Radiofrequency Ablation Combined with Radioactive Seed Implantation for Nonsmall Cell Lung Cancer

The aim of this study is to investigate the value of radiofrequency ablation combined with radioactive seed implantation in nonsmall cell lung cancer treatment. 30 patients with primary nonsmall cell lung cancer were randomly divided into two groups. Group A was treated with radiofrequency ablation combined with radiation seed implantation, and group B was treated with radiofrequency ablation only. We compared the incidence of complications in the two groups and reviewed the effective percentage every 3 months. All patients were treated successfully, and there were no deaths during treatment. There were no deaths and no cases of distant organ metastasis in nine months of follow-up. There were no significant differences in treatment-related complications between the two groups. The early postoperative (three and six months) effective percentage was not significantly different (P > 0.05). After 9 months, the postoperative effective rate for group A (9/15) was significantly different from that for group B (radiofrequency ablation) (6/15) (P < 0.05). Radiofrequency ablation combined with radiation 125I seed implantation can complement each other in the treatment of nonsmall cell lung cancer.

Synthetic Data for Multi-Parameter Camera-Based Physiological Sensing

Synthetic data is a powerful tool in training data hungry deep learning algorithms. However, to date, camera-based physiological sensing has not taken full advantage of these techniques. In this work, we leverage a high-fidelity synthetics pipeline for generating videos of faces with faithful blood flow and breathing patterns. We present systematic experiments showing how physiologically-grounded synthetic data can be used in training camera-based multi-parameter cardiopulmonary sensing. We provide empirical evidence that heart and breathing rate measurement accuracy increases with the number of synthetic avatars in the training set. Furthermore, training with avatars with darker skin types leads to better overall performance than training with avatars with lighter skin types. Finally, we discuss the opportunities that synthetics present in the domain of camera-based physiological sensing and limitations that need to be overcome.

Effect of Ambient Lighting and Skin Tone on Estimation of Heart Rate and Pulse Transit Time from Video Plethysmography

Video-based photoplethysmography (vPPG) enables remote and contactless detection of the peripheral pulse of blood flow. This provides a potential mean to extract heart rate (HR) and pulse transit time (PTT) for the purpose of remote health monitoring. The accuracy of average HR and PTT extracted from a two-minute vPPG recording has been investigated at six different lighting conditions among participants with a range of Fitzpatrick skin scores. 12 healthy volunteers (6 females, 27 ± 6 years) were recruited. The video, electrocardiogram and finger PPG were acquired from immobile resting subjects. The vPPG signals from red, green and blue channels, and a combination of those were investigated. The vPPG signals were extracted from two regions of interest (ROIs): one on the forehead and one on the palm of the left hand. The estimated HR error (HR-error) was significantly lower for vPPG from green channels in both ROIs (ROI1 [p<0.001], ROI2 [p<0.05]). The signal from ROI1 demonstrated lower HR-error than ROI2 (p<0.001). HR-error from the darkest lighting conditions (Lumen 1 and 2) were significantly higher than the others (p<0.05). Furthermore, HR-error showed a positive correlation with skin tone scores in every lighting condition. However, at brighter lighting intensity, HR-error was independent of the skin tone score. PTT calculated from vPPG (vPTT) were compared between the 6 levels of lightings and the result was significantly different (p<0.05). In darker lighting conditions, the vPTT increased. Pulse arrival time measured from PPG (PAT-PPG) was calculated, and a positive correlation was found between the ratio of vPTT/PAT-PPG and skin tone score at six different lightings. However, this dependency decreases in brighter lighting intensity. These results suggest that HR-error and the ratio of vPTT/PAT increase with darker skins and at darker backgrounds. However, at brighter lighting conditions, the skin tone score is not a confounder of vPPG accuracy.

Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

Percutaneous thermal ablation has proven to be an effective modality for treating both benign and malignant tumours in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50°C, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumour destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of-the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non-invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow.

Using Non-Contact Imaging Photoplethysmography to Recover Diurnal Patterns in Heart Rate

Daily patterns in cardiovascular signals can reveal important information about physiological processes, health and well-being. Traditionally, contact sensors have been used to collect longitudinal data of this kind. However, recent advances in non-contact imaging techniques have led to algorithms that can be used to measure vital signs unobtrusively. Imaging methods are highly scalable due to the availability of webcams and computing devices making them attractive for longitudinal, in-situ measurement. Using a software tool we captured over 1,000 hours of non-contact heart rate measurements, via imaging photoplethysmography. Using these data we were able to recover diurnal patterns in heart rate during the working day. Non-contact sensing techniques hold much promise but also raise ethical issues that need to be addressed seriously within the biomedical engineering community.

iPhys: An Open Non-Contact Imaging-Based Physiological Measurement Toolbox

Imaging-based, non-contact measurement of physiology (including imaging photoplethysmography and imaging ballistocardiography) is a growing field of research. There are several strengths of imaging methods that make them attractive. They remove the need for uncomfortable contact sensors and can enable spatial and concomitant measurement from a single sensor. Furthermore, cameras are ubiquitous and often low-cost solutions for sensing. Open source toolboxes help accelerate the progress of research by providing a means to compare new approaches against standard implementations of the state-of-the-art. We present an open source imaging-based physiological measurement toolbox with implementations of many of the most frequently employed computational methods. We hope that this toolbox will contribute to the advancement of noncontact physiological sensing methods. Code: https://github.com/danmcduff/iphys-toolbox.

Video-based heart rate monitoring across a range of skin pigmentations during an acute hypoxic challenge

The robust monitoring of heart rate from the video-photoplethysmogram (video-PPG) during challenging conditions requires new analysis techniques. The work reported here extends current research in this area by applying a motion tolerant algorithm to extract high quality video-PPGs from a cohort of subjects undergoing marked heart rate changes during a hypoxic challenge, and exhibiting a full range of skin pigmentation types. High uptimes in reported video-based heart rate (HRvid) were targeted, while retaining high accuracy in the results. Ten healthy volunteers were studied during a double desaturation hypoxic challenge. Video-PPGs were generated from the acquired video image stream and processed to generate heart rate. HRvid was compared to the pulse rate posted by a reference pulse oximeter device (HRp). Agreement between video-based heart rate and that provided by the pulse oximeter was as follows: Bias = - 0.21 bpm, RMSD = 2.15 bpm, least squares fit gradient = 1.00 (Pearson R = 0.99, p < 0.0001), with a 98.78% reporting uptime. The difference between the HRvid and HRp exceeded 5 and 10 bpm, for 3.59 and 0.35% of the reporting time respectively, and at no point did these differences exceed 25 bpm. Excellent agreement was found between the HRvid and HRp in a study covering the whole range of skin pigmentation types (Fitzpatrick scales I-VI), using standard room lighting and with moderate subject motion. Although promising, further work should include a larger cohort with multiple subjects per Fitzpatrick class combined with a more rigorous motion and lighting protocol.

Patch-Based Label Fusion with Structured Discriminant Embedding for Hippocampus Segmentation

Automatic and accurate segmentation of hippocampal structures in medical images is of great importance in neuroscience studies. In multi-atlas based segmentation methods, to alleviate the misalignment when registering atlases to the target image, patch-based methods have been widely studied to improve the performance of label fusion. However, weights assigned to the fused labels are usually computed based on predefined features (e.g. image intensities), thus being not necessarily optimal. Due to the lack of discriminating features, the original feature space defined by image intensities may limit the description accuracy. To solve this problem, we propose a patch-based label fusion with structured discriminant embedding method to automatically segment the hippocampal structure from the target image in a voxel-wise manner. Specifically, multi-scale intensity features and texture features are first extracted from the image patch for feature representation. Margin fisher analysis (MFA) is then applied to the neighboring samples in the atlases for the target voxel, in order to learn a subspace in which the distance between intra-class samples is minimized and the distance between inter-class samples is simultaneously maximized. Finally, the k-nearest neighbor (kNN) classifier is employed in the learned subspace to determine the final label for the target voxel. In the experiments, we evaluate our proposed method by conducting hippocampus segmentation using the ADNI dataset. Both the qualitative and quantitative results show that our method outperforms the conventional multi-atlas based segmentation methods.

Design of a Novel Electrode of Radiofrequency Ablation for Large Tumors: A Finite Element Study

The aim of the study was to design a novel radiofrequency (RF) electrode for larger and rounder ablation volumes and its ability to achieve the complete ablation of liver tumors larger than 3 cm in diameter using finite element method. A new RF expandable electrode comprising three parts (i.e., insulated shaft, changing shaft, and hooks) was designed. Two modes of this new electrode, such as monopolar expandable electrode (MEE) and hybrid expandable electrode (HEE), and a commercial expandable electrode (CEE) were investigated using liver tissue with (scenario I) and without (scenario II) a liver tumor. A temperature-controlled radiofrequency ablation (RFA) protocol with a target temperature of 95 °C and an ablation time of 15 min was used in the study. Both the volume and shape of the ablation zone were examined for all RF electrodes in scenario I. Then, the RF electrode with the best performance in scenario I and CEE were used to ablate a large liver tumor with the diameter of 3.5 cm (scenario II) to evaluate the effectiveness of complete tumor ablation of the designed RF electrode. In scenario I, the ablation volumes of CEE, HEE, and MEE were 12.11 cm3, 33.29 cm3, and 48.75 cm3, respectively. The values of sphericity index (SI) of CEE, HEE, and MEE were 0.457, 0.957, and 0.976, respectively. The best performance was achieved by using MEE. In scenario II, the ablation volumes of MEE and CEE were 71.59 cm3 and 19.53 cm3, respectively. Also, a rounder ablation volume was achieved by using MEE compared to CEE (SI: 0.978 versus 0.596). The study concluded that: (1) compared with CEE, both MEE and HEE get larger and rounder ablation volumes due to the larger electrode–tissue interface and rounder shape of hook deployment; (2) MEE has the best performance in getting a larger and rounder ablation volume; and (3) computer simulation result shows that MEE is also able to ablate a large liver tumor (i.e., 3.5 cm in diameter) completely, which has at least 0.785 cm safety margin.