Assessment of the axial resolution of a compact gamma camera with coded aperture collimator

PURPOSE

Handheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods.

METHODS

An experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holes of 0.08 mm in diameter in a 0.11 mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12-100 mm. For each source position, the detector image was reconstructed in 0.5 mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM).

RESULTS

While taking 4400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3 mm and 1.8 mm at 12 mm to 42.2 mm and 13.5 mm at 100 mm for MURA Decoding and 3D-MLEM respectively.

CONCLUSION

Our results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging.

Point Cloud Registration for Measuring Shape Dependence of Soft Tissue Deformation by Digital Twins in Head and Neck Surgery

Introduction

A 2½ D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts.

Methods

An ML model was trained using simulated surface scan data obtained from tumor resections in a pig head cadaver model. It hereby uses 438 2½ D scans of the tissue surface. Tissue shift was induced by a temperature change from 7.91 ± 4.1°C to 36.37 ± 1.28°C.

Results

Digital twins were generated from various branched and compact resection cavities (RCs) and cut tissues (CT). A temperature increase induced a tissue shift with a significant volume increase of 6 mL and 2 mL in branched and compact RCs, respectively (p = 0.0443; 0.0157). The volumes of branched and compact CT were decreased by 3 and 4 mL (p < 0.001). In the warm state, RC and CT no longer fit together because of the significant tissue deformation. Although not significant, the compact RC showed a greater tissue deformation of 1 μL than the branched RC with 0.5 μL induced by the temperature change (p = 0.7874). The branched and compact CT forms responded almost equally to changes in temperature (p = 0.1461).

Conclusions

The simulation experiment of induced soft tissue deformation using digital twins based on 2½ D point cloud models proved that our method helps to quantify shape-dependent tissue shifts.

Refocusing of Attention on Positive Events Using Monitoring-Based Feedback and Microinterventions for Patients With Chronic Musculoskeletal Pain in the PerPAIN Randomized Controlled Trial: Protocol for a Microrandomized Trial

BACKGROUND

Chronic musculoskeletal pain (CMSP) affects between 13% and 47% of the population, with a global growth rate of 20.3% within the last 15 years, suggesting that there is a high need for effective treatments. Pain diaries have long been a common tool in nonpharmacological pain treatment for monitoring and providing feedback on patients' symptoms in daily life. More recently, positive refocusing techniques have come to be used, promoting pain-free episodes and positive outcomes rather than focusing on managing the pain.

OBJECTIVE

This study aims to evaluate the feasibility (ie, acceptability, intervention adherence, and fidelity) and initial signals of efficacy of the PerPAIN app, an ecological momentary intervention for patients with CMSP. The app comprises digitalized monitoring using the experience sampling method (ESM) and feedback. In addition, the patients receive 3 microinterventions targeted at refocusing of attention on positive events.

METHODS

In a microrandomized trial, we will recruit 35 patients with CMSP who will be offered the app for 12 weeks. Participants will be prompted to fill out 4 ESM monitoring questionnaires a day assessing information on their current context and the proximal outcome variables: absence of pain, positive mood, and subjective activity. Participants will be randomized daily and weekly to receive no feedback, verbal feedback, or visual feedback on proximal outcomes assessed by the ESM. In addition, the app will encourage participants to complete 3 microinterventions based on positive psychology and cognitive behavioral therapy techniques. These microinterventions are prompts to report joyful moments and everyday successes or to plan pleasant activities. After familiarizing themselves with each microintervention individually, participants will be randomized daily to receive 1 of the 3 exercises or none. We will assess whether the 2 feedback types and the 3 microinterventions increase proximal outcomes at the following time point. The microrandomized trial is part of the PerPAIN randomized controlled trial (German Clinical Trials Register DRKS00022792) investigating a personalized treatment approach to enhance treatment outcomes in CMSP.

RESULTS

Approval was granted by the Ethics Committee II of the University of Heidelberg on August 4, 2020. Recruitment for the microrandomized trial began in May 2021 and is ongoing at the time of submission. By October 10, 2022, a total of 24 participants had been enrolled in the microrandomized trial.

CONCLUSIONS

This trial will provide evidence on the feasibility of the PerPAIN app and the initial signals of efficacy of the different intervention components. In the next step, the intervention would need to be further refined and investigated in a definitive trial. This ecological momentary intervention presents a potential method for offering low-level accessible treatment to a wide range of people, which could have substantial implications for public health by reducing disease burden of chronic pain in the population.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/43376.

Artificial intelligence directed development of a digital twin to measure soft tissue shift during head and neck surgery

Digital twins derived from 3D scanning data were developed to measure soft tissue deformation in head and neck surgery by an artificial intelligence approach. This framework was applied suggesting feasibility of soft tissue shift detection as a hitherto unsolved problem. In a pig head cadaver model 104 soft tissue resection had been performed. The surface of the removed soft tissue (RTP) and the corresponding resection cavity (RC) was scanned (N = 416) to train an artificial intelligence (AI) with two different 3D object detectors (HoloLens 2; ArtecEva). An artificial tissue shift (TS) was created by changing the tissue temperature from 7,91±4,1°C to 36,37±1,28°C. Digital twins of RTP and RC in cold and warm conditions had been generated and volumes were calculated based on 3D surface meshes. Significant differences in number of vertices created by the different 3D scanners (HoloLens2 51313 vs. ArtecEva 21694, p<0.0001) hence result in differences in volume measurement of the RTC (p = 0.0015). A significant TS could be induced by changing the temperature of the tissue of RC (p = 0.0027) and RTP (p = <0.0001). RC showed more correlation in TS by heating than RTP with a volume increase of 3.1 μl or 9.09% (p = 0.449). Cadaver models are suitable for training a machine learning model for deformable registration through creation of a digital twin. Despite different point cloud densities, HoloLens and ArtecEva provide only slightly different estimates of volume. This means that both devices can be used for the task.TS can be simulated and measured by temperature change, in which RC and RTP react differently. This corresponds to the clinical behaviour of tumour and resection cavity during surgeries, which could be used for frozen section management and a range of other clinical applications.

A theoretical study of H2 O2 as the surrogate of dose in minibeam radiotherapy, with a diffusion model considering radical removal process

BACKGROUND

Minibeam radiation therapy (MBRT) is an innovative dose delivery method with the potential to spare normal tissue while achieving similar tumor control as conventional radiotherapy. However, it is difficult to use a single dose parameter, such as mean dose, to compare different patterns of MBRT due to the spatially fractionated radiation. Also, the mechanism leading to the biological effects is still unknown.

PURPOSE

This study aims to demonstrate that the hydrogen peroxide (H2 O2 ) distribution could serve as a surrogate of dose distribution when comparing different patterns of MBRT.

METHODS

A free diffusion model (FDM) for H2 O2 developed with Fick's second law was compared with a previously published model based on Monte Carlo & convolution method. Since cells form separate compartments that can eliminate H2 O2 radicals diffusing inside the cell, a term describing the elimination was introduced into the equation. The FDM and the diffusion model considering removal (DMCR) were compared by simulating various dose rate irradiation schemes and uniform irradiation. Finally, the DMCR was compared with previous microbeam and minibeam animal experiments.

RESULTS

Compared with a previous Monte Carlo & Convolution method, this analytical method provides more accurate results. Furthermore, the new model shows H2 O2 concentration distribution instead of the time to achieve a certain H2 O2 uniformity. The comparison between FDM and DMCR showed that H2 O2 distribution from FDM varied with dose rate irradiation, while DMCR had consistent results. For uniform irradiation, FDM resulted in a Gaussian distribution, while the H2 O2 distribution from DMCR was close to the dose distribution. The animal studies' evaluation showed a correlation between the H2 O2 concentration in the valley region and treatment outcomes.

CONCLUSION

DMCR is a more realistic model for H2 O2 simulation than the FDM. In addition, the H2 O2 distribution can be a good surrogate of dose distribution when the minibeam effect could be observed.

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction

Objective. The image reconstruction of ultrasound computed tomography is computationally expensive with conventional iterative methods. The fully learned direct deep learning reconstruction is promising to speed up image reconstruction significantly. However, for direct reconstruction from measurement data, due to the lack of real labeled data, the neural network is usually trained on a simulation dataset and shows poor performance on real data because of the simulation-to-real gap.Approach. To improve the simulation-to-real generalization of neural networks, a series of strategies are developed including a Fourier-transform-integrated neural network, measurement-domain data augmentation methods, and a self-supervised-learning-based patch-wise preprocessing neural network. Our strategies are evaluated on both the simulation dataset and real measurement datasets from two different prototype machines.Main results. The experimental results show that our deep learning methods help to improve the neural networks' robustness against noise and the generalizability to real measurement data.Significance. Our methods prove that it is possible for neural networks to achieve superior performance to traditional iterative reconstruction algorithms in imaging quality and allow for real-time 2D-image reconstruction. This study helps pave the path for the application of deep learning methods to practical ultrasound tomography image reconstruction based on simulation datasets.

The PerPAIN trial: a pilot randomized controlled trial of personalized treatment allocation for chronic musculoskeletal pain-a protocol

BACKGROUND

The therapy of chronic musculoskeletal pain (CMSP) is complex and the treatment results are often insufficient despite numerous therapeutic options. While individual patients respond very well to specific interventions, other patients show no improvement. Personalized treatment assignment offers a promising approach to improve response rates; however, there are no validated cross-disease allocation algorithms available for the treatment of chronic pain in validated personalized pain interventions. This trial aims to test the feasibility and safety of a personalized pain psychotherapy allocation with three different treatment modules and estimate initial signals of efficacy and utility of such an approach compared to non-personalized allocation.

METHODS

This is a randomized, controlled assessor-blinded pilot trial with a multifactorial parallel arm design. CMSP patients (n = 105) will be randomly assigned 1:1 to personalized or non-personalized treatment based on a cluster assignment of the West Haven-Yale Multidimensional Pain Inventory (MPI). In the personalized assignment condition, patients with high levels of distress receive an emotional distress-tailored intervention, patients with pain-related interference receive an exposure/extinction-tailored treatment intervention and patients who adapt relatively well to the pain receive a low-level smartphone-based activity diary intervention. In the control arm, patients receive one of the two non-matching interventions. Effect sizes will be calculated for change in core pain outcome domains (pain intensity, physical and emotional functioning, stress experience, participant ratings of improvement and satisfaction) after intervention and at follow-up. Feasibility and safety outcomes will assess rates of recruitment, retention, adherence and adverse events. Additional data on neurobiological and psychological characteristics of the patients are collected to improve treatment allocation in future studies.

CONCLUSION

Although the call for personalized treatment approaches is widely discussed, randomized controlled trials are lacking. As the personalization of treatment approaches is challenging, both allocation and intervention need to be dynamically coordinated. This study will test the feasibility and safety of a novel study design in order to provide a methodological framework for future multicentre RCTs for personalized pain psychotherapy.

TRIAL REGISTRATION

German Clinical Trials Register, DRKS00022792 ( https://www.drks.de ). Prospectively registered on 04/06/2021.

Remote sensing array (RSA) for linac beam monitoring

The purpose of the present work is to evaluate the feasibility of a novel real-time beam monitoring device for medical linacs which remotely senses charge carriers produced in air by the beam without intersecting and attenuating the beamline. The primary goal is to elaborate a theoretical concept of a possible detector geometry and underlying physical model that allows for determination of clinically relevant beam data in real time, namely MLC leaf positions and dose rate. The detector consists of two opposing electrode arrays arranged in two possible orientations around the beamline. Detection of charge carriers is governed by electromagnetic principles described by Shockley–Ramo theorem. Ions produced by ionization of the air column upstream of patient move laterally in an external electric field. According to the method of images, mirror charges and mirror currents are formed in the strip electrodes. Determination of MU rate and MLC positions using the measured signal requires solution of an inverse problem. In the present work we adopted a Least-Square approach and characterized detector response and sensitivity to detection of beam properties for different electrode geometries and MLC shapes. Results were dependent on MLC field shape and the leaf position within the active volume. The accuracy of determination of leaf positions were in the sub-mm range (up to 0.25–1 mm). Additionally, detector sensitivity was quantified by simulating ions/pulse delivered with a radiation transport deterministic computation in 1D in CEPXS/ONEDANT. For a 6 MV linac pulse, signal amplitude per pulse was estimated to be in the lower pA to fA range. We computationally demonstrated feasibility of the remote sensing detector capable of measuring beam parameters such as MLC leaf positions and dose range for each pulse. Future work should focus on optimizing the electrode geometry to increase sensitivity and better reconstruction algorithms to provide more accurate solutions of the inverse problem.

Normalization of HE-stained histological images using cycle consistent generative adversarial networks

Background

Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques.

Methods

In this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network G_B that learns to map an image X from a source domain A to a target domain B, i.e. G_B:X_A→X_B. In addition, a discriminator network D_B is trained to distinguish whether an image from domain B is real or generated. The same process is applied to another generator-discriminator pair (G_A,D_A), for the inverse mapping G_A:X_B→X_A. Cycle consistency ensures that a generated image is close to its original when being mapped backwards (G_A(G_B(X_A))≈X_A and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma data set for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. In addition, we apply stain normalization on pathological lymph node data from our institute and test the gain from normalization on a ResNet classifier pre-trained on the Camelyon16 data set.

Results

Qualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%.

Conclusions

CycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch. The data set supporting the solutions is available at 10.11588/data/8LKEZF.

Augmented Reality with HoloLens® in Parotid Tumor Surgery: A Prospective Feasibility Study

INTRODUCTION

Augmented reality can improve planning and execution of surgical procedures. Head-mounted devices such as the HoloLens® (Microsoft, Redmond, WA, USA) are particularly suitable to achieve these aims because they are controlled by hand gestures and enable contactless handling in a sterile environment.

OBJECTIVES

So far, these systems have not yet found their way into the operating room for surgery of the parotid gland. This study explored the feasibility and accuracy of augmented reality-assisted parotid surgery.

METHODS

2D MRI holographic images were created, and 3D holograms were reconstructed from MRI DICOM files and made visible via the HoloLens. 2D MRI slices were scrolled through, 3D images were rotated, and 3D structures were shown and hidden only using hand gestures. The 3D model and the patient were aligned manually.

RESULTS

The use of augmented reality with the HoloLens in parotic surgery was feasible. Gestures were recognized correctly. Mean accuracy of superimposition of the holographic model and patient's anatomy was 1.3 cm. Highly significant differences were seen in position error of registration between central and peripheral structures (p = 0.0059), with a least deviation of 10.9 mm (centrally) and highest deviation for the peripheral parts (19.6-mm deviation).

CONCLUSION

This pilot study offers a first proof of concept of the clinical feasibility of the HoloLens for parotid tumor surgery. Workflow is not affected, but additional information is provided. The surgical performance could become safer through the navigation-like application of reality-fused 3D holograms, and it improves ergonomics without compromising sterility. Superimposition of the 3D holograms with the surgical field was possible, but further invention is necessary to improve the accuracy.

Ultrasound transmission tomography image reconstruction with a fully convolutional neural network

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction method based on a convolutional neural network. The image is reconstructed via one forward propagation of the network given input sensor data, which is much faster than the reconstruction using conventional iterative optimization methods. To transform the ultrasound measured data in the sensor domain into the reconstructed image in the image domain, we apply multiple down-scaling and up-scaling convolutional units to efficiently increase the number of hidden layers with a large receptive and projective field that can cover all elements in inputs and outputs, respectively. For dataset generation, a paraxial approximation forward model is used to simulate ultrasound measurement data. The neural network is trained with a dataset derived from natural images in ImageNet and tested with a dataset derived from medical images in OA-Breast Phantom dataset. Test results show the superior efficiency of the proposed neural network to other reconstruction algorithms including popular neural networks. When compared with conventional iterative optimization algorithms, our neural network can reconstruct a 110 × 86 image more than 20 times faster on a CPU and 1000 times faster on a GPU with comparable image quality and is also more robust to noise.

Imaging and Characterization of Sustained Gadolinium Nanoparticle Release from Next Generation Radiotherapy Biomaterial

Smart radiotherapy biomaterials (SRBs) present a new opportunity to enhance image-guided radiotherapy while replacing routinely used inert radiotherapy biomaterials like fiducials. In this study the potential of SRBs loaded with gadolinium-based nanoparticles (GdNPs) is investigated for magnetic resonance imaging (MRI) contrast. GdNP release from SRB is quantified and modelled for accurate prediction. SRBs were manufactured similar to fiducials, with a cylindrical shell consisting of poly(lactic-co-glycolic) acid (PLGA) and a core loaded with GdNPs. Magnetic resonance imaging (MRI) contrast was investigated at 7T in vitro (in agar) and in vivo in subcutaneous tumors grown with the LLC1 lung cancer cell line in C57/BL6 mice. GdNPs were quantified in-phantom and in tumor and their release was modelled by the Weibull distribution. Gd concentration was linearly fitted to the R₁ relaxation rate with a detection limit of 0.004 mmol/L and high confidence level (R² = 0.9843). GdNP loaded SRBs in tumor were clearly visible up to at least 14 days post-implantation. Signal decrease during this time showed GdNP release in vivo, which was calculated as 3.86 ± 0.34 µg GdNPs release into the tumor. This study demonstrates potential and feasibility for SRBs with MRI-contrast, and sensitive GdNP quantification and release from SRBs in a preclinical animal model. The feasibility of monitoring nanoparticle (NP) concentration during treatment, allowing dynamic quantitative treatment planning, is also discussed.

Augmented reality with HoloLens in parotid surgery: how to assess and to improve accuracy

PURPOSE

Augmented reality improves planning and execution of surgical procedures. The aim of this study was to evaluate the feasibility of a 3D augmented reality hologram in live parotic surgery. Another goal was to develop an accuracy measuring instrument and to determine the accuracy of the system.

METHODS

We created a software to build and manually align 2D and 3D augmented reality models generated from MRI data onto the patient during surgery using the HoloLens^® 1 (Microsoft Corporation, Redmond, USA). To assess the accuracy of the system, we developed a specific measuring tool applying a standard electromagnetic navigation device (Fiagon GmbH, Hennigsdorf, Germany).

RESULTS

The accuracy of our system was measured during real surgical procedures. Training of the experimenters and the use of fiducial markers significantly reduced the accuracy of holographic system (p = 0.0166 and p = 0.0132). Precision of the developed measuring system was very high with a mean error of the basic system of 1.3 mm. Feedback evaluation demonstrated 86% of participants agreed or strongly agreed that the HoloLens will play a role in surgical education. Furthermore, 80% of participants agreed or strongly agreed that the HoloLens is feasible to be introduced in clinical routine and will play a role within surgery in the future.

CONCLUSION

The use of fiducial markers and repeated training reduces the positional error between the hologram and the real structures. The developed measuring device under the use of the Fiagon navigation system is suitable to measure accuracies of holographic augmented reality images of the HoloLens.

Towards customizable thin-panel low-Z detector arrays: electrode design for increased spatial resolution ion chamber arrays

The purpose of the present development is to employ 3D printing to prototype an ion chamber array with a scalable design potentially allowing increased spatial resolution and a larger active area. An additional goal is to design and fabricate a custom size thin-panel detector array with low-Z components. As a proof of principle demonstration, a medium size detector array with 30 × 30 air-vented ion chambers was 3D-printed using PLA as frame for the electrodes. The active-area is 122 mm × 120 mm with 4 × 4 mm² spatial resolution. External electrodes are cylindrical and made from conductive PLA. Internal electrodes are made from microwire. The array is symmetric with respect to the central plane and its thickness is 10 mm including build-up/-down plates of 2.5 mm thickness. Data acquisition is realized by biasing only selected chamber rows and reading only 30 chambers at a time. To test the device for potential clinical applications, 1D dose profiles and 2D dose maps with various square and irregular fields were measured. The overall agreement with the reference doses (film and treatment planning system) was satisfactory, but the measured dose differs in the penumbra region and in the field size dependence. Both of these features are related to the thin walls between neighboring ion chambers and different lateral phantom scatter in the detector panel vs homogeneous material. We demonstrated feasibility of radiation detector arrays with minimal number of readout channels and low-cost electronics. The acquisition scheme based on selected row or column 'activation' by bias voltage is not practical for 2D dosimetry but it allows for rapid turn-around when testing of custom arrays with the aid of multiple 1D dose profiles. Future progress in this area includes overcoming the limitations due high chamber packing ratio, which leads to the lateral scattering effects.

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising

OBJECTIVE

Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. However, the low signal-to-noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Quality improvement of the fetal ECG is of great importance for providing accurate information to enable support in medical decision-making. In this paper we propose the use of artificial intelligence for the task of one-channel fetal ECG enhancement as a post-processing step after maternal ECG suppression.

APPROACH

We propose a deep fully convolutional encoder-decoder framework, learning end-to-end mappings from noise-contaminated fetal ECGs to clean ones. Symmetric skip-layer connections are used between corresponding convolutional and transposed convolutional layers to help recover the signal details.

MAIN RESULTS

Experiments on synthetic data show an average improvement of 7.5 dB in the signal-to-noise ratio (SNR) for input SNRs in the range of  -15 to 15 dB. Application of the method with real signals and subsequent ECG interval analysis demonstrates a root mean square error of 9.9 and 14 ms for the PR and QT intervals, respectively, when compared with simultaneous scalp measurements. The proposed network can achieve substantial noise removal on both synthetic and real data. In cases of highly noise-contaminated signals some morphological features might be unreliably reconstructed.

SIGNIFICANCE

The presented method has the advantage of preserving individual variations in pulse shape and beat-to-beat intervals. Moreover, no prior knowledge on the power spectra of the noise or the pulse locations is required.

Analysis of Impact of Humidity and Temperature on Excimer Laser Ablation of Polyethylene Terephthalate, Polymethylmethacrylate, and Porcine Corneal Tissue

BACKGROUND AND OBJECTIVES

To analyze the impact of humidity and temperature on excimer laser ablation of polyethylene terephthalate (PET), polymethylmethacrylate (PMMA) and porcine corneal tissue, and an ablation model to compensate for the temperature and humidity changes on ablation efficiency.

STUDY DESIGN/MATERIALS AND METHODS

The study was conducted using an AMARIS 1050RS (Schwind eye-tech-solutions) placed inside a climate chamber at ACTS. Ablations were performed on PET, PMMA, and porcine cornea. The impact of a wide range of temperature (~18°C to ~30°C) and relative humidity (~25% to ~80%) on laser ablation outcomes was tested using nine climate test settings. For porcine eyes, change in defocus was calculated from the difference of post-ablation to pre-ablation average keratometry readings. Laser scanning deflectometry was performed to measure refractive change achieved in PMMA. Multiple linear regression was performed using the least square method with predictive factors: temperature, relative humidity, time stamp. Influence of climate settings was modeled for pulse energy, pulse fluence, ablation efficiency on PMMA and porcine cornea tissue.

RESULTS

Temperature changes did not affect laser pulse energy, pulse fluence (PET), and ablation efficiency (on PMMA or porcine corneal tissue) significantly. Changes in relative humidity were critical and significantly affected laser pulse energy, high fluence and low fluence. The opposite trend was observed between the ablation performance on PMMA and porcine cornea.

CONCLUSIONS

The proposed well-fitting multi-linear model can be utilized for compensation of temperature and humidity changes on ablation efficiency. Based on this model, a working window for optimum operation has been found (temperature 18°C to 28°C and relative humidity 25% to 65%) for a maximum deviation of ±2.5% in ablation efficiency in PMMA and porcine corneal tissue. Lasers Surg. Med. © 2019 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.

Evaluation of a new image reconstruction method for digital breast tomosynthesis: effects on the visibility of breast lesions and breast density

OBJECTIVE

To compare image quality and breast density of two reconstruction methods, the widely-used filtered-back projection (FBP) reconstruction and the iterative heuristic Bayesian inference reconstruction (Bayesian inference reconstruction plus the method of total variation applied, HBI).

METHODS

Thirty-two clinical DBT data sets with malignant and benign findings, n = 27 and 17, respectively, were reconstructed using FBP and HBI. Three experienced radiologists evaluated the images independently using a 5-point visual grading scale and classified breast density according to the American College of Radiology Breast Imaging-Reporting And Data System Atlas, fifth edition. Image quality metrics included lesion conspicuity, clarity of lesion borders and spicules, noise level, artifacts surrounding the lesion, visibility of parenchyma and breast density.

RESULTS

For masses, the image quality of HBI reconstructions was superior to that of FBP in terms of conspicuity,clarity of lesion borders and spicules (p < 0.01). HBI and FBP were not significantly different in calcification conspicuity. Overall, HBI reduced noise and supressed artifacts surrounding the lesions better (p < 0.01). The visibility of fibroglandular parenchyma increased using the HBI method (p < 0.01). On average, five cases per radiologist were downgraded from BI-RADS breast density category C/D to A/B.

CONCLUSION

HBI significantly improves lesion visibility compared to FBP. HBI-visibility of breast parenchyma increased, leading to a lower breast density rating. Applying the HBIR algorithm should improve the diagnostic performance of DBT and decrease the need for additional imaging in patients with dense breasts.

ADVANCES IN KNOWLEDGE

Iterative heuristic Bayesian inference (HBI) image reconstruction substantially improves the image quality of breast tomosynthesis leading to a better visibility of breast carcinomas and reduction of the perceived breast density compared to the widely-used filtered-back projection (FPB) reconstruction. Applying HBI should improve the accuracy of breast tomosynthesis and reduce the number of unnecessary breast biopsies. It may also reduce the radiation dose for the patients, which is especially important in the screening context.

Multiple retrieval case-based reasoning for incomplete datasets

The performance of case-based reasoning (CBR) depends on an accurate ranking of similar cases in the retrieval phase that affects all subsequent phases and profits from the potential of large databases. Unfortunately, growing databases come along with a rising amount of missing data that reduces the stability of the ranking since incomplete cases cannot be ranked as reliable as complete ones. In context of CBR hardly any work was done so far to rigorously analyze the impact of missing data and solutions to tackle this issue. In particular, a generalized solution which is able to process data under different missingness conditions for different variable types is missing. In this paper we present a multiple retrieval case-based reasoning (MRCBR) framework for incomplete databases that provides a statistically accurate ranking for similar cases. It unifies the advantages of multiple imputation and CBR while it preserves both the data distribution and database structure. Built as generalized CBR system, MRCBR was optimized and tested for medical decision support but can be extended to any CBR requirement as well. It is suitable for numerical and categorical variables and all sorts of missingness conditions. The approach was compared to eight competing methods applicable to handle incomplete databases in context of CBR. The comparison to the true ranking was based on two various error measures. In the evaluation we tested four representative scenarios that considered different conditions for missing data analysis. The outcome for every method in each scenario resulted in 200 miscellaneous setups. MRCBR outperforms all compared CBR methods in presence of missing data and shows reliable and stable results in every scenario. Especially with larger databases and rising number of incomplete variables it enlarges its lead to all other methods. Our study demonstrates that missing data must not be ignored when a correct CBR outcome is required.

Modeling sphere dynamics in blood vessels for SIRT pre-planning - To fathom the potential and limitations

For selective internal radiation therapy (SIRT) the calculation of the 3D distribution of spheres based on individual blood flow properties is still an open and relevant research question. The purpose of this work is to develop and analyze a new treatment planning method for SIRT to calculate the absorbed dose distribution. For this intention, flow dynamics of the SIRT-spheres inside the blood vessels was simulated. The challenge is treatment planning solely using high-resolution imaging data available before treatment. The resolution required to reliably predict the sphere distribution and hence the dose was investigated. For this purpose, arteries of the liver were segmented from a contrast-enhanced angiographic CT. Due to the limited resolution of the given CT, smaller vessels were generated via a vessel model. A combined 1D/3D-flow simulation model was implemented to simulate the final 3D distribution of spheres and dose. Results were evaluated against experimental data from Y90-PET. Analysis showed that the resolution of the vessels within the angiographic CT of about 0.5mm should be improved to a limit of about 150μm to reach a reliable prediction.

MIRACUM: Medical Informatics in Research and Care in University Medicine

INTRODUCTION

This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research.

OBJECTIVES

Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal.

GOVERNANCE AND POLICIES

Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor.

ARCHITECTURAL FRAMEWORK AND METHODOLOGY

The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept.

USE CASES

MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards.

RESULTS

Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued.

DISCUSSION

Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.

Using immunotherapy to boost the abscopal effect

More than 60 years ago, the effect whereby radiotherapy at one site may lead to regression of metastatic cancer at distant sites that are not irradiated was described and called the abscopal effect (from 'ab scopus', that is, away from the target). The abscopal effect has been connected to mechanisms involving the immune system. However, the effect is rare because at the time of treatment, established immune-tolerance mechanisms may hamper the development of sufficiently robust abscopal responses. Today, the growing consensus is that combining radiotherapy with immunotherapy provides an opportunity to boost abscopal response rates, extending the use of radiotherapy to treatment of both local and metastatic disease. In this Opinion article, we review evidence for this growing consensus and highlight emerging limitations to boosting the abscopal effect using immunotherapy. This is followed by a perspective on current and potential cross-disciplinary approaches, including the use of smart materials to address these limitations.

Potential of metric homotopy between intensity and geometry information for multi-modal 3D registration

This paper focuses on a novel strategy increasing robustness with respect to local optima when using Mutual Information (MI) in multi-modal image registration. This is realized by integrating additional geometry information in the cost function. Hereby, the main innovation is a generalization of multi-metric registration approaches by means of a metric homotopy. Particularly we realize a method which automatically determines the parameters of the metric homotopy. To construct the cost function independent of the choice of the optimizer, the weighting is defined as a function of one of the metrics instead of optimizer steps. In addition, a differentiable cost function is developed. In comparison to the commonly used technique to process an intensity based registration on different resolutions, the proposed method is three times faster with unchanged accuracy. It is also shown that in the presence of large landmark errors the proposed method outperforms an approach in accuracy in which both similarity functionals are applied one after the other. The method is evaluated on 3D multi-modal human brain data sets from the Retrospective Image Registration Evaluation Project (RIRE). The evaluation is performed using the evaluation website of the RIRE project to make the registration results of the proposed method easily comparable to other methods. Therefore, the presented results are also available online on the RIRE project page.

Live-Wires Using Path-Graphs

Objectives: This article discusses our new Path-Graph approach for the interactive Live-Wire segmentation method in 2D applied to pre-segmented data. Furthermore, we examine whether or not the Live-Lane extension provides advantages in combination with pre-segmentation.

Methods: We automatically over-segment the image data in a preprocessing step, using region growing with an automatic seed point generation. The Live-Wire algorithm is applied on this mosaic data by using the outlines of the homogeneous regions as the basis for graph building. We present a new definition of this underlying graph where the edges of the standard graphs are turning into vertices and the vertices of the new graph are defined by the edge connectivity in the standard graph. For better differentiation we name our new graph Path-Graph and the original defined graph Node-Graph.

Results: The quality evaluation is done by comparing our segmentation results with existing model data. We show that using the Path-Graph as basis for the Live-Wire algorithm instead of the Node-Graph allows for a finer segmentation. We achieve a reduction of incorrectly classified pixels by 20.66 per cent and a decrease of the mean boundary deviation by 11.61 per cent. Since savings on cost tree calculations are compensated by additional computation time required to compute the Live-Lanes, a performance loss of 2.41 per cent is measured.

Conclusions: Our redefinition of the underlying graph increases the quality of the Live-Wire segmentation. The Live-Lane extension in combination with pre-segmentation is not justified for our data.

Real-time, ray casting-based scatter dose estimation for c-arm x-ray system

Objectives

Dosimetric control of staff exposure during interventional procedures under fluoroscopy is of high relevance. In this paper, a novel ray casting approximation of radiation transport is presented and the potential and limitation vs. a full Monte Carlo transport and dose measurements are discussed.

Method

The x‐ray source of a Siemens Axiom Artix C‐arm is modeled by a virtual source model using single Gaussian‐shaped source. A Geant4‐based Monte Carlo simulation determines the radiation transport from the source to compute scatter from the patient, the table, the ceiling and the floor. A phase space around these scatterers stores all photon information. Only those photons are traced that hit a surface of phantom that represents medical staff in the treatment room, no indirect scattering is considered; and a complete dose deposition on the surface is calculated. To evaluate the accuracy of the approximation, both experimental measurements using Thermoluminescent dosimeters (TLDs) and a Geant4‐based Monte Carlo simulation of dose depositing for different tube angulations of the C‐arm from cranial‐caudal angle 0° and from LAO (Left Anterior Oblique) 0°–90° are realized. Since the measurements were performed on both sides of the table, using the symmetry of the setup, RAO (Right Anterior Oblique) measurements were not necessary.

Results

The Geant4‐Monte Carlo simulation agreed within 3% with the measured data, which is within the accuracy of measurement and simulation. The ray casting approximation has been compared to TLD measurements and the achieved percentage difference was −7% for data from tube angulations 45°–90° and −29% from tube angulations 0°–45° on the side of the x‐ray source, whereas on the opposite side of the x‐ray source, the difference was −83.8% and −75%, respectively. Ray casting approximation for only LAO 90° was compared to a Monte Carlo simulation, where the percentage differences were between 0.5–3% on the side of the x‐ray source where the highest dose usually detected was mainly from primary scattering (photons), whereas percentage differences between 2.8–20% are found on the side opposite to the x‐ray source, where the lowest doses were detected. Dose calculation time of our approach was 0.85 seconds.

Conclusion

The proposed approach yields a fast scatter dose estimation where we could run the Monte Carlo simulation only once for each x‐ray tube angulation to get the Phase Space Files (PSF) for being used later by our ray casting approach to calculate the dose from only photons which will hit an movable elliptical cylinder shaped phantom and getting an output file for the positions of those hits to be used for visualizing the scatter dose propagation on the phantom surface. With dose calculation times of less than one second, we are saving much time compared to using a Monte Carlo simulation instead. With our approach, larger deviations occur only in regions with very low doses, whereas it provides a high precision in high‐dose regions.

Automated Vessel Segmentation in Dual Energy Computed Tomography Data of the Pelvis and Lower Extremities

AIM

To evaluate the clinical feasibility of a newly developed, fully automatic vessel segmentation software with automatic structured bone elimination (ASBE) using graph-matching and subvoxel analysis.

MATERIALS AND METHODS

Dual energy computed tomography angiography (DECTA) data of 108 vessel segments were evaluated using the ASBE software and a commercial software against the digital subtraction angiography (DSA) standard of reference.

RESULTS

Using the ASBE software, sensitivity increased from 87.1% to 96.8% and data concordance with DSA increased from 64.5% to 88.6%, whereas specificity slightly decreased (79.2% vs. 87%) compared to the commercial software. Data concordance between ASBE software and DSA was especially high in severely stenosed (grade of stenosis >75%) blood vessels.

CONCLUSION

ASBE showed good concordance with the DSA standard of reference and non-inferiority compared to the commercial segmentation software. The main advantage of the ASBE software lies in its full automation and, thus, lower susceptibility to user prone errors.

Optimization of Single Voxel MR Spectroscopy Sequence Parameters and Data Analysis Methods for Thermometry in Deep Hyperthermia Treatments

OBJECTIVE

The difference in the resonance frequency of water and methylene moieties of lipids quantifies in magnetic resonance spectroscopy the absolute temperature using a predefined calibration curve. The purpose of this study was the investigation of peak evaluation methods and the magnetic resonance spectroscopy sequence (point-resolved spectroscopy) parameter optimization that enables thermometry during deep hyperthermia treatments.

MATERIALS AND METHODS

Different Lorentz peak-fitting methods and a peak finding method using singular value decomposition of a Hankel matrix were compared. Phantom measurements on organic substances (mayonnaise and pork) were performed inside the hyperthermia 1.5-T magnetic resonance imaging system for the parameter optimization study. Parameter settings such as voxel size, echo time, and flip angle were varied and investigated.

RESULTS

Usually all peak analyzing methods were applicable. Lorentz peak-fitting method in MATLAB proved to be the most stable regardless of the number of fitted peaks, yet the slowest method. The examinations yielded an optimal parameter combination of 8 cm³ voxel volume, 55 millisecond echo time, and a 90° excitation pulse flip angle.

CONCLUSION

The Lorentz peak-fitting method in MATLAB was the most reliable peak analyzing method. Measurements in homogeneous and heterogeneous phantoms resulted in optimized parameters for the magnetic resonance spectroscopy sequence for thermometry.

Influence of regional cerebral blood volume on voxel-based morphometry

The investigation of structural brain alterations is one focus in research of brain diseases like depression. Voxel-based morphometry (VBM) based on high-resolution 3D MRI images is a widely used non-invasive tool for such investigations. However, the result of VBM might be sensitive to local physiological parameters such as regional cerebral blood volume (rCBV) changes. In order to investigate whether rCBV changes may contribute to variation in VBM, we performed analyses in a study with the congenital learned helplessness (cLH) model for long-term findings. The 3D structural and rCBV data were acquired with T2 -weighted rapid acquisition with relaxation enhancement (RARE) pulse sequences. The group effects were determined by standard statistical parametric mapping (SPM) and biological parametric mapping (BPM) and examined further using atlas-based regions. In our genetic animal model of depression, we found co-occurrence of differences in gray matter volume and rCBV, while there was no evidence of significant interaction between both. However, the multimodal analysis showed similar gray matter differences compared with the standard VBM approach. Our data corroborate the idea that two group VBM differences might not be influenced by rCBV differences in genetically different strains. Copyright © 2016 John Wiley & Sons, Ltd.

Towards clinical implementation of ultrafast combined kV-MV CBCT for IGRT of lung cancer : Evaluation of registration accuracy based on phantom study

PURPOSE

Combined kV-MV cone-beam CT (CBCT) is a promising approach to accelerate imaging for patients with lung tumors treated with deep inspiration breath-hold. During a single breath-hold (15 s), a 3D kV-MV CBCT can be acquired, thus minimizing motion artifacts and increasing patient comfort. Prior to clinical implementation, positioning accuracy was evaluated and compared to clinically established imaging techniques.

METHODS AND MATERIALS

An inhomogeneous thorax phantom with four tumor-mimicking inlays was imaged in 10 predefined positions and registered to a planning CT. Novel kV-MV CBCT imaging (90° arc) was compared to clinically established kV-chest CBCT (360°) as well as nonclinical kV-CBCT and low-dose MV-CBCT (each 180°). Manual registration, automatic registration provided by the manufacturer and an additional in-house developed manufacturer-independent framework based on the MATLAB registration toolkit were applied.

RESULTS

Systematic setup error was reduced to 0.05 mm by high-precision phantom positioning with optical tracking. Stochastic mean displacement errors were 0.5 ± 0.3 mm in right-left, 0.4  ± 0.4 mm in anteroposterior and 0.0 ± 0.4 mm in craniocaudal directions for kV-MV CBCT with manual registration (maximum errors of no more than 1.4 mm). Clinical kV-chest CBCT resulted in mean errors of 0.2 mm (other modalities: 0.4-0.8 mm). Similar results were achieved with both automatic registration methods.

CONCLUSION

The comparison study of repositioning accuracy between novel kV-MV CBCT and clinically established volume imaging demonstrated that registration accuracy is maintained below 1 mm. Since imaging time is reduced to one breath-hold, kV-MV CBCT is ideal for image guidance, e.g., in lung stereotactic ablative radiotherapy.

Combined stereotactic biopsy and stepping-source interstitial irradiation of glioblastoma multiforme

Patients diagnosed with glioblastoma multiforme receiving stereotactic biopsy only either due to tumor localization or impaired clinical status face a devastating prognosis with very short survival times. One strategy to provide an initial cytoreductive and palliative therapy at the time of the stereotactic biopsy is interstitial irradiation through the pre-defined trajectory of the biopsy channel. We designed a novel treatment planning system and evaluated the treatment potential of a fixed-source and a stepping-source algorithm for interstitial radiosurgery on non-spherical glioblastoma in direct adjacency to risk structures. Using both setups, we show that radiation doses delivered to 100% of the gross tumor volume shifts from sub-therapeutic (10-12 Gy) to sterilizing single doses (25-30 Gy) when using the stepping source algorithm due to improved sparing of organs-at-risk. Specifically, the maximum doses at the brain stem were 100% of the PTV dose when a fixed central source and 38% when a stepping-source algorithm was used. We also demonstrated precision of intracranial target points and stability of superficial and deep trajectories using both a phantom and a body donor study. Our setup now for the first time provides a basis for a clinical proof-of-concept trial and may widen palliation options for patients with limited life expectancy that should not undergo time-consuming therapies.

Robustness of sweeping-window arc therapy treatment sequences against intrafractional tumor motion

PURPOSE

Due to the potentially periodic collimator dynamic in volumetric modulated arc therapy (VMAT) dose deliveries with the sweeping-window arc therapy (SWAT) technique, additional manifestations of dosimetric deviations in the presence of intrafractional motion may occur. With a fast multileaf collimator (MLC), and a flattening filter free dose delivery, treatment times close to 60 s per fraction are clinical reality. For these treatment sequences, the human breathing period can be close to the collimator sweeping period. Compared to a random arrangement of the segments, this will cause a further degradation of the dose homogeneity.

METHODS

Fifty VMAT sequences of potentially moving target volumes were delivered on a two dimensional ionization chamber array. In order to detect interplay effects along all three coordinate axes, time resolved measurements were performed twice--with the detector aligned in vertical (V) or horizontal (H) orientation. All dose matrices were then moved within a simulation software by a time-dependent motion vector. The minimum relative equivalent uniform dose EUDr,m for all breathing starting phases was determined for each amplitude and period. Furthermore, an estimation of periods with minimum EUD was performed. Additionally, LINAC logfiles were recorded during plan delivery. The MLC, jaw, gantry angle, and monitor unit settings were continuously saved and used to calculate the correlation coefficient between the target motion and the dose weighed collimator motion component for each direction (CC, LR, AP) separately.

RESULTS

The resulting EUDr,m were EUDr,m(CCV) = (98.3 ± 0.6)%, EUDr,m(CCH) = (98.6 ± 0.5)%, EUDr,m(APV) = (97.7 ± 0.9)%, and EUDr,m(LRH) = (97.8 ± 0.9)%. The overall minimum relative EUD observed for 360(∘) arc midventilation treatments was 94.6%. The treatment plan with the shortest period and a minimum relative EUD of less than 97% was found at T = 6.1 s. For a partial 120(∘) arc, an EUDr,m = 92.0% was found. In all cases, a correlation coefficient above 0.5 corresponded to a minimum in EUD.

CONCLUSIONS

With the advent of fast VMAT delivery techniques, nonrobust treatment sequences for human breathing patterns can be generated. These sequences are characterized by a large correlation coefficient between a target motion component and the corresponding collimator dynamic. By iteratively decreasing the maximum allowed dose rate, a low correlation coefficient and consequentially a robust treatment sequence are ensured.

Distortion correction of EPI data using multimodal nonrigid registration with an anisotropic regularization

In this paper, a novel strategy for correcting both geometric and image intensity distortions of echo-planar imaging (EPI) MRI data is presented. To deal with small local distortions caused by rapid changes of the magnetic field, an improved multimodal registration framework using normalized mutual information (NMI) in combination with a multi-scale technique is presented to estimate a dense displacement field. To ensure the robustness of this high dimensional ill-posed inverse problem, a novel anisotropic regularization functional is used. In order to quantify geometric distortions, a new quality measure, called standardized contour distance (SCD), is introduced. It uses the outer structure shape (OSS) information as basis for the evaluation. The new registration method was evaluated with one monomodal phantom data set and two multimodal human brain data sets (BrainSuite trainings data, SPM Subject data). By comparing with recent and efficient techniques of the state of the art, in the monomodal case, the new approach achieves results comparable to the sum of squared differences as data term. In the multimodal cases, our new registration strategy improves the mean of the SCD from 0.96±0.11 to 0.60±0.13 in case of the SPM Subject data and from 0.92±0.07 to 0.78±0.11 in case of the BrainSuite trainings data.

Technical Note: Nanometric organic photovoltaic thin film detectors for dose monitoring in diagnostic x-ray imaging

PURPOSE

To fabricate organic photovoltaic (OPV) cells with nanometric active layers sensitive to ionizing radiation and measure their dosimetric characteristics in clinical x-ray beams in the diagnostic tube potential range of 60-150 kVp.

METHODS

Experiments were designed to optimize the detector's x-ray response and find the best parameter combination by changing the active layer thickness and the area of the electrode. The OPV cell consisted of poly (3-hexylthiophene-2,5-diyl): [6,6]-phenyl C61 butyric acid methyl ester photoactive donor and acceptor semiconducting organic materials sandwiched between an aluminum electrode as an anode and an indium tin oxide electrode as a cathode. The authors measured the radiation-induced electric current at zero bias voltage in all fabricated OPV cells.

RESULTS

The net OPV current as a function of beam potential (kVp) was proportional to kVp(-0.5) when normalized to x-ray tube output, which varies with kVp. Of the tested configurations, the best combination of parameters was 270 nm active layer thicknesses with 0.7 cm(2) electrode area, which provided the highest signal per electrode area. For this cell, the measured current ranged from approximately 0.7 to 2.4 nA/cm(2) for 60-150 kVp, corresponding to about 0.09 nA-0.06 nA/mGy air kerma, respectively. When compared to commercial amorphous silicon thin film photovoltaic cells irradiated under the same conditions, this represents 2.5 times greater sensitivity. An additional 40% signal enhancement was observed when a 1 mm layer of plastic scintillator was attached to the cells' beam-facing side.

CONCLUSIONS

Since both OPVs can be produced as flexible devices and they do not require external bias voltage, they open the possibility for use as thin film in vivo detectors for dose monitoring in diagnostic x-ray imaging.

Accurate low-dose iterative CT reconstruction from few projections by Generalized Anisotropic Total Variation minimization for industrial CT

BACKGROUND

Reducing the amount of time for data acquisition and reconstruction in industrial CT decreases the operation time of the X-ray machine and therefore increases the sales. This can be achieved by reducing both, the dose and the pulse length of the CT system and the number of projections for the reconstruction, respectively.

OBJECTIVE

In this paper, a novel generalized Anisotropic Total Variation regularization for under-sampled, low-dose iterative CT reconstruction is discussed and compared to the standard methods, Total Variation, Adaptive weighted Total Variation and Filtered Backprojection.

METHOD

The novel regularization function uses a priori information about the Gradient Magnitude Distribution of the scanned object for the reconstruction. We provide a general parameterization scheme and evaluate the efficiency of our new algorithm for different noise levels and different number of projection views.

RESULTS

When noise is not present, error-free reconstructions are achievable for AwTV and GATV from 40 projections. In cases where noise is simulated, our strategy achieves a Relative Root Mean Square Error that is up to 11 times lower than Total Variation-based and up to 4 times lower than AwTV-based iterative statistical reconstruction (e.g. for a SNR of 223 and 40 projections).

CONCLUSION

To obtain the same reconstruction quality as achieved by Total Variation, the projection number and the pulse length, and the acquisition time and the dose respectively can be reduced by a factor of approximately 3.5, when AwTV is used and a factor of approximately 6.7, when our proposed algorithm is used.

Low-cost flexible thin-film detector for medical dosimetry applications

The purpose of this study is to characterize dosimetric properties of thin film photovoltaic sensors as a platform for development of prototype dose verification equipment in radiotherapy. Towards this goal, flexible thin‐film sensors of dose with embedded data acquisition electronics and wireless data transmission are prototyped and tested in kV and MV photon beams. Fundamental dosimetric properties are determined in view of a specific application to dose verification in multiple planes or curved surfaces inside a phantom. Uniqueness of the new thin‐film sensors consists in their mechanical properties, low‐power operation, and low‐cost. They are thinner and more flexible than dosimetric films. In principle, each thin‐film sensor can be fabricated in any size (mm² – cm² areas) and shape. Individual sensors can be put together in an array of sensors spreading over large areas and yet being light. Photovoltaic mode of charge collection (of electrons and holes) does not require external electric field applied to the sensor, and this implies simplicity of data acquisition electronics and low power operation. The prototype device use for testing consists of several thin film dose sensors, each of about 1.5 cm×5 cm area, connected to simple readout electronics. Sensitivity of the sensors is determined per unit area and compared to EPID sensitivity, as well as other standard photodiodes. Each sensor independently measures dose and is based on commercially available flexible thin‐film aSi photodiodes. Readout electronics consists of an ultra low‐power microcontroller, radio frequency transmitter, and a low‐noise amplification circuit implemented on a flexible printed circuit board. Detector output is digitized and transmitted wirelessly to an external host computer where it is integrated and processed. A megavoltage medical linear accelerator (Varian Tx) equipped with kilovoltage online imaging system and a Cobalt source are use to irradiate different thin‐film detector sensors in a Solid Water phantom under various irradiation conditions. Different factors are considered in characterization of the device attributes: energies (80 kVp, 130 kVp, 6 MV, 15 MV), dose rates (different ms × mA, 100–600 MU/min), total doses (0.1 cGy‐500 cGy), depths (0.5 cm–20 cm), irradiation angles with respect to the detector surface (0°‐180°), and IMRT tests (closed MLC, sweeping gap). The detector response to MV radiation is both linear with total dose (~1‐400 cGy) and independent of dose rate (100‐600 Mu/min). The sensitivity per unit area of thin‐film sensors is lower than for aSi flat‐panel detectors, but sufficient to acquire stable and accurate signals during irradiations. The proposed thin‐film photodiode system has properties which make it promising for clinical dosimetry. Due to the mechanical flexibility of each sensor and readout electronics, low‐cost, and wireless data acquisition, it could be considered for quality assurance (e.g., IMRT, mechanical linac QA), as well as real‐time dose monitoring in challenging setup configurations, including large area and 3D detection (multiple planes or curved surfaces).

PACS number: 87.56.Fc

Dependence of Monte Carlo microdosimetric computations on the simulation geometry of gold nanoparticles

Recently, interactions of x-rays with gold nanoparticles (GNPs) and the resulting dose enhancement have been studied using several Monte Carlo (MC) codes (Jones et al 2010 Med. Phys. 37 3809-16, Lechtman et al 2011 Phys. Med. Biol. 56 4631-47, McMahon et al 2011 Sci. Rep. 1 1-9, Leung et al 2011 Med. Phys. 38 624-31). These MC simulations were carried out in simplified geometries and provided encouraging preliminary data in support of GNP radiotherapy. As these studies showed, radiation transport computations of clinical beams to obtain dose enhancement from nanoparticles has several challenges, mostly arising from the requirement of high spatial resolution and from the approximations used at the interface between the macroscopic clinical beam transport and the nanoscopic electron transport originating in the nanoparticle or its vicinity. We investigate the impact of MC simulation geometry on the energy deposition due to the presence of GNPs, including the effects of particle clustering and morphology. Dose enhancement due to a single and multiple GNPs using various simulation geometries is computed using GEANT4 MC radiation transport code. Various approximations in the geometry and in the phase space transition from macro- to micro-beams incident on GNPs are analyzed. Simulations using GEANT4 are compared to a deterministic code CEPXS/ONEDANT for microscopic (nm-µm) geometry. Dependence on the following microscopic (µ) geometry parameters is investigated: µ-source-to-GNP distance (µSAD), µ-beam size (µS), and GNP size (µC). Because a micro-beam represents clinical beam properties at the microscopic scale, the effect of using different types of micro-beams is also investigated. In particular, a micro-beam with the phase space of a clinical beam versus a plane-parallel beam with an equivalent photon spectrum is characterized. Furthermore, the spatial anisotropy of energy deposition around a nanoparticle is analyzed. Finally, dependence of dose enhancement on the number of GNPs in a finite cluster of nanoparticles is determined. Simulations were performed for 100 nm GNPs irradiated in water phantom by various monoenergetic (11 keV-1 MeV) and spectral (50 kVp) sources. The dose enhancement ratio (DER) is very sensitive to the specific simulation geometry (µSAD, µS, µC parameters) and µ-source type. For a single GNP the spatial distribution of DER is found to be nearly isotropic with limited magnitude and relatively short range (∼100-200 nm for DER significantly greater than 1). For a cluster of GNPs both the magnitude and range are found much greater (∼1-2 µm). The relation between DER for a cluster of GNPs and a single GNP is strongly nonlinear. Relatively strong dependence of DER on the simulation micro-geometry cautions future studies and the interpretation of existing MC results obtained in different simulations geometries. The nonlinear relation between DER for a single and multiple GNPs suggests that parameters such as the number of adjacent nanoparticles per cell and the distances between the GNPs and the cellular target may be important in assessing the biological effectiveness associated with GNP.

High throughput film dosimetry in homogeneous and heterogeneous media for a small animal irradiator

PURPOSE

We have established a high-throughput Gafchromic film dosimetry protocol for narrow kilovoltage beams in homogeneous and heterogeneous media for small-animal radiotherapy applications. The kV beam characterization is based on extensive Gafchromic film dosimetry data acquired in homogeneous and heterogeneous media. An empirical model is used for parameterization of depth and off-axis dependence of measured data.

METHODS

We have modified previously published methods of film dosimetry to suit the specific tasks of the study. Unlike film protocols used in previous studies, our protocol employs simultaneous multi-channel scanning and analysis of up to nine Gafchromic films per scan. A scanner and background correction were implemented to improve accuracy of the measurements. Measurements were taken in homogeneous and inhomogeneous phantoms at 220 kVp and a field size of 5 × 5 mm(2). The results were compared against Monte Carlo simulations.

RESULTS

Dose differences caused by variations in background signal were effectively removed by the corrections applied. Measurements in homogeneous phantoms were used to empirically characterize beam data in homogeneous and heterogeneous media. Film measurements in inhomogeneous phantoms and their empirical parameterization differed by about 2%-3%. The model differed from MC by about 1% (water, lung) to 7% (bone). Good agreement was found for measured and modelled off-axis ratios.

CONCLUSIONS

EBT2 films are a valuable tool for characterization of narrow kV beams, though care must be taken to eliminate disturbances caused by varying background signals. The usefulness of the empirical beam model in interpretation and parameterization of film data was demonstrated.