Showing papers in "Physics in Medicine and Biology in 2016"
TL;DR: This technical review will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
Abstract: Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
TL;DR: This study evaluated the conventional imaging performance of a research whole-body photon-counting CT system and investigated its feasibility for imaging using clinically realistic levels of x-ray photon flux, finding the impact of high photon flux to be negligible for the PCD subsystem.
Abstract: This study evaluated the conventional imaging performance of a research whole-body photon-counting CT system and investigated its feasibility for imaging using clinically realistic levels of x-ray photon flux. This research system was built on the platform of a 2nd generation dual-source CT system: one source coupled to an energy integrating detector (EID) and the other coupled to a photon-counting detector (PCD). Phantom studies were conducted to measure CT number accuracy and uniformity for water, CT number energy dependency for high-Z materials, spatial resolution, noise, and contrast-to-noise ratio. The results from the EID and PCD subsystems were compared. The impact of high photon flux, such as pulse pile-up, was assessed by studying the noise-to-tube-current relationship using a neonate water phantom and high x-ray photon flux. Finally, clinical feasibility of the PCD subsystem was investigated using anthropomorphic phantoms, a cadaveric head, and a whole-body cadaver, which were scanned at dose levels equivalent to or higher than those used clinically. Phantom measurements demonstrated that the PCD subsystem provided comparable image quality to the EID subsystem, except that the PCD subsystem provided slightly better longitudinal spatial resolution and about 25% improvement in contrast-to-noise ratio for iodine. The impact of high photon flux was found to be negligible for the PCD subsystem: only subtle high-flux effects were noticed for tube currents higher than 300 mA in images of the neonate water phantom. Results of the anthropomorphic phantom and cadaver scans demonstrated comparable image quality between the EID and PCD subsystems. There were no noticeable ring, streaking, or cupping/capping artifacts in the PCD images. In addition, the PCD subsystem provided spectral information. Our experiments demonstrated that the research whole-body photon-counting CT system is capable of providing clinical image quality at clinically realistic levels of x-ray photon flux.
TL;DR: The extent to which MRE has revealed significant alterations to the brain in patients with neurological disorders is assessed and discussed in terms of known pathophysiology, and the trends for future MRE research and applications in neuroscience are predicted.
Abstract: Neurological disorders are one of the most important public health concerns in developed countries. Established brain imaging techniques such as magnetic resonance imaging (MRI) and x-ray computerised tomography (CT) have been essential in the identification and diagnosis of a wide range of disorders, although usually are insufficient in sensitivity for detecting subtle pathological alterations to the brain prior to the onset of clinical symptoms-at a time when prognosis for treatment is more favourable. The mechanical properties of biological tissue provide information related to the strength and integrity of the cellular microstructure. In recent years, mechanical properties of the brain have been visualised and measured non-invasively with magnetic resonance elastography (MRE), a particularly sensitive medical imaging technique that may increase the potential for early diagnosis. This review begins with an introduction to the various methods used for the acquisition and analysis of MRE data. A systematic literature search is then conducted to identify studies that have specifically utilised MRE to investigate the human brain. Through the conversion of MRE-derived measurements to shear stiffness (kPa) and, where possible, the loss tangent (rad), a summary of results for global brain tissue and grey and white matter across studies is provided for healthy participants, as potential baseline values to be used in future clinical investigations. In addition, the extent to which MRE has revealed significant alterations to the brain in patients with neurological disorders is assessed and discussed in terms of known pathophysiology. The review concludes by predicting the trends for future MRE research and applications in neuroscience.
TL;DR: An automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution and adapted into a segmentation model, which is globally optimized in a surface evolution way is proposed.
Abstract: The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of [Formula: see text], yielding a mean Dice similarity coefficient of [Formula: see text], and an average symmetric surface distance of [Formula: see text] mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.
TL;DR: This review focuses on rigid body motion correction of head and brain MRI and its application in diagnosis and research.
Abstract: Subject motion in MRI is a relevant problem in the daily clinical routine as well as in scientific studies. Since the beginning of clinical use of MRI, many research groups have developed methods to suppress or correct motion artefacts. This review focuses on rigid body motion correction of head and brain MRI and its application in diagnosis and research. It explains the sources and types of motion and related artefacts, classifies and describes existing techniques for motion detection, compensation and correction and lists established and experimental approaches. Retrospective motion correction modifies the MR image data during the reconstruction, while prospective motion correction performs an adaptive update of the data acquisition. Differences, benefits and drawbacks of different motion correction methods are discussed.
TL;DR: The timing benefits of prompt photons at the beginning of the scintillation process (Cherenkov etc) are further studied, which leads to the conclusion that theScintillation rise time, SPTR and PTS have to be lowered simultaneously to fully profit from these fast photons in order to improve the CTR significantly.
Abstract: The coincidence time resolution (CTR) of scintillator based detectors commonly used in positron emission tomography is well known to be dependent on the scintillation decay time (τd) and the number of photons detected (n'), i.e. CTR proportional variant √τd/n'. However, it is still an open question to what extent the scintillation rise time (τr) and other fast or prompt photons, e.g. Cherenkov photons, at the beginning of the scintillation process influence the CTR. This paper presents measurements of the scintillation emission rate for different LSO type crystals, i.e. LSO:Ce, LYSO:Ce, LSO:Ce codoped Ca and LGSO:Ce. For the various LSO-type samples measured we find an average value of 70 ps for the scintillation rise time, although some crystals like LSO:Ce codoped Ca seem to have a much faster rise time in the order of 20 ps. Additional measurements for LuAG:Ce and LuAG:Pr show a rise time of 535 ps and 251 ps, respectively. For these crystals, prompt photons (Cherenkov) can be observed at the beginning of the scintillation event. Furthermore a significantly lower rise time value is observed when codoping with calcium. To quantitatively investigate the influence of the rise time to the time resolution we measured the CTR with the same L(Y)SO samples and compared the values to Monte Carlo simulations. Using the measured relative light yields, rise- and decay times of the scintillators we are able to quantitatively understand the measured CTRs in our simulations. Although the rise time is important to fully explain the CTR variation for the different samples tested we determined its influence on the CTR to be in the order of a few percent only. This result is surprising because, if only photonstatistics of the scintillation process is considered, the CTR would be proportional to the square root of the rise time. The unexpected small rise time influence on the CTR can be explained by the convolution of the scintillation rate with the single photon time resolution (SPTR) of the photodetector and the photon travel spread (PTS) in the crystal. The timing benefits of prompt photons at the beginning of the scintillation process (Cherenkov etc) are further studied, which leads to the conclusion that the scintillation rise time, SPTR and PTS have to be lowered simultaneously to fully profit from these fast photons in order to improve the CTR significantly.
TL;DR: It is shown that cortical region of influence can be controlled while balancing other design factors such as intensity at the target and uni-directionality, and the evaluated concentric HD-tDCS approaches can provide categorical improvements in targeting compared to conventional tDCS.
Abstract: Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that applies low amplitude current via electrodes placed on the scalp. Rather than directly eliciting a neuronal response, tDCS is believed to modulate excitability-enhancing or suppressing neuronal activity in regions of the brain depending on the polarity of stimulation. The specificity of tDCS to any therapeutic application derives in part from how electrode configuration determines the brain regions that are stimulated. Conventional tDCS uses two relatively large pads (>25 cm(2)) whereas high-definition tDCS (HD-tDCS) uses arrays of smaller electrodes to enhance brain targeting. The 4 × 1 concentric ring HD-tDCS (one center electrode surrounded by four returns) has been explored in application where focal targeting of cortex is desired. Here, we considered optimization of concentric ring HD-tDCS for targeting: the role of electrodes in the ring and the ring's diameter. Finite element models predicted cortical electric field generated during tDCS. High resolution MRIs were segmented into seven tissue/material masks of varying conductivities. Computer aided design (CAD) model of electrodes, gel, and sponge pads were incorporated into the segmentation. Volume meshes were generated and the Laplace equation ([Formula: see text] · (σ [Formula: see text] V) = 0) was solved for cortical electric field, which was interpreted using physiological assumptions to correlate with stimulation and modulation. Cortical field intensity was predicted to increase with increasing ring diameter at the cost of focality while uni-directionality decreased. Additional surrounding ring electrodes increased uni-directionality while lowering cortical field intensity and increasing focality; though, this effect saturated and more than 4 surround electrode would not be justified. Using a range of concentric HD-tDCS montages, we showed that cortical region of influence can be controlled while balancing other design factors such as intensity at the target and uni-directionality. Furthermore, the evaluated concentric HD-tDCS approaches can provide categorical improvements in targeting compared to conventional tDCS. Hypothesis driven clinical trials, based on specific target engagement, would benefit by this more precise method of stimulation that could avoid potentially confounding brain regions.
TL;DR: This topical review is intended to provide the medical physics community with a wide overview of scintillation physics, related optical concepts, and applications of plastic scintillator dosimetry.
Abstract: While scintillation dosimetry has been around for decades, the need for a dosimeter tailored to the reality of modern radiation therapy-in particular a real-time, water-equivalent, energy-independent dosimeter with high spatial resolution-has generated renewed interest in scintillators over the last 10 years. With the advent of at least one commercial plastic scintillation dosimeter and the ever-growing scientific literature on this subject, this topical review is intended to provide the medical physics community with a wide overview of scintillation physics, related optical concepts, and applications of plastic scintillation dosimetry.
TL;DR: The results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used.
Abstract: Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue (4D PAT). A particular example is the planar Fabry-Perot (FP) photoacoustic scanner, which yields high-resolution 3D images but takes several minutes to sequentially map the incident photoacoustic field on the 2D sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining model-based, variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: first, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP interferometer and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation (TV) regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of photoacoustic scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.
TL;DR: A method to estimate 3D deformation vector fields (DVFs) with high spatial and temporal resolution based on fast 2D imaging and a subject-specific motion model based on respiratory correlated MRI is proposed.
Abstract: Respiratory motion introduces substantial uncertainties in abdominal radiotherapy for which traditionally large margins are used. The MR-Linac will open up the opportunity to acquire high resolution MR images just prior to radiation and during treatment. However, volumetric MRI time series are not able to characterize 3D tumor and organ-at-risk motion with sufficient temporal resolution. In this study we propose a method to estimate 3D deformation vector fields (DVFs) with high spatial and temporal resolution based on fast 2D imaging and a subject-specific motion model based on respiratory correlated MRI. In a pre-beam phase, a retrospectively sorted 4D-MRI is acquired, from which the motion is parameterized using a principal component analysis. This motion model is used in combination with fast 2D cine-MR images, which are acquired during radiation, to generate full field-of-view 3D DVFs with a temporal resolution of 476 ms. The geometrical accuracies of the input data (4D-MRI and 2D multi-slice acquisitions) and the fitting procedure were determined using an MR-compatible motion phantom and found to be 1.0-1.5 mm on average. The framework was tested on seven healthy volunteers for both the pancreas and the kidney. The calculated motion was independently validated using one of the 2D slices, with an average error of 1.45 mm. The calculated 3D DVFs can be used retrospectively for treatment simulations, plan evaluations, or to determine the accumulated dose for both the tumor and organs-at-risk on a subject-specific basis in MR-guided radiotherapy.
TL;DR: Recent advances in instrumentation and spectral analysis have substantially improved the clinical feasibility of RS, so that it is now being investigated with increased success in a wide range of cancer types and locations, as well as for non-oncological conditions.
Abstract: There is an urgent need for improved techniques for disease detection Optical spectroscopy and imaging technologies have potential for non- or minimally-invasive use in a wide range of clinical applications The focus here, in vivo Raman spectroscopy (RS), measures inelastic light scattering based on interaction with the vibrational and rotational modes of common molecular bonds in cells and tissue The Raman 'signature' can be used to assess physiological status and can also be altered by disease This information can supplement existing diagnostic (eg radiological imaging) techniques for disease screening and diagnosis, in interventional guidance for identifying disease margins, and in monitoring treatment responses Using fiberoptic-based light delivery and collection, RS is most easily performed on accessible tissue surfaces, either on the skin, in hollow organs or intra-operatively The strength of RS lies in the high biochemical information content of the spectra, that characteristically show an array of very narrow peaks associated with specific chemical bonds This results in high sensitivity and specificity, for example to distinguish malignant or premalignant from normal tissues A critical issue is that the Raman signal is often very weak, limiting clinical use to point-by-point measurements However, non-linear techniques using pulsed-laser sources have been developed to enable in vivo Raman imaging Changes in Raman spectra with disease are often subtle and spectrally distributed, requiring full spectral scanning, together with the use of tissue classification algorithms that must be trained on large numbers of independent measurements Recent advances in instrumentation and spectral analysis have substantially improved the clinical feasibility of RS, so that it is now being investigated with increased success in a wide range of cancer types and locations, as well as for non-oncological conditions This review covers recent advances and continuing challenges, with emphasis on clinical translation
TL;DR: The PETPVC toolbox described in this paper comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data, and the recovery of both GM and a simulated lesion was improved by combining two PVC techniques together.
Abstract: Positron emission tomography (PET) images are degraded by a phenomenon known as the partial volume effect (PVE) Approaches have been developed to reduce PVEs, typically through the utilisation of structural information provided by other imaging modalities such as MRI or CT These methods, known as partial volume correction (PVC) techniques, reduce PVEs by compensating for the effects of the scanner resolution, thereby improving the quantitative accuracy The PETPVC toolbox described in this paper comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data Eight core PVC techniques are available These core methods can be combined to create a total of 22 different PVC techniques Simulated brain PET data are used to demonstrate the utility of toolbox in idealised conditions, the effects of applying PVC with mismatched point-spread function (PSF) estimates and the potential of novel hybrid PVC methods to improve the quantification of lesions All anatomy-based PVC techniques achieve complete recovery of the PET signal in cortical grey matter (GM) when performed in idealised conditions Applying deconvolution-based approaches results in incomplete recovery due to premature termination of the iterative process PVC techniques are sensitive to PSF mismatch, causing a bias of up to 167% in GM recovery when over-estimating the PSF by 3 mm The recovery of both GM and a simulated lesion was improved by combining two PVC techniques together The PETPVC toolbox has been written in C++, supports Windows, Mac and Linux operating systems, is open-source and publicly available
TL;DR: A method allowing to record timestamps of several photons, at two ends of the scintillator strip, by means of matrix of silicon photomultipliers (SiPM), constitutes an optimal solution for positron emission tomography and open perspectives for construction of a cost-effective TOF-PET scanner with significantly better TOF resolution and larger AFOV with respect to the current ToF- PET modalities.
Abstract: Recent tests of a single module of the Jagiellonian Positron Emission Tomography system (J-PET) consisting of 30 cm long plastic scintillator strips have proven its applicability for the detection of annihilation quanta (0.511 MeV) with a coincidence resolving time (CRT) of 0.266 ns. The achieved resolution is almost by a factor of two better with respect to the current TOF-PET detectors and it can still be improved since, as it is shown in this article, the intrinsic limit of time resolution for the determination of time of the interaction of 0.511 MeV gamma quanta in plastic scintillators is much lower. As the major point of the article, a method allowing to record timestamps of several photons, at two ends of the scintillator strip, by means of matrix of silicon photomultipliers (SiPM) is introduced. As a result of simulations, conducted with the number of SiPM varying from 4 to 42, it is shown that the improvement of timing resolution saturates with the growing number of photomultipliers, and that the [Formula: see text] configuration at two ends allowing to read twenty timestamps, constitutes an optimal solution. The conducted simulations accounted for the emission time distribution, photon transport and absorption inside the scintillator, as well as quantum efficiency and transit time spread of photosensors, and were checked based on the experimental results. Application of the [Formula: see text] matrix of SiPM allows for achieving the coincidence resolving time in positron emission tomography of [Formula: see text]0.170 ns for 15 cm axial field-of-view (AFOV) and [Formula: see text]0.365 ns for 100 cm AFOV. The results open perspectives for construction of a cost-effective TOF-PET scanner with significantly better TOF resolution and larger AFOV with respect to the current TOF-PET modalities.
TL;DR: In this paper, a primal-dual algorithm is proposed for one-step inversion of spectral CT transmission photon counts data to a basis map decomposition. But the algorithm requires the spectral CT data discrepancy terms to be non-convex.
Abstract: We develop a primal-dual algorithm that allows for one-step inversion of spectral CT transmission photon counts data to a basis map decomposition. The algorithm allows for image constraints to be enforced on the basis maps during the inversion. The derivation of the algorithm makes use of a local upper bounding quadratic approximation to generate descent steps for non-convex spectral CT data discrepancy terms, combined with a new convex-concave optimization algorithm. Convergence of the algorithm is demonstrated on simulated spectral CT data. Simulations with noise and anthropomorphic phantoms show examples of how to employ the constrained one-step algorithm for spectral CT data.
TL;DR: This work investigates image similarity metrics in 3D-2D registration offering improved robustness against mismatch, thereby improving performance and reducing or eliminating the need for manual masking in image-guided spine surgery.
Abstract: In image-guided spine surgery, robust three-dimensional to two-dimensional (3D-2D) registration of preoperative computed tomography (CT) and intraoperative radiographs can be challenged by the image content mismatch associated with the presence of surgical instrumentation and implants as well as soft-tissue resection or deformation. This work investigates image similarity metrics in 3D-2D registration offering improved robustness against mismatch, thereby improving performance and reducing or eliminating the need for manual masking. The performance of four gradient-based image similarity metrics (gradient information (GI), gradient correlation (GC), gradient information with linear scaling (GS), and gradient orientation (GO)) with a multi-start optimization strategy was evaluated in an institutional review board-approved retrospective clinical study using 51 preoperative CT images and 115 intraoperative mobile radiographs. Registrations were tested with and without polygonal masks as a function of the number of multistarts employed during optimization. Registration accuracy was evaluated in terms of the projection distance error (PDE) and assessment of failure modes (PDE > 30 mm) that could impede reliable vertebral level localization. With manual polygonal masking and 200 multistarts, the GC and GO metrics exhibited robust performance with 0% gross failures and median PDE 14%; however, GO maintained robustness with a 0% gross failure rate. Overall, the GI, GC, and GS metrics were susceptible to registration errors associated with content mismatch, but GO provided robust registration (median PDE = 5.5 mm, 2.6 mm IQR) without manual masking and with an improved runtime (29.3 s). The GO metric improved the registration accuracy and robustness in the presence of strong image content mismatch. This capability could offer valuable assistance and decision support in spine level localization in a manner consistent with clinical workflow.
TL;DR: 4D ultrafast ultrasound flow imaging, a novel ultrasound-based volumetric imaging technique for the quantitative mapping of blood flow, was presented and the in vivo feasibility of the technique was shown in the carotid arteries of two healthy volunteers.
Abstract: We present herein 4D ultrafast ultrasound flow imaging, a novel ultrasound-based volumetric imaging technique for the quantitative mapping of blood flow. Complete volumetric blood flow distribution imaging was achieved through 2D tilted plane-wave insonification, 2D multi-angle cross-beam beamforming, and 3D vector Doppler velocity components estimation by least-squares fitting. 4D ultrafast ultrasound flow imaging was performed in large volumetric fields of view at very high volume rate (>4000 volumes s-1) using a 1024-channel 4D ultrafast ultrasound scanner and a 2D matrix-array transducer. The precision of the technique was evaluated in vitro by using 3D velocity vector maps to estimate volumetric flow rates in a vessel phantom. Volumetric Flow rate errors of less than 5% were found when volumetric flow rates and peak velocities were respectively less than 360 ml min-1 and 100 cm s-1. The average volumetric flow rate error increased to 18.3% when volumetric flow rates and peak velocities were up to 490 ml min-1 and 1.3 m s-1, respectively. The in vivo feasibility of the technique was shown in the carotid arteries of two healthy volunteers. The 3D blood flow velocity distribution was assessed during one cardiac cycle in a full volume and it was used to quantify volumetric flow rates (375 ± 57 ml min-1 and 275 ± 43 ml min-1). Finally, the formation of 3D vortices at the carotid artery bifurcation was imaged at high volume rates.
TL;DR: It is expected that their continued development will both greatly improve the safety and efficacy of existing ultrasound therapies as well as enable treatments that are not currently possible with existing technology.
Abstract: Focused ultrasound offers a non-invasive way of depositing acoustic energy deep into the body, which can be harnessed for a broad spectrum of therapeutic purposes, including tissue ablation, the targeting of therapeutic agents, and stem cell delivery. Phased array transducers enable electronic control over the beam geometry and direction, and can be tailored to provide optimal energy deposition patterns for a given therapeutic application. Their use in combination with modern medical imaging for therapy guidance allows precise targeting, online monitoring, and post-treatment evaluation of the ultrasound-mediated bioeffects. In the past there have been some technical obstacles hindering the construction of large aperture, high-power, densely-populated phased arrays and, as a result, they have not been fully exploited for therapy delivery to date. However, recent research has made the construction of such arrays feasible, and it is expected that their continued development will both greatly improve the safety and efficacy of existing ultrasound therapies as well as enable treatments that are not currently possible with existing technology. This review will summarize the basic principles, current statures, and future potential of image-guided ultrasound phased arrays for therapy.
TL;DR: Careful attention to methodologies that can identify and advance the most critical dosimetric measurements, either direct or surrogate, are needed to ensure successful incorporation of PDT into niche clinical procedures.
Abstract: Photodynamic therapy (PDT) can be a highly complex treatment, with many parameters influencing treatment efficacy. The extent to which dosimetry is used to monitor and standardize treatment delivery varies widely, ranging from measurement of a single surrogate marker to comprehensive approaches that aim to measure or estimate as many relevant parameters as possible. Today, most clinical PDT treatments are still administered with little more than application of a prescribed drug dose and timed light delivery, and thus the role of patient-specific dosimetry has not reached widespread clinical adoption. This disconnect is at least partly due to the inherent conflict between the need to measure and understand multiple parameters in vivo in order to optimize treatment, and the need for expedience in the clinic and in the regulatory and commercialization process. Thus, a methodical approach to selecting primary dosimetry metrics is required at each stage of translation of a treatment procedure, moving from complex measurements to understand PDT mechanisms in pre-clinical and early phase I trials, towards the identification and application of essential dose-limiting and/or surrogate measurements in phase II/III trials. If successful, identifying the essential and/or reliable surrogate dosimetry measurements should help facilitate increased adoption of clinical PDT. In this paper, examples of essential dosimetry points and surrogate dosimetry tools that may be implemented in phase II/III trials are discussed. For example, the treatment efficacy as limited by light penetration in interstitial PDT may be predicted by the amount of contrast uptake in CT, and so this could be utilized as a surrogate dosimetry measurement to prescribe light doses based upon pre-treatment contrast. Success of clinical ALA-based skin lesion treatment is predicted almost uniquely by the explicit or implicit measurements of photosensitizer and photobleaching, yet the individualization of treatment based upon each patients measured bleaching needs to be attempted. In the case of ALA, lack of PpIX is more likely an indicator that alternative PpIX production methods must be implemented. Parsimonious dosimetry, using surrogate measurements that are clinically acceptable, might strategically help to advance PDT in a medical world that is increasingly cost and time sensitive. Careful attention to methodologies that can identify and advance the most critical dosimetric measurements, either direct or surrogate, are needed to ensure successful incorporation of PDT into niche clinical procedures.
TL;DR: A two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences with improved bone extraction accuracy is proposed and is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning.
Abstract: Magnetic resonance imaging (MRI)-guided attenuation correction (AC) of positron emission tomography (PET) data and/or radiation therapy (RT) treatment planning is challenged by the lack of a direct link between MRI voxel intensities and electron density. Therefore, even if this is not a trivial task, a pseudo-computed tomography (CT) image must be predicted from MRI alone. In this work, we propose a two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences and evaluate its performance against the conventional MRI segmentation technique and a recently proposed multi-atlas approach. The clinical studies consisted of pelvic CT, PET and MRI scans of 12 patients with loco-regionally advanced rectal disease. In the first step, bone segmentation of the target image is optimized through local weighted atlas voting. The obtained bone map is then used to assess the quality of deformed atlases to perform voxel-wise weighted atlas fusion. To evaluate the performance of the method, a leave-one-out cross-validation (LOOCV) scheme was devised to find optimal parameters for the model. Geometric evaluation of the produced pseudo-CT images and quantitative analysis of the accuracy of PET AC were performed. Moreover, a dosimetric evaluation of volumetric modulated arc therapy photon treatment plans calculated using the different pseudo-CT images was carried out and compared to those produced using CT images serving as references. The pseudo-CT images produced using the proposed method exhibit bone identification accuracy of 0.89 based on the Dice similarity metric compared to 0.75 achieved by the other atlas-based method. The superior bone extraction resulted in a mean standard uptake value bias of -1.5 ± 5.0% (mean ± SD) in bony structures compared to -19.9 ± 11.8% and -8.1 ± 8.2% achieved by MRI segmentation-based (water-only) and atlas-guided AC. Dosimetric evaluation using dose volume histograms and the average difference between minimum/maximum absorbed doses revealed a mean error of less than 1% for the both target volumes and organs at risk. Two-dimensional (2D) gamma analysis of the isocenter dose distributions at 1%/1 mm criterion revealed pass rates of 91.40 ± 7.56%, 96.00 ± 4.11% and 97.67 ± 3.6% for MRI segmentation, atlas-guided and the proposed methods, respectively. The proposed method generates accurate pseudo-CT images from conventional Dixon MRI sequences with improved bone extraction accuracy. The approach is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning.
TL;DR: The basic technology for US motion estimation, and its current clinical application to the prostate, is described here, along with recent developments in robust motion-estimation algorithms, and three dimensional (3D) imaging.
Abstract: Imaging has become an essential tool in modern radiotherapy (RT), being used to plan dose delivery prior to treatment and verify target position before and during treatment. Ultrasound (US) imaging is cost-effective in providing excellent contrast at high resolution for depicting soft tissue targets apart from those shielded by the lungs or cranium. As a result, it is increasingly used in RT setup verification for the measurement of inter-fraction motion, the subject of Part I of this review (Fontanarosa et al 2015 Phys. Med. Biol. 60 R77-114). The combination of rapid imaging and zero ionising radiation dose makes US highly suitable for estimating intra-fraction motion. The current paper (Part II of the review) covers this topic. The basic technology for US motion estimation, and its current clinical application to the prostate, is described here, along with recent developments in robust motion-estimation algorithms, and three dimensional (3D) imaging. Together, these are likely to drive an increase in the number of future clinical studies and the range of cancer sites in which US motion management is applied. Also reviewed are selections of existing and proposed novel applications of US imaging to RT. These are driven by exciting developments in structural, functional and molecular US imaging and analytical techniques such as backscatter tissue analysis, elastography, photoacoustography, contrast-specific imaging, dynamic contrast analysis, microvascular and super-resolution imaging, and targeted microbubbles. Such techniques show promise for predicting and measuring the outcome of RT, quantifying normal tissue toxicity, improving tumour definition and defining a biological target volume that describes radiation sensitive regions of the tumour. US offers easy, low cost and efficient integration of these techniques into the RT workflow. US contrast technology also has potential to be used actively to assist RT by manipulating the tumour cell environment and by improving the delivery of radiosensitising agents. Finally, US imaging offers various ways to measure dose in 3D. If technical problems can be overcome, these hold potential for wide-dissemination of cost-effective pre-treatment dose verification and in vivo dose monitoring methods. It is concluded that US imaging could eventually contribute to all aspects of the RT workflow.
TL;DR: validation on clinical WB dynamic data demonstrated the clinical feasibility and superior K i CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging.
Abstract: Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate K i as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting K i images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit K i bias of sPatlak analysis at regions with non-negligible (18)F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source software for tomographic image reconstruction platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published (18)F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced K i target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D versus the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10-20 sub-iterations. Moreover, systematic reduction in K i % bias and improved TBR were observed for gPatlak versus sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior K i CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging.
TL;DR: This work investigates the achievable CTR with LGSO:Ce (0.025 mol%) when coupled to new silicon photomultipliers, as it has similar light output and equivalent stopping power for 511 keV annihilation photons compared to industry standard LSO: ce and LYSO:ce, and the decay time is improved by more than 30% with proper Ce concentration.
Abstract: Coincidence time resolution (CTR), an important parameter for time-of-flight (TOF) PET performance, is determined mainly by properties of the scintillation crystal and photodetector used. Stable production techniques for LGSO:Ce (Lu1.8Gd0.2SiO5:Ce) with decay times varying from ∼ 30-40 ns have been established over the past decade, and the decay time can be accurately controlled with varying cerium concentration (0.025-0.075 mol%). This material is promising for TOF-PET, as it has similar light output and equivalent stopping power for 511 keV annihilation photons compared to industry standard LSO:Ce and LYSO:Ce, and the decay time is improved by more than 30% with proper Ce concentration. This work investigates the achievable CTR with LGSO:Ce (0.025 mol%) when coupled to new silicon photomultipliers. Crystal element dimension is another important parameter for achieving fast timing. 20 mm length crystal elements achieve higher 511 keV photon detection efficiency, but also introduce higher scintillation photon transit time variance. 3 mm length crystals are not practical for PET, but have reduced scintillation transit time spread. The CTR between pairs of 2.9 × 2.9 × 3 mm(3) and 2.9 × 2.9 × 20 mm(3) LGSO:Ce crystals was measured to be 80 ± 4 and 122 ± 4 ps FWHM, respectively. Measurements of light yield and intrinsic decay time are also presented for a thorough investigation into the timing performance with LGSO:Ce (0.025 mol%).
TL;DR: Clinical relevant thresholds that put patients at risk of developing radiation pneumonitis were determined in a cohort of 201 stage I NSCLC patients treated with SBRT and can provide radiation oncologists with an estimate of their reliability and may inform treatment planning and patient counseling.
Abstract: To develop a patient-specific 'big data' clinical decision tool to predict pneumonitis in stage I non-small cell lung cancer (NSCLC) patients after stereotactic body radiation therapy (SBRT). 61 features were recorded for 201 consecutive patients with stage I NSCLC treated with SBRT, in whom 8 (4.0%) developed radiation pneumonitis. Pneumonitis thresholds were found for each feature individually using decision stumps. The performance of three different algorithms (Decision Trees, Random Forests, RUSBoost) was evaluated. Learning curves were developed and the training error analyzed and compared to the testing error in order to evaluate the factors needed to obtain a cross-validated error smaller than 0.1. These included the addition of new features, increasing the complexity of the algorithm and enlarging the sample size and number of events. In the univariate analysis, the most important feature selected was the diffusion capacity of the lung for carbon monoxide (DLCO adj%). On multivariate analysis, the three most important features selected were the dose to 15 cc of the heart, dose to 4 cc of the trachea or bronchus, and race. Higher accuracy could be achieved if the RUSBoost algorithm was used with regularization. To predict radiation pneumonitis within an error smaller than 10%, we estimate that a sample size of 800 patients is required. Clinically relevant thresholds that put patients at risk of developing radiation pneumonitis were determined in a cohort of 201 stage I NSCLC patients treated with SBRT. The consistency of these thresholds can provide radiation oncologists with an estimate of their reliability and may inform treatment planning and patient counseling. The accuracy of the classification is limited by the number of patients in the study and not by the features gathered or the complexity of the algorithm.
TL;DR: Dosimetry of ionising radiation is a well-established and mature branch of physical sciences with many applications in medicine and biology and two fundamental issues remain: the accuracy of the measurement from a scientific perspective and the importance to link the measurement to a clinically relevant question.
Abstract: Dosimetry of ionising radiation is a well-established and mature branch of physical sciences with many applications in medicine and biology. In particular radiotherapy relies on dosimetry for optimisation of cancer treatment and avoidance of severe toxicity for patients. Several novel developments in radiotherapy have introduced new challenges for dosimetry with small and dynamically changing radiation fields being central to many of these applications such as stereotactic ablative body radiotherapy and intensity modulated radiation therapy. There is also an increasing awareness of low doses given to structures not in the target region and the associated risk of secondary cancer induction. Here accurate dosimetry is important not only for treatment optimisation but also for the generation of data that can inform radiation protection approaches in the future. The article introduces some of the challenges and highlights the interdependence of dosimetric calculations and measurements. Dosimetric concepts are explored in the context of six application fields: reference dosimetry, small fields, low dose out of field, in vivo dosimetry, brachytherapy and auditing of radiotherapy practice. Recent developments of dosimeters that can be used for these purposes are discussed using spatial resolution and number of dimensions for measurement as sorting criteria. While dosimetry is ever evolving to address the needs of advancing applications of radiation in medicine two fundamental issues remain: the accuracy of the measurement from a scientific perspective and the importance to link the measurement to a clinically relevant question. This review aims to provide an update on both of these.
TL;DR: The most widely used national and international dosimetry protocols for mammography are based on different simple geometrical models of the breast, and harmonisation of these protocols using more complex breast models is desirable.
Abstract: The estimation of the mean glandular dose to the breast (MGD) for x-ray based imaging modalities forms an essential part of quality control and is needed for risk estimation and for system design and optimisation. This review considers the development of methods for estimating the MGD for mammography, digital breast tomosynthesis (DBT) and dedicated breast CT (DBCT). Almost all of the methodology used employs Monte Carlo calculated conversion factors to relate the measurable quantity, generally the incident air kerma, to the MGD. After a review of the size and composition of the female breast, the various mathematical models used are discussed, with particular emphasis on models for mammography. These range from simple geometrical shapes, to the more recent complex models based on patient DBCT examinations. The possibility of patient-specific dose estimates is considered as well as special diagnostic views and the effect of breast implants. Calculations using the complex models show that the MGD for mammography is overestimated by about 30% when the simple models are used. The design and uses of breast-simulating test phantoms for measuring incident air kerma are outlined and comparisons made between patient and phantom-based dose estimates. The most widely used national and international dosimetry protocols for mammography are based on different simple geometrical models of the breast, and harmonisation of these protocols using more complex breast models is desirable.
TL;DR: The kernel method is extended, to incorporate anatomical side information into the PET reconstruction model and results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
Abstract: This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
TL;DR: This first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment investigates and optimizes a Random Forest algorithm for automated organ segmentation.
Abstract: There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment. Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck–chest–abdomen–pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the TWS plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 n , (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21 patient image sections, were analyzed. The automated algorithm produced segmentation of seven material classes with a median DSC of 0.86 ± 0.03 for pediatric patient protocols, and 0.85 ± 0.04 for adult patient protocols. Additionally, 100 randomly selected patient examinations were segmented and analyzed, and a mean sensitivity of 0.91 (range: 0.82–0.98), specificity of 0.89 (range: 0.70–0.98), and accuracy of 0.90 (range: 0.76–0.98) were demonstrated. In this study, we demonstrate that this fully automated segmentation tool was able to produce fast and accurate segmentation of the neck and trunk of the body over a wide range of patient habitus and scan parameters.
TL;DR: This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging and shows substantial image noise reduction in narrow energy bins without sacrificing CT number accuracy or spatial resolution.
Abstract: Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43-73%) without sacrificing CT number accuracy or spatial resolution.
TL;DR: The first results indicate the feasibility of clinical breast CT with SR, using a simultaneous algebraic reconstruction technique to low-dose phase-retrieved data sets with a reduced number of projections.
Abstract: The aim of the SYRMA-CT collaboration is to set-up the first clinical trial of phase-contrast breast CT with synchrotron radiation (SR). In order to combine high image quality and low delivered dose a number of innovative elements are merged: a CdTe single photon counting detector, state-of-the-art CT reconstruction and phase retrieval algorithms. To facilitate an accurate exam optimization, a Monte Carlo model was developed for dose calculation using GEANT4. In this study, high isotropic spatial resolution (120 μm)(3) CT scans of objects with dimensions and attenuation similar to a human breast were acquired, delivering mean glandular doses in the range of those delivered in clinical breast CT (5-25 mGy). Due to the spatial coherence of the SR beam and the long distance between sample and detector, the images contain, not only absorption, but also phase information from the samples. The application of a phase-retrieval procedure increases the contrast-to-noise ratio of the tomographic images, while the contrast remains almost constant. After applying the simultaneous algebraic reconstruction technique to low-dose phase-retrieved data sets (about 5 mGy) with a reduced number of projections, the spatial resolution was found to be equal to filtered back projection utilizing a four fold higher dose, while the contrast-to-noise ratio was reduced by 30%. These first results indicate the feasibility of clinical breast CT with SR.
TL;DR: Subharmonic acoustic emission detection with implementation on a confocal dual-frequency piezoelectric ceramic structure has the potential to be implemented as a real-time feedback control structure for reliable indication of intact FUS-BBB opening for CNS brain drug delivery.
Abstract: Burst-tone focused ultrasound exposure in the presence of microbubbles has been demonstrated to be effective at inducing temporal and local opening of the blood-brain barrier (BBB), which promises significant clinical potential to deliver therapeutic molecules into the central nervous system (CNS). Traditional contrast-enhanced imaging confirmation after focused ultrasound (FUS) exposure serves as a post-operative indicator of the effectiveness of FUS-BBB opening, however, an indicator that can concurrently report the BBB status and BBB-opening effectiveness is required to provide effective feedback to implement this treatment clinically. In this study, we demonstrate the use of subharmonic acoustic emission detection with implementation on a confocal dual-frequency piezoelectric ceramic structure to perform real-time monitoring of FUS-BBB opening. A confocal dual-frequency (0.55 MHz/1.1 MHz) focused ultrasound transducer was designed. The 1.1 MHz spherically-curved ceramic was employed to deliver FUS exposure to induce BBB-opening, whereas the outer-ring 0.55 MHz ceramic was employed to detect the subharmonic acoustic emissions originating from the target position. In stage-1 experiments, we employed spectral analysis and performed an energy spectrum density (ESD) calculation. An optimized 0.55 MHz ESD level change was shown to effectively discriminate the occurrence of BBB-opening. Wideband acoustic emissions received from 0.55 MHz ceramics were also analyzed to evaluate its correlations with erythrocyte extravasations. In stage-2 real-time monitoring experiments, we applied the predetermined ESD change as a detection threshold in PC-controlled algorithm to predict the FUS exposure intra-operatively. In stage-1 experiment, we showed that subharmonic ESD presents distinguishable dynamics between intact BBB and opened BBB, and therefore a threshold ESD change level (5.5 dB) can be identified for BBB-opening prediction. Using this ESD change threshold detection as a surrogate to on/off control the FUS exposure in stage-2 experiments, we demonstrated both excellent sensitivity (92%) and specificity (92.3%) in discriminating BBB-opening occurrence can be obtained in animal treatments, while concurrently achieving a high positive predicted value (95.8%). Wideband ESD was also highly correlated with the occurrence and level of erythrocyte extravasations (r (2) = 0.81). The proposed system configuration and corresponding analysis based on subharmonic acoustic emissions has the potential to be implemented as a real-time feedback control structure for reliable indication of intact FUS-BBB opening for CNS brain drug delivery.