa between 0.05 and 0.9 cm-1 and μs' between 5.5 and 16.5 cm-1). To cross-check the two methods, solid phantoms were created of known optical properties at the University of Pennsylvania and sent for measurement to Princess Margaret Cancer Centre (PMH) to mimic realistic patient simulating conditions. Measurements were taken and optical properties were then recovered without knowing the expected values to cross-validate each probe. The results show modest agreement between the measured μa and μs'values, but high degree of agreement between the measured μeff performed independently using the two methods.

Thinker invariance: enabling deep neural networks for BCI across more people

OBJECTIVE

Most deep neural networks (DNNs) used as brain computer interfaces (BCI) classifiers are rarely viable for more than one person and are relatively shallow compared to the state-of-the-art in the wider machine learning literature. The goal of this work is to frame these as a unified challenge and reconsider how transfer learning is used to overcome these difficulties.

APPROACH

We present two variations of a holistic approach to transfer learning with DNNs for BCI that rely on a deeper network called TIDNet. Our approaches use multiple subjects for training in the interest of creating a more universal classifier that is applicable for new (unseen) subjects. The first approach is purely subject-invariant and the second targets specific subjects, without loss of generality. We use five publicly accessible datasets covering a range of tasks and compare our approaches to state-of-the-art alternatives in detail.

MAIN RESULTS

We observe that TIDNet in conjunction with our training augmentations is more consistent when compared to shallower baselines, and in some cases exhibits large and significant improvements, for instance motor imagery classification improvements of over 8%. Furthermore, we show that our suggested multi-domain learning (MDL) strategy strongly outperforms simply fine-tuned general models when targeting specific subjects, while remaining more generalizable to still unseen subjects.

SIGNIFICANCE

TIDNet in combination with a data alignment-based training augmentation proves to be a consistent classification approach of single raw trials and can be trained even with the inclusion of corrupted trials. Our MDL strategy calls into question the intuition to fine-tune trained classifiers to new subjects, as it proves simpler and more accurate while remaining general. Furthermore, we show evidence that augmented TIDNet training makes better use of additional subjects, showing continued and greater performance improvement over shallower alternatives, indicating promise for a new subject-invariant paradigm rather than a subject-specific one.