Showing papers in "Physics in Medicine and Biology in 2017"

First patients treated with a 1.5 T MRI-Linac: clinical proof of concept of a high-precision, high-field MRI guided radiotherapy treatment.

Abstract: The integration of 1.5 T MRI functionality with a radiotherapy linear accelerator (linac) has been pursued since 1999 by the UMC Utrecht in close collaboration with Elekta and Philips. The idea behind this integrated device is to offer unrivalled, online and real-time, soft-tissue visualization of the tumour and the surroundings for more precise radiation delivery. The proof of concept of this device was given in 2009 by demonstrating simultaneous irradiation and MR imaging on phantoms, since then the device has been further developed and commercialized by Elekta. The aim of this work is to demonstrate the clinical feasibility of online, high-precision, high-field MRI guidance of radiotherapy using the first clinical prototype MRI-Linac. Four patients with lumbar spine bone metastases were treated with a 3 or 5 beam step-and-shoot IMRT plan. The IMRT plan was created while the patient was on the treatment table and based on the online 1.5 T MR images; pre-treatment CT was deformably registered to the online MRI to obtain Hounsfield values. Bone metastases were chosen as the first site as these tumors can be clearly visualized on MRI and the surrounding spine bone can be detected on the integrated portal imager. This way the portal images served as an independent verification of the MRI based guidance to quantify the geometric precision of radiation delivery. Dosimetric accuracy was assessed post-treatment from phantom measurements with an ionization chamber and film. Absolute doses were found to be highly accurate, with deviations ranging from 0.0% to 1.7% in the isocenter. The geometrical, MRI based targeting as confirmed using portal images was better than 0.5 mm, ranging from 0.2 mm to 0.4 mm. In conclusion, high precision, high-field, 1.5 T MRI guided radiotherapy is clinically feasible.

A deep learning framework for supporting the classification of breast lesions in ultrasound images.

Abstract: In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

Magnetic particle imaging: from proof of principle to preclinical applications

Abstract: Tomographic imaging has become a mandatory tool for the diagnosis of a majority of diseases in clinical routine. Since each method has its pros and cons, a variety of them is regularly used in clinics to satisfy all application needs. Magnetic particle imaging (MPI) is a relatively new tomographic imaging technique that images magnetic nanoparticles with a high spatiotemporal resolution in a quantitative way, and in turn is highly suited for vascular and targeted imaging. MPI was introduced in 2005 and now enters the preclinical research phase, where medical researchers get access to this new technology and exploit its potential under physiological conditions. Within this paper, we review the development of MPI since its introduction in 2005. Besides an in-depth description of the basic principles, we provide detailed discussions on imaging sequences, reconstruction algorithms, scanner instrumentation and potential medical applications.

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

Abstract: Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the 'knowledge' learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p = 0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study.

Abstract: Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT + BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D 0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

RBE and related modeling in carbon-ion therapy

Abstract: Carbon ion therapy is a promising evolving modality in radiotherapy to treat tumors that are radioresistant against photon treatments. As carbon ions are more effective in normal and tumor tissue, the relative biological effectiveness (RBE) has to be calculated by bio-mathematical models and has to be considered in the dose prescription. This review (i) introduces the concept of the RBE and its most important determinants, (ii) describes the physical and biological causes of the increased RBE for carbon ions, (iii) summarizes available RBE measurements in vitro and in vivo, and (iv) describes the concepts of the clinically applied RBE models (mixed beam model, local effect model, and microdosimetric-kinetic model), and (v) the way they are introduced into clinical application as well as (vi) their status of experimental and clinical validation, and finally (vii) summarizes the current status of the use of the RBE concept in carbon ion therapy and points out clinically relevant conclusions as well as open questions. The RBE concept has proven to be a valuable concept for dose prescription in carbon ion radiotherapy, however, different centers use different RBE models and therefore care has to be taken when transferring results from one center to another. Experimental studies significantly improve the understanding of the dependencies and limitations of RBE models in clinical application. For the future, further studies investigating quantitatively the differential effects between normal tissues and tumors are needed accompanied by clinical studies on effectiveness and toxicity.

Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks.

Abstract: Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to 'see' the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our multimodal CNNs but have not been carefully studied previously. (1) Given limited training data, how can these be augmented in sufficient numbers and variety for fine-tuning deep CNN networks for PCa diagnosis? (2) How can multimodal MP-MRI information be effectively combined in CNNs? (3) What is the impact of different CNN architectures on the accuracy of PCa diagnosis? Experimental results on extensive clinical data from 364 patients with a total of 463 PCa lesions and 450 identified noncancerous image patches demonstrate that our system can achieve a sensitivity of 89.85% and a specificity of 95.83% for distinguishing cancer from noncancerous tissues and a sensitivity of 100% and a specificity of 76.92% for distinguishing indolent PCa from CS PCa. This result is significantly superior to the state-of-the-art method relying on handcrafted features.

Robust sound speed estimation for ultrasound-based hepatic steatosis assessment.

Abstract: Hepatic steatosis is a common condition, the prevalence of which is increasing along with non-alcoholic fatty liver disease (NAFLD). Currently, the most accurate noninvasive imaging method for diagnosing and quantifying hepatic steatosis is MRI, which estimates the proton-density fat fraction (PDFF) as a measure of fractional fat content. However, MRI suffers several limitations including cost, contra-indications and poor availability. Although conventional ultrasound is widely used by radiologists for hepatic steatosis assessment, it remains qualitative and operator dependent. Interestingly, the speed of sound within soft tissues is known to vary slightly from muscle (1.575 mm · µs-1) to fat (1.450 mm · µs-1). Building upon this fact, steatosis could affect liver sound speed when the fat content increases. The main objectives of this study are to propose a robust method for sound speed estimation (SSE) locally in the liver and to assess its accuracy for steatosis detection and staging. This technique was first validated on two phantoms and SSE was assessed with a precision of 0.006 and 0.003 mm · µs-1 respectively for the two phantoms. Then a preliminary clinical trial (N = 17 patients) was performed. SSE results was found to be highly correlated with MRI proton density fat fraction (R 2 = 0.69) and biopsy (AUROC = 0.952) results. This new method based on the assessment of spatio-temporal properties of the local speckle noise for SSE provides an efficient way to diagnose and stage hepatic steatosis.

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method

Abstract: Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12–13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

A 20-channel magnetoencephalography system based on optically pumped magnetometers

Abstract: We describe a multichannel magnetoencephalography (MEG) system that uses optically pumped magnetometers (OPMs) to sense the magnetic fields of the human brain. The system consists of an array of 20 OPM channels conforming to the human subject's head, a person-sized magnetic shield containing the array and the human subject, a laser system to drive the OPM array, and various control and data acquisition systems. We conducted two MEG experiments: auditory evoked magnetic field and somatosensory evoked magnetic field, on three healthy male subjects, using both our OPM array and a 306-channel Elekta-Neuromag superconducting quantum interference device (SQUID) MEG system. The described OPM array measures the tangential components of the magnetic field as opposed to the radial component measured by most SQUID-based MEG systems. Herein, we compare the results of the OPM- and SQUID-based MEG systems on the auditory and somatosensory data recorded in the same individuals on both systems.

Towards fast online intrafraction replanning for free-breathing stereotactic body radiation therapy with the MR-linac.

Abstract: The hybrid MRI-radiotherapy machines, like the MR-linac (Elekta AB, Stockholm, Sweden) installed at the UMC Utrecht (Utrecht, The Netherlands), will be able to provide real-time patient imaging during treatment. In order to take advantage of the system's capabilities and enable online adaptive treatments, a new generation of software should be developed, ranging from motion estimation to treatment plan adaptation. In this work we present a proof of principle adaptive pipeline designed for high precision stereotactic body radiation therapy (SBRT) suitable for sites affected by respiratory motion, like renal cell carcinoma (RCC). We utilized our research MRL treatment planning system (MRLTP) to simulate a single fraction 25 Gy free-breathing SBRT treatment for RCC by performing inter-beam replanning for two patients and one volunteer. The simulated pipeline included a combination of (pre-beam) 4D-MRI and (online) 2D cine-MR acquisitions. The 4DMRI was used to generate the mid-position reference volume, while the cine-MRI, via an in-house motion model, provided three-dimensional (3D) deformable vector fields (DVFs) describing the anatomical changes during treatment. During the treatment fraction, at an inter-beam interval, the mid-position volume of the patient was updated and the delivered dose was accurately reconstructed on the underlying motion calculated by the model. Fast online replanning, targeting the latest anatomy and incorporating the previously delivered dose was then simulated with MRLTP. The adaptive treatment was compared to a conventional mid-position SBRT plan with a 3 mm planning target volume margin reconstructed on the same motion trace. We demonstrate that our system produced tighter dose distributions and thus spared the healthy tissue, while delivering more dose to the target. The pipeline was able to account for baseline variations/drifts that occurred during treatment ensuring target coverage at the end of the treatment fraction.

Combining magnetic particle imaging and magnetic fluid hyperthermia in a theranostic platform

Abstract: Magnetic particle imaging (MPI) is a rapidly developing molecular and cellular imaging modality. Magnetic fluid hyperthermia (MFH) is a promising therapeutic approach where magnetic nanoparticles are used as a conduit for targeted energy deposition, such as in hyperthermia induction and drug delivery. The physics germane to and exploited by MPI and MFH are similar, and the same particles can be used effectively for both. Consequently, the method of signal localization through the use of gradient fields in MPI can also be used to spatially localize MFH, allowing for spatially selective heating deep in the body and generally providing greater control and flexibility in MFH. Furthermore, MPI and MFH may be integrated together in a single device for simultaneous MPI-MFH and seamless switching between imaging and therapeutic modes. Here we show simulation and experimental work quantifying the extent of spatial localization of MFH using MPI systems: we report the first combined MPI-MFH system and demonstrate on-demand selective heating of nanoparticle samples separated by only 3 mm (up to 0.4 °C s-1 heating rates and 150 W g-1 SAR deposition). We also show experimental data for MPI performed at a typical MFH frequency and show preliminary simultaneous MPI-MFH experimental data.

Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction

Abstract: A significant and increasing number of patients receiving radiation therapy present with metal objects close to, or even within, the treatment area, resulting in artifacts in computed tomography (CT) imaging, which is the most commonly used imaging method for treatment planning in radiation therapy. In the presence of metal implants, such as dental fillings in treatment of head-and-neck tumors, spinal stabilization implants in spinal or paraspinal treatment or hip replacements in prostate cancer treatments, the extreme photon absorption by the metal object leads to prominent image artifacts. Although current CT scanners include a series of correction steps for beam hardening, scattered radiation and noisy measurements, when metal implants exist within or close to the treatment area, these corrections do not suffice. CT metal artifacts affect negatively the treatment planning of radiation therapy either by causing difficulties to delineate the target volume or by reducing the dose calculation accuracy. Various metal artifact reduction (MAR) methods have been explored in terms of improvement of organ delineation and dose calculation in radiation therapy treatment planning, depending on the type of radiation treatment and location of the metal implant and treatment site. Including a brief description of the available CT MAR methods that have been applied in radiation therapy, this article attempts to provide a comprehensive review on the dosimetric effect of the presence of CT metal artifacts in treatment planning, as reported in the literature, and the potential improvement suggested by different MAR approaches. The impact of artifacts on the treatment planning and delivery accuracy is discussed in the context of different modalities, such as photon external beam, brachytherapy and particle therapy, as well as by type and location of metal implants.

Dosimetric evaluation of a commercial proton spot scanning Monte-Carlo dose algorithm: comparisons against measurements and simulations.

Abstract: RaySearch Americas Inc. (NY) has introduced a commercial Monte Carlo dose algorithm (RS-MC) for routine clinical use in proton spot scanning. In this report, we provide a validation of this algorithm against phantom measurements and simulations in the GATE software package. We also compared the performance of the RayStation analytical algorithm (RS-PBA) against the RS-MC algorithm. A beam model (G-MC) for a spot scanning gantry at our proton center was implemented in the GATE software package. The model was validated against measurements in a water phantom and was used for benchmarking the RS-MC. Validation of the RS-MC was performed in a water phantom by measuring depth doses and profiles for three spread-out Bragg peak (SOBP) beams with normal incidence, an SOBP with oblique incidence, and an SOBP with a range shifter and large air gap. The RS-MC was also validated against measurements and simulations in heterogeneous phantoms created by placing lung or bone slabs in a water phantom. Lateral dose profiles near the distal end of the beam were measured with a microDiamond detector and compared to the G-MC simulations, RS-MC and RS-PBA. Finally, the RS-MC and RS-PBA were validated against measured dose distributions in an Alderson-Rando (AR) phantom. Measurements were made using Gafchromic film in the AR phantom and compared to doses using the RS-PBA and RS-MC algorithms. For SOBP depth doses in a water phantom, all three algorithms matched the measurements to within ±3% at all points and a range within 1 mm. The RS-PBA algorithm showed up to a 10% difference in dose at the entrance for the beam with a range shifter and >30 cm air gap, while the RS-MC and G-MC were always within 3% of the measurement. For an oblique beam incident at 45°, the RS-PBA algorithm showed up to 6% local dose differences and broadening of distal fall-off by 5 mm. Both the RS-MC and G-MC accurately predicted the depth dose to within ±3% and distal fall-off to within 2 mm. In an anthropomorphic phantom, the gamma index (dose tolerance = 3%, distance-to-agreement = 3 mm) was greater than 90% for six out of seven planes using the RS-MC, and three out seven for the RS-PBA. The RS-MC algorithm demonstrated improved dosimetric accuracy over the RS-PBA in the presence of homogenous, heterogeneous and anthropomorphic phantoms. The computation performance of the RS-MC was similar to the RS-PBA algorithm. For complex disease sites like breast, head and neck, and lung cancer, the RS-MC algorithm will provide significantly more accurate treatment planning.

BGO as a hybrid scintillator / Cherenkov radiator for cost-effective time-of-flight PET

Abstract: Due to detector developments in the last decade, the time-of-flight (TOF) method is now commonly used to improve the quality of positron emission tomography (PET) images. Clinical TOF-PET systems based on L(Y)SO:Ce crystals and silicon photomultipliers (SiPMs) with coincidence resolving times (CRT) between 325 ps and 400 ps FWHM have recently been developed. Before the introduction of L(Y)SO:Ce, BGO was used in many PET systems. In addition to a lower price, BGO offers a superior attenuation coefficient and a higher photoelectric fraction than L(Y)SO:Ce. However, BGO is generally considered an inferior TOF-PET scintillator. In recent years, TOF-PET detectors based on the Cherenkov effect have been proposed. However, the low Cherenkov photon yield in the order of ∼10 photons per event complicates energy discrimination-a severe disadvantage in clinical PET. The optical characteristics of BGO, in particular its high transparency down to 310 nm and its high refractive index of ∼2.15, are expected to make it a good Cherenkov radiator. Here, we study the feasibility of combining event timing based on Cherenkov emission with energy discrimination based on scintillation in BGO, as a potential approach towards a cost-effective TOF-PET detector. Rise time measurements were performed using a time-correlated single photon counting (TCSPC) setup implemented on a digital photon counter (DPC) array, revealing a prompt luminescent component likely to be due to Cherenkov emission. Coincidence timing measurements were performed using BGO crystals with a cross-section of 3 mm × 3 mm and five different lengths between 3 mm and 20 mm, coupled to DPC arrays. Non-Gaussian coincidence spectra with a FWHM of 200 ps were obtained with the 27 mm3 BGO cubes, while FWHM values as good as 330 ps were achieved with the 20 mm long crystals. The FWHM value was found to improve with decreasing temperature, while the FWTM value showed the opposite trend.

Dosimetric and workflow evaluation of first commercial synthetic CT software for clinical use in pelvis.

Abstract: To evaluate a commercial synthetic CT (syn-CT) software for use in prostate radiotherapy. Twenty-five prostate patients underwent CT and MR simulation scans in treatment position on a 3T MR scanner. A commercially available MR protocol was used that included a T2w turbo spin-echo sequence for soft-tissue contrast and a dual echo 3D mDIXON fast field echo (FFE) sequence for generating syn-CT. A dual-echo 3D FFE B 0 map was used for patient-induced susceptibility distortion analysis and a new 3D balanced-FFE sequence was evaluated for identification of implanted gold fiducial markers and subsequent image-guidance during radiotherapy delivery. Tissues were classified as air, adipose, water, trabecular/spongy bone and compact/cortical bone and assigned bulk HU values. The accuracy of syn-CT for treatment planning was analyzed by transferring the structures and plan from planning CT to syn-CT and recalculating the dose. Accuracy of localization at the treatment machine was evaluated by comparing registration of kV radiographs to either digitally reconstructed radiographs (DRRs) generated from syn-CT or traditional DRRs generated from the planning CT. Similarly, accuracy of setup using CBCT and syn-CT was compared to that using the planning CT. Finally, a MR-only simulation workflow was established and end-to-end testing was completed on five patients undergoing MR-only simulation. Dosimetric comparison between the original CT and syn-CT plans was within 0.5% on average for all structures. The de-novo optimized plans on the syn-CT met institutional clinical objectives for target and normal structures. Patient-induced susceptibility distortion based on B 0 maps was within 1 mm and 0.5 mm in the body and prostate respectively. DRR and CBCT localization based on MR-localized fiducials showed a standard deviation of <1 mm. End-to-end testing and MR simulation workflow was successfully validated. MRI derived synthetic CT can be successfully used for a MR-only planning and treatment for prostate radiotherapy.

Quantitative image reconstruction for total-body PET imaging using the 2-meter long EXPLORER scanner

Abstract: The EXPLORER project aims to build a 2 meter long total-body PET scanner, which will provide extremely high sensitivity for imaging the entire human body. It will possess a range of capabilities currently unavailable to state-of-the-art clinical PET scanners with a limited axial field-of-view. The huge number of lines-of-response (LORs) of the EXPLORER poses a challenge to the data handling and image reconstruction. The objective of this study is to develop a quantitative image reconstruction method for the EXPLORER and compare its performance with current whole-body scanners. Fully 3D image reconstruction was performed using time-of-flight list-mode data with parallel computation. To recover the resolution loss caused by the parallax error between crystal pairs at a large axial ring difference or transaxial radial offset, we applied an image domain resolution model estimated from point source data. To evaluate the image quality, we conducted computer simulations using the SimSET Monte–Carlo toolkit and XCAT 2.0 anthropomorphic phantom to mimic a 20 min whole-body PET scan with an injection of 25 MBq 18F-FDG. We compare the performance of the EXPLORER with a current clinical scanner that has an axial FOV of 22 cm. The comparison results demonstrated superior image quality from the EXPLORER with a 6.9-fold reduction in noise standard deviation comparing with multi-bed imaging using the clinical scanner.

First in vivo magnetic particle imaging of lung perfusion in rats.

Abstract: Pulmonary embolism (PE), along with the closely related condition of deep vein thrombosis, affect an estimated 600 000 patients in the US per year. Untreated, PE carries a mortality rate of 30%. Because many patients experience mild or non-specific symptoms, imaging studies are necessary for definitive diagnosis of PE. Iodinated CT pulmonary angiography is recommended for most patients, while nuclear medicine-based ventilation/perfusion (V/Q) scans are reserved for patients in whom the use of iodine is contraindicated. Magnetic particle imaging (MPI) is an emerging tracer imaging modality with high image contrast (no tissue background signal) and sensitivity to superparamagnetic iron oxide (SPIO) tracer. Importantly, unlike CT or nuclear medicine, MPI uses no ionizing radiation. Further, MPI is not derived from magnetic resonance imaging (MRI); MPI directly images SPIO tracers via their strong electronic magnetization, enabling deep imaging of anatomy including within the lungs, which is very challenging with MRI. Here, the first high-contrast in vivo MPI lung perfusion images of rats are shown using a novel lung perfusion agent, MAA-SPIOs.

Treatment planning of intensity modulated composite particle therapy with dose and linear energy transfer optimization.

Abstract: The biological effect of charged-particle beams depends on both dose and particle spectrum. As one of the physical quantities describing the particle spectrum of charged-particle beams, we considered the linear energy transfer (LET) throughout this study. We investigated a new therapeutic technique using two or more ion species in one treatment session, which we call an intensity modulated composite particle therapy (IMPACT), for optimizing the physical dose and dose-averaged LET distributions in a patient as its proof of principle. Protons and helium, carbon, and oxygen ions were considered as ion species for IMPACT. For three cubic targets of 4 × 4 × 4, 8 × 8 × 8, and 12 × 12 × 12 cm3, defined at the center of the water phantom of 20 × 20 × 20 cm3, we made IMPACT plans of two composite fields with opposing and orthogonal geometries. The prescribed dose to the target was fixed at 1 Gy, while the prescribed LET to the target was varied from 1 keV µm-1 to 120 keV µm-1 to investigate the range of LET valid for prescription. The minimum and maximum prescribed LETs, (L T_min, L T_max), by the opposing-field geometry, were (3 keV µm-1, 115 keV µm-1), (2 keV µm-1, 84 keV µm-1),and (2 keV µm-1, 66 keV µm-1), while those by the orthogonal-field geometry were (8 keV µm-1, 98 keV µm-1), (7 keV µm-1, 72 keV µm-1), and (8 keV µm-1, 57 keV µm-1) for the three targets, respectively. To show the proof of principle of IMPACT in a clinical situation, we made IMPACT plans for a prostate case. In accordance with the prescriptions, the LETs in prostate, planning target volume (PTV), and rectum could be adjusted at 80 keV µm-1, at 50 keV µm-1, and below 30 keV µm-1, respectively, while keeping the dose to the PTV at 2 Gy uniformly. IMPACT enables the optimization of the dose and the LET distributions in a patient, which will maximize the potential of charged-particle therapy by expanding the therapeutic window. Further studies and developments will enable this therapeutic technique to be used in clinical practice.

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets.

Abstract: Photon-counting computed tomography (PCCT) uses a photon counting detector to count individual photons and allocate them to specific energy bins by comparing photon energy to preset thresholds. This enables simultaneous multi-energy CT with a single source and detector. Phantom studies were performed to assess the spectral performance of a research PCCT scanner by assessing the accuracy of derived images sets. Specifically, we assessed the accuracy of iodine quantification in iodine map images and of CT number accuracy in virtual monoenergetic images (VMI). Vials containing iodine with five known concentrations were scanned on the PCCT scanner after being placed in phantoms representing the attenuation of different size patients. For comparison, the same vials and phantoms were also scanned on 2nd and 3rd generation dual-source, dual-energy scanners. After material decomposition, iodine maps were generated, from which iodine concentration was measured for each vial and phantom size and compared with the known concentration. Additionally, VMIs were generated and CT number accuracy was compared to the reference standard, which was calculated based on known iodine concentration and attenuation coefficients at each keV obtained from the U.S. National Institute of Standards and Technology (NIST). Results showed accurate iodine quantification (root mean square error of 0.5 mgI/cc) and accurate CT number of VMIs (percentage error of 8.9%) using the PCCT scanner. The overall performance of the PCCT scanner, in terms of iodine quantification and VMI CT number accuracy, was comparable to that of EID-based dual-source, dual-energy scanners.

Monte Carlo simulations of skin exposure to electromagnetic field from 10 GHz to 1 THz.

Abstract: In this study, we present an assessment of human-body exposure to an electromagnetic field at frequencies ranging from 10 GHz to 1 THz. The energy absorption and temperature elevation were assessed by solving boundary value problems of the one-dimensional Maxwell equations and a bioheat equation for a multilayer plane model. Dielectric properties were measured [Formula: see text] at frequencies of up to 1 THz at body temperature. A Monte Carlo simulation was conducted to assess variations of the transmittance into a skin surface and temperature elevation inside a body by considering the variation of the tissue thickness due to individual differences among human bodies. Furthermore, the impact of the dielectric properties of adipose tissue on temperature elevation, for which large discrepancies between our present measurement results and those in past works were observed, was also examined. We found that the dielectric properties of adipose tissue do not impact on temperature elevation at frequencies over 30 GHz. The potential risk of skin burn was discussed on the basis of the temperature elevation in millimeter-wave and terahertz-wave exposure. Furthermore, the consistency of the basic restrictions in the international guidelines set by ICNIRP was discussed.

Ultrasonic computed tomography based on full-waveform inversion for bone quantitative imaging

Abstract: We introduce an ultrasonic quantitative imaging method for long bones based on full-waveform inversion. The cost function is defined as the difference in the L 2-norm sense between observed data and synthetic results at a given iteration of the iterative inversion process. For simplicity, and in order to reduce the computational cost, we use a two-dimensional acoustic approximation. The inverse problem is solved iteratively based on a quasi-Newton technique called the Limited-memory Broyden-Fletcher-Goldfarb-Shanno method. We show how the technique can be made to work fine for benchmark models consisting of a single cylinder, and then five cylinders, the latter case including significant multiple diffraction effects. We then show pictures obtained for a tibia-fibula bone pair model. Convergence is fast, typically in 15 to 30 iterations in practice in each frequency band used. We discuss the so-called 'cycle skipping' effect that can occur in such full waveform inversion techniques and make them remain trapped in a local minimum of the cost function. We illustrate strategies that can be used in practice to avoid this. Future work should include viscoelastic materials rather than acoustic, and real data instead of synthetic data.

Voxel-based dose prediction with multi-patient atlas selection for automated radiotherapy treatment planning

Abstract: Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to be used in plan optimization. In this work we outline a multi-patient atlas-based dose prediction approach that learns to predict the dose-per-voxel for a novel patient directly from the computed tomography planning scan without the requirement of specifying any objectives. Our method learns to automatically select the most effective atlases for a novel patient, and then map the dose from those atlases onto the novel patient. We extend our previous work to include a conditional random field for the optimization of a joint distribution prior that matches the complementary goals of an accurately spatially distributed dose distribution while still adhering to the desired dose volume histograms. The resulting distribution can then be used for inverse-planning with a new spatial dose objective, or to create typical dose volume objectives for the canonical optimization pipeline. We investigated six treatment sites (633 patients for training and 113 patients for testing) and evaluated the mean absolute difference in all DVHs for the clinical and predicted dose distribution. The results on average are favorable in comparison to our previous approach (1.91 versus 2.57). Comparing our method with and without atlas-selection further validates that atlas-selection improved dose prediction on average in whole breast (0.64 versus 1.59), prostate (2.13 versus 4.07), and rectum (1.46 versus 3.29) while it is less important in breast cavity (0.79 versus 0.92) and lung (1.33 versus 1.27) for which there is high conformity and minimal dose shaping. In CNS brain, atlas-selection has the potential to be impactful (3.65 versus 5.09), but selecting the ideal atlas is the most challenging.

First in vivo traumatic brain injury imaging via magnetic particle imaging.

Abstract: Emergency room visits due to traumatic brain injury (TBI) is common, but classifying the severity of the injury remains an open challenge. Some subjective methods such as the Glasgow Coma Scale attempt to classify traumatic brain injuries, as well as some imaging based modalities such as computed tomography and magnetic resonance imaging. However, to date it is still difficult to detect and monitor mild to moderate injuries. In this report, we demonstrate that the magnetic particle imaging (MPI) modality can be applied to imaging TBI events with excellent contrast. MPI can monitor injected iron nanoparticles over long time scales without signal loss, allowing researchers and clinicians to monitor the change in blood pools as the wound heals.

Attenuation measuring ultrasound shearwave elastography and in vivo application in post-transplant liver patients

Abstract: Ultrasound and magnetic resonance elastography techniques are used to assess mechanical properties of soft tissues. Tissue stiffness is related to various pathologies such as fibrosis, loss of compliance, and cancer. One way to perform elastography is measuring shear wave velocity of propagating waves in tissue induced by intrinsic motion or an external source of vibration, and relating the shear wave velocity to tissue elasticity. All tissues are inherently viscoelastic and ignoring viscosity biases the velocity-based estimates of elasticity and ignores a potentially important parameter of tissue health. We present attenuation measuring ultrasound shearwave elastography (AMUSE), a technique that independently measures both shear wave velocity and attenuation in tissue and therefore allows characterization of viscoelasticity without using a rheological model. The theoretical basis for AMUSE is first derived and validated in finite element simulations. AMUSE is validated against the traditional methods for assessing shear wave velocity (phase gradient) and attenuation (amplitude decay) in tissue mimicking phantoms and excised tissue. The results agreed within one standard deviation. AMUSE was used to measure shear wave velocity and attenuation in 15 transplanted livers in patients with potential acute rejection, and the results were compared with the biopsy findings in a preliminary study. The comparison showed excellent agreement and suggests that AMUSE can be used to separate transplanted livers with acute rejection from livers with no rejection.

On the in vivo photochemical rate parameters for PDT reactive oxygen species modeling.

Abstract: Photosensitizer photochemical parameters are crucial data in accurate dosimetry for photodynamic therapy (PDT) based on photochemical modeling. Progress has been made in the last few decades in determining the photochemical properties of commonly used photosensitizers (PS), but mostly in solution or in vitro. Recent developments allow for the estimation of some of these photochemical parameters in vivo. This review will cover the currently available in vivo photochemical properties of photosensitizers as well as the techniques for measuring those parameters. Furthermore, photochemical parameters that are independent of environmental factors or are universal for different photosensitizers will be examined. Most photosensitizers discussed in this review are of the type II (singlet oxygen) photooxidation category, although type I photosensitizers that involve other reactive oxygen species (ROS) will be discussed as well. The compilation of these parameters will be essential for ROS modeling of PDT.

Low-dose lung cancer screening with photon-counting CT: a feasibility study.

Abstract: To evaluate the feasibility of using a whole-body photon-counting detector (PCD) CT scanner for low-dose lung cancer screening compared to a conventional energy integrating detector (EID) system. Radiation dose-matched EID and PCD scans of the COPDGene 2 phantom were acquired at different radiation dose levels (CTDIvol: 3.0, 1.5, and 0.75 mGy) and different tube voltages (120, 100, and 80 kVp). EID and PCD images were compared for quantitative Hounsfield unit (HU) accuracy, noise levels, and contrast-to-noise ratios (CNR) for detection of ground-glass nodules (GGN) and emphysema. The PCD HU accuracy was better than EID for water at all scan parameters. PCD HU stability for lung, GGN and emphysema regions were superior to EID and PCD attenuation values were more reproducible than EID for all scan parameters (all P < 0.01), while HUs for lung, GGN and emphysema ROIs changed significantly for EID with decreasing dose (all P < 0.001). PCD showed lower noise levels at the lowest dose setting at 120, 100 and 80 kVp (15.2 ± 0.3 HU versus 15.8 ± 0.2 HU, P = 0.03; 16.1 ± 0.3 HU versus 18.0 ± 0.4 HU, P = 0.003; and 16.1 ± 0.3 HU versus 17.9 ± 0.3 HU, P = 0.001, respectively), resulting in superior CNR for evaluation of GGNs and emphysema at 100 and 80 kVp. PCD provided better HU stability for lung, ground-glass, and emphysema-equivalent foams at lower radiation dose settings with better reproducibility than EID. Additionally, PCD showed up to 10% less noise, and 11% higher CNR at 0.75 mGy for both 100 and 80 kVp. PCD technology may help reduce radiation exposure in lung cancer screening while maintaining diagnostic quality.

On the averaging area for incident power density for human exposure limits at frequencies over 6 GHz

Abstract: Incident power density is used as the dosimetric quantity to specify the restrictions on human exposure to electromagnetic fields at frequencies above 3 or 10 GHz in order to prevent excessive temperature elevation at the body surface. However, international standards and guidelines have different definitions for the size of the area over which the power density should be averaged. This study reports computational evaluation of the relationship between the size of the area over which incident power density is averaged and the local peak temperature elevation in a multi-layer model simulating a human body. Three wave sources are considered in the frequency range from 3 to 300 GHz: an ideal beam, a half-wave dipole antenna, and an antenna array. 1D analysis shows that averaging area of 20 mm × 20 mm is a good measure to correlate with the local peak temperature elevation when the field distribution is nearly uniform in that area. The averaging area is different from recommendations in the current international standards/guidelines, and not dependent on the frequency. For a non-uniform field distribution, such as a beam with small diameter, the incident power density should be compensated by multiplying a factor that can be derived from the ratio of the effective beam area to the averaging area. The findings in the present study suggest that the relationship obtained using the 1D approximation is applicable for deriving the relationship between the incident power density and the local temperature elevation.

Sensitivity of simulated transcranial ultrasound fields to acoustic medium property maps.

Abstract: High intensity transcranial focused ultrasound is an FDA approved treatment for essential tremor, while low-intensity applications such as neurostimulation and opening the blood brain barrier are under active research. Simulations of transcranial ultrasound propagation are used both for focusing through the skull, and predicting intracranial fields. Maps of the skull acoustic properties are necessary for accurate simulations, and can be derived from medical images using a variety of methods. The skull maps range from segmented, homogeneous models, to fully heterogeneous models derived from medical image intensity. In the present work, the impact of uncertainties in the skull properties is examined using a model of transcranial propagation from a single element focused transducer. The impact of changes in bone layer geometry and the sound speed, density, and acoustic absorption values is quantified through a numerical sensitivity analysis. Sound speed is shown to be the most influential acoustic property, and must be defined with less than 4% error to obtain acceptable accuracy in simulated focus pressure, position, and volume. Changes in the skull thickness of as little as 0.1 mm can cause an error in peak intracranial pressure of greater than 5%, while smoothing with a 1 mm 3 kernel to imitate the effect of obtaining skull maps from low resolution images causes an increase of over 50% in peak pressure. The numerical results are confirmed experimentally through comparison with sonications made through 3D printed and resin cast skull bone phantoms.

Monte Carlo simulated corrections for beam commissioning measurements with circular and MLC shaped fields on the CyberKnife M6 System: a study including diode, microchamber, point scintillator, and synthetic microdiamond detectors

Abstract: Monte Carlo simulation was used to calculate correction factors for output factor (OF), percentage depth-dose (PDD), and off-axis ratio (OAR) measurements with the CyberKnife M6 System. These include the first such data for the InCise MLC. Simulated detectors include diodes, air-filled microchambers, a synthetic microdiamond detector, and point scintillator. Individual perturbation factors were also evaluated. OF corrections show similar trends to previous studies. With a 5 mm fixed collimator the diode correction to convert a measured OF to the corresponding point dose ratio varies between −6.1% and −3.5% for the diode models evaluated, while in a 7.6 mm × 7.7 mm MLC field these are −4.5% to −1.8%. The corresponding microchamber corrections are +9.9% to +10.7% and +3.5% to +4.0%. The microdiamond corrections have a maximum of −1.4% for the 7.5 mm and 10 mm collimators. The scintillator corrections are 15%, reducing to d max were <2% for all detectors except IBA Razor where a maximum 4% correction was observed at 300 mm depth. OAR corrections were smaller inside the field than outside. At the beam edge microchamber OAR corrections were up to 15%, mainly caused by density perturbations, which blurs the measured penumbra. With larger beams and depths, PTW and IBA diode corrections outside the beam were up to 20% while the Edge detector needed smaller corrections although these did vary with orientation. These effects are most noticeable for large field size and depth, where they are dominated by fluence and stopping power perturbations. The microdiamond OAR corrections were <3% outside the beam. This paper provides OF corrections that can be used for commissioning new CyberKnife M6 Systems and retrospectively checking estimated corrections used previously. We recommend the PDD and OAR corrections are used to guide detector selection and inform the evaluation of results rather than to explicitly correct measurements.