Table of contents

Magnetic particle spectrometry (MPS) is an excellent and straight forward method to determine the response of magnetic nanoparticles to an oscillating magnetic field. Such fields are applied in magnetic particle imaging (MPI). However, state of the art MPS devices lack the ability to excite particles in multidimensional field sequences that are present in MPI devices. Especially the particle behavior caused by Lissajous sequences cannot be measured with only one excitation direction. This work presents a new kind of MPS which features two excitation directions to overcome this limitation. Both field coils can drive AC as well as DC currents and are thereby able to emulate the field sequences for arbitrary spatial positions inside an MPI device. Since the DC currents can be switched very fast, the device can be used as system calibration unit and acquire system matrices in very short time. These are crucial for MPI image reconstruction. As the signal-to-noise-ratio provided by the MPS is approximately 1000 times higher than that of actual imaging devices, the time space analysis of particle signals is more precise and easier done. Four system matrices are presented in this paper which have been measured with the realized multidimensional MPS. Additionally, a time space comparison of the particle signal for Lissajous, radial and spiral trajectories is given.

Magnetic particle imaging visualizes the spatial distribution of superparamagnetic nanoparticles. Because of its key features of excellent sensitivity, high temporal and spatial resolution and biocompatibility of the tracer material it can be used in multiple medical imaging applications. The common reconstruction technique for Lissajous-type trajectories uses a system matrix that has to be previously acquired in a time-consuming calibration scan, leading to long downtimes of the scanning device. In this work, the system matrix is determined by a hybrid approach. Using the hybrid system matrix for reconstruction, the calibration downtime of the scanning device can be neglected. Furthermore, the signal to noise ratio of the hybrid system matrix is much higher, since the size of the required nanoparticle sample can be chosen independently of the desired voxel size. As the signal to noise ratio influences the reconstruction process, the resulting images have better resolution and are less affected by artefacts. Additionally, a new approach is introduced to address the background signal in image reconstruction. The common technique of subtraction of the background signal is replaced by extending the system matrix with an entry that represents the background. It is shown that this approach reduces artefacts in the reconstructed images.

Magnetic particle imaging (MPI) is a quantitative imaging modality that allows us to determine the distribution of superparamagnetic nanoparticles. Sampling is achieved by moving a field-free point (FFP) along a specific trajectory through the volume of interest. The magnetic material that lies along the path or in the close vicinity of the FFP changes its magnetization and induces a voltage in the surrounding receiver coils. Various trajectories for the FFP are conceivable, but most experimental MPI scanners either use a Cartesian or a Lissajous sampling trajectory. For the first time, this study compares both sampling methods experimentally using an MPI scanner that allows us to implement both sampling patterns. By default, the scanner is capable of scanning 2D and 3D field of views using a Lissajous trajectory. But since it also has a 1D mode, it is possible to perform Cartesian measurements by shifting the 1D scan line in a perpendicular direction to the FFP movement using the focus field. These line scans are jointly reconstructed to obtain a 2D image. In a further step, the unidirectional Cartesian trajectory is improved by interchanging the excitation and the focus-field direction leading to a bidirectional Cartesian trajectory. Our findings reveal similar results for the bidirectional Cartesian and Lissajous trajectory concerning the overall image quality and sensitivity. In a more detailed view, the bidirectional Cartesian trajectory achieves a slightly higher spatial center resolution, whereas the Lissajous trajectory is more efficient regarding the temporal resolution since less acquisition time is needed to reach an adequate image quality.

Magnetic particle imaging (MPI) has been shown to provide remarkable contrast for imaging applications such as angiography, stem cell tracking, and cancer imaging. Recently, there is growing interest in the functional imaging capabilities of MPI, where 'color MPI' techniques have explored separating different nanoparticles, which could potentially be used to distinguish nanoparticles in different states or environments. Viscosity mapping is a promising functional imaging application for MPI, as increased viscosity levels in vivo have been associated with numerous diseases such as hypertension, atherosclerosis, and cancer. In this work, we propose a viscosity mapping technique for MPI through the estimation of the relaxation time constant of the nanoparticles. Importantly, the proposed time constant estimation scheme does not require any prior information regarding the nanoparticles. We validate this method with extensive experiments in an in-house magnetic particle spectroscopy (MPS) setup at four different frequencies (between 250 Hz and 10.8 kHz) and at three different field strengths (between 5 mT and 15 mT) for viscosities ranging between 0.89 mPa · s–15.33 mPa · s. Our results demonstrate the viscosity mapping ability of MPI in the biologically relevant viscosity range.

Magnetic particle imaging (MPI) is an emerging tracer-based medical imaging modality that images non-radioactive, kidney-safe superparamagnetic iron oxide (SPIO) tracers. MPI offers quantitative, high-contrast and high-SNR images, so MPI has exceptional promise for applications such as cell tracking, angiography, brain perfusion, cancer detection, traumatic brain injury and pulmonary imaging. In assessing MPI's utility for applications mentioned above, it is important to be able to assess tracer short-term biodistribution as well as long-term clearance from the body. Here, we describe the biodistribution and clearance for two commonly used tracers in MPI: Ferucarbotran (Meito Sangyo Co., Japan) and LS-oo8 (LodeSpin Labs, Seattle, WA). We successfully demonstrate that 3D MPI is able to quantitatively assess short-term biodistribution, as well as long-term tracking and clearance of these tracers in vivo.

Optimizing tracers for individual imaging techniques is an active field of research. The purpose of this study was to perform in vitro and in vivo magnetic particle imaging (MPI) measurements using a new monodisperse and size-optimized tracer, LS-008, and to compare it with the performance of Resovist, the standard MPI tracer.

Magnetic particle spectroscopy (MPS) and in vitro MPI measurements were performed in concerns of concentration and amount of tracer in a phantom. In vivo studies were carried out in healthy FVB mice. The first group (n  =  3) received 60 µl LS-008 (87 mM) and the second (n  =  3) diluted Resovist of the same concentration and volume. Tracer injections were performed with a syringe pump during a dynamic MPI scan. For anatomic referencing MRI was applied beforehand of the MPI measurements.

Summing up MPS examinations and in vitro MPI experiments, LS-008 showed better sensitivity and spatial resolution than Resovist. In vivo both tracers can visualize the propagation of the bolus through the inferior vena cava. MPI with LS-008 did show less temporal fluctuation artifacts and the pulsation of blood due to respiratory and cardiac cycle was detectable. With LS-008 the aorta was distinguishable from the caval vein while with Resovist this failed. A liver vessel and a vessel structure leading cranially could only be observed with LS-008 and not with Resovist. Beside these structural advantages both tracers showed very different blood half-life. For LS-008 we found 88 min. Resovist did show a fast liver accumulation and a half-life of 13 min. Only with LS-008 the perfusion fraction in liver and kidney was measureable.

MPI for angiography can be significantly improved by applying more effective tracers. LS-008 shows a clear improvement concerning the delineation while resolving a larger number of vessels in comparison to Resovist. Therefore, in aspects of quality and quantity LS-008 is clearly favorable for angiographic and perfusion studies.

Magnetic particle imaging (MPI) facilitates the rapid determination of 3D in vivo magnetic nanoparticle distributions. In this work, liver MPI following intravenous injections of ferucarbotran (Resovist^®) was studied. The image reconstruction was based on a calibration measurement, the so called system function. The application of an enhanced system function sample reflecting the particle mobility and aggregation status of ferucarbotran resulted in significantly improved image reconstructions. The finding was supported by characterizations of different ferucarbotran compositions with the magnetorelaxometry and magnetic particle spectroscopy technique. For instance, similar results were obtained between ferucarbotran embedded in freeze-dried mannitol sugar and liver tissue harvested after a ferucarbotran injection. In addition, the combination of multiple shifted measurement patches for a joint reconstruction of the MPI data enlarged the field of view and increased the covering of liver MPI on magnetic resonance images noticeably.

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.

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.

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.

The objective of this study was to demonstrate the potential benefits of using high energy x-rays in comparison with the conventional mammography imaging systems for phase sensitive imaging of breast tissues with varying glandular-adipose ratios.

This study employed two modular phantoms simulating the glandular (G) and adipose (A) breast tissue composition in 50 G–50 A and 70 G–30 A percentage densities. Each phantom had a thickness of 5 cm with a contrast detail test pattern embedded in the middle. For both phantoms, the phase contrast images were acquired using a micro-focus x-ray source operated at 120 kVp and 4.5 mAs, with a magnification factor (M) of 2.5 and a detector with a 50 µm pixel pitch. The mean glandular dose delivered to the 50 G–50 A and 70 G–30 A phantom sets were 1.33 and 1.3 mGy, respectively. A phase retrieval algorithm based on the phase attenuation duality that required only a single phase contrast image was applied. Conventional low energy mammography images were acquired using GE Senographe DS and Hologic Selenia systems utilizing their automatic exposure control (AEC) settings. In addition, the automatic contrast mode (CNT) was also used for the acquisition with the GE system. The AEC mode applied higher dose settings for the 70 G–30 A phantom set. As compared to the phase contrast images, the dose levels for the AEC mode acquired images were similar while the dose levels for the CNT mode were almost double.

The observer study, contrast-to-noise ratio and figure of merit comparisons indicated a large improvement with the phase retrieved images in comparison to the AEC mode images acquired with the clinical systems for both density levels. As the glandular composition increased, the detectability of smaller discs decreased with the clinical systems, particularly with the GE system, even at higher dose settings. As compared to the CNT mode (double dose) images, the observer study also indicated that the phase retrieved images provided similar or improved detection for all disc sizes except for the disk diameters of 2 mm and 1 mm for the 50 G–50 A phantom and 3 mm and 0.5 mm for the 70 G–30 A phantom.

This study demonstrated the potential of utilizing a high energy phase sensitive x-ray imaging system to improve lesion detection and reduce radiation dose when imaging breast tissues with varying glandular compositions.

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7–8 mCi of ¹⁸F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans—each containing 1/8th of the total number of events—were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of ¹⁸F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of ${{K}_{1}}$, the tracer transport rate (ml · min⁻¹ · ml⁻¹), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced ${{K}_{1}}$ maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced ${{K}_{1}}$ estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

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 (R²  =  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.

A split feasibility formulation for the inverse problem of intensity-modulated radiation therapy treatment planning with dose-volume constraints included in the planning algorithm is presented. It involves a new type of sparsity constraint that enables the inclusion of a percentage-violation constraint in the model problem and its handling by continuous (as opposed to integer) methods. We propose an iterative algorithmic framework for solving such a problem by applying the feasibility-seeking CQ-algorithm of Byrne combined with the automatic relaxation method that uses cyclic projections. Detailed implementation instructions are furnished. Functionality of the algorithm was demonstrated through the creation of an intensity-modulated proton therapy plan for a simple 2D C-shaped geometry and also for a realistic base-of-skull chordoma treatment site. Monte Carlo simulations of proton pencil beams of varying energy were conducted to obtain dose distributions for the 2D test case. A research release of the Pinnacle³ proton treatment planning system was used to extract pencil beam doses for a clinical base-of-skull chordoma case. In both cases the beamlet doses were calculated to satisfy dose-volume constraints according to our new algorithm. Examination of the dose-volume histograms following inverse planning with our algorithm demonstrated that it performed as intended. The application of our proposed algorithm to dose-volume constraint inverse planning was successfully demonstrated. Comparison with optimized dose distributions from the research release of the Pinnacle³ treatment planning system showed the algorithm could achieve equivalent or superior results.

Compact gamma cameras with a square-shaped monolithic scintillator crystal and an array of silicon photomultipliers (SiPMs) are actively being developed for applications in areas such as small animal imaging, cancer diagnostics and radiotracer guided surgery. Statistical methods of position reconstruction, which are potentially superior to the traditional centroid method, require accurate knowledge of the spatial response of each photomultiplier. Using both Monte Carlo simulations and experimental data obtained with a camera prototype, we show that the spatial response of all photomultipliers (light response functions) can be parameterized with axially symmetric functions obtained iteratively from flood field irradiation data. The study was performed with a camera prototype equipped with a 30  ×  30  ×  2 mm³ LYSO crystal and an 8  ×  8 array of SiPMs for 140 keV gamma rays. The simulations demonstrate that the images, reconstructed with the maximum likelihood method using the response obtained with the iterative approach, exhibit only minor distortions: the average difference between the reconstructed and the true positions in X and Y directions does not exceed 0.2 mm in the central area of 22  ×  22 mm² and 0.4 mm at the periphery of the camera. A similar level of image distortions is shown experimentally with the camera prototype.

We developed a method to evaluate variations in the PET imaging process in order to characterize the relative ability of static and dynamic metrics to measure breast cancer response to therapy in a clinical trial setting. We performed a virtual clinical trial by generating 540 independent and identically distributed PET imaging study realizations for each of 22 original dynamic fluorodeoxyglucose (¹⁸F-FDG) breast cancer patient studies pre- and post-therapy. Each noise realization accounted for known sources of uncertainty in the imaging process, such as biological variability and SUV uptake time. Four definitions of SUV were analyzed, which were SUVmax, SUVmean, SUVpeak, and SUV50%. We performed a ROC analysis on the resulting SUV and kinetic parameter uncertainty distributions to assess the impact of the variability on the measurement capabilities of each metric. The kinetic macro parameter, K_i, showed more variability than SUV (mean CV K_i  =  17%, SUV  =  13%), but K_i pre- and post-therapy distributions also showed increased separation compared to the SUV pre- and post-therapy distributions (mean normalized difference K_i  =  0.54, SUV  =  0.27). For the patients who did not show perfect separation between the pre- and post-therapy parameter uncertainty distributions (ROC AUC  <  1), dynamic imaging outperformed SUV in distinguishing metabolic change in response to therapy, ranging from 12 to 14 of 16 patients over all SUV definitions and uptake time scenarios (p  <  0.05). For the patient cohort in this study, which is comprised of non-high-grade ER+  tumors, K_i outperformed SUV in an ROC analysis of the parameter uncertainty distributions pre- and post-therapy. This methodology can be applied to different scenarios with the ability to inform the design of clinical trials using PET imaging.

Multi-atlas segmentation (MAS) has been widely used to automate the delineation of organs at risk (OARs) for radiotherapy. Label fusion is a crucial step in MAS to cope with the segmentation variabilities among multiple atlases. However, most existing label fusion methods do not consider the potential dosimetric impact of the segmentation result. In this proof-of-concept study, we propose a novel geometry-dosimetry label fusion method for MAS-based OAR auto-contouring, which evaluates the segmentation performance in terms of both geometric accuracy and the dosimetric impact of the segmentation accuracy on the resulting treatment plan. Differently from the original selective and iterative method for performance level estimation (SIMPLE), we evaluated and rejected the atlases based on both Dice similarity coefficient and the predicted error of the dosimetric endpoints. The dosimetric error was predicted using our previously developed geometry-dosimetry model. We tested our method in MAS-based rectum auto-contouring on 20 prostate cancer patients. The accuracy in the rectum sub-volume close to the planning tumor volume (PTV), which was found to be a dosimetric sensitive region of the rectum, was greatly improved. The mean absolute distance between the obtained contour and the physician-drawn contour in the rectum sub-volume 2 mm away from PTV was reduced from 3.96 mm to 3.36 mm on average for the 20 patients, with the maximum decrease found to be from 9.22 mm to 3.75 mm. We also compared the dosimetric endpoints predicted for the obtained contours with those predicted for the physician-drawn contours. Our method led to smaller dosimetric endpoint errors than the SIMPLE method in 15 patients, comparable errors in 2 patients, and slightly larger errors in 3 patients. These results indicated the efficacy of our method in terms of considering both geometric accuracy and dosimetric impact during label fusion. Our algorithm can be applied to different tumor sites and radiation treatments, given a specifically trained geometry-dosimetry model.

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79−0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Monte Carlo (MC) simulation is considered as the most accurate method for calculation of absorbed dose and fundamental physics quantities related to biological effects in carbon ion therapy. To improve its computational efficiency, we have developed a GPU-oriented fast MC package named goCMC, for carbon therapy. goCMC simulates particle transport in voxelized geometry with kinetic energy up to 450 MeV u⁻¹. Class II condensed history simulation scheme with a continuous slowing down approximation was employed. Energy straggling and multiple scattering were modeled. δ-electrons were terminated with their energy locally deposited. Four types of nuclear interactions were implemented in goCMC, i.e. carbon–hydrogen, carbon–carbon, carbon–oxygen and carbon–calcium inelastic collisions. Total cross section data from Geant4 were used. Secondary particles produced in these interactions were sampled according to particle yield with energy and directional distribution data derived from Geant4 simulation results. Secondary charged particles were transported following the condensed history scheme, whereas secondary neutral particles were ignored. goCMC was developed under OpenCL framework and is executable on different platforms, e.g. GPU and multi-core CPU. We have validated goCMC with Geant4 in cases with different beam energy and phantoms including four homogeneous phantoms, one heterogeneous half-slab phantom, and one patient case. For each case $3\times {{10}^{7}}$ carbon ions were simulated, such that in the region with dose greater than 10% of maximum dose, the mean relative statistical uncertainty was less than 1%. Good agreements for dose distributions and range estimations between goCMC and Geant4 were observed. 3D gamma passing rates with 1%/1 mm criterion were over 90% within 10% isodose line except in two extreme cases, and those with 2%/1 mm criterion were all over 96%. Efficiency and code portability were tested with different GPUs and CPUs. Depending on the beam energy and voxel size, the computation time to simulate ${{10}^{7}}$ carbons was 9.9–125 s, 2.5–50 s and 60–612 s on an AMD Radeon GPU card, an NVidia GeForce GTX 1080 GPU card and an Intel Xeon E5-2640 CPU, respectively. The combined accuracy, efficiency and portability make goCMC attractive for research and clinical applications in carbon ion therapy.

LSO and LYSO are today the most common scintillators used in positron emission tomography. Lutetium contains traces of ¹⁷⁶Lu, a radioactive isotope that decays β⁻ with a cascade of γ photons in coincidence. Therefore, Lutetium-based scintillators are characterized by a small natural radiation background. In this paper, we investigate and characterize the ¹⁷⁶Lu radiation background via experiments performed on LSO-based PET scanners. LSO background was measured at different energy windows and different time coincidence windows, and by using shields to alter the original spectrum. The effect of radiation background in particularly count-starved applications, such as ⁹⁰Y imaging, is analysed and discussed. Depending on the size of the PET scanner, between 500 and 1000 total random counts per second and between 3 and 5 total true coincidences per second were measured in standard coincidence mode. The LSO background counts in a Siemens mCT in the standard PET energy and time windows are in general negligible in terms of trues, and are comparable to that measured in a BGO scanner of similar size.

Cone-beam CT (CBCT) for musculoskeletal imaging would benefit from a method to reduce the effects of involuntary patient motion. In particular, the continuing improvement in spatial resolution of CBCT may enable tasks such as quantitative assessment of bone microarchitecture (0.1 mm–0.2 mm detail size), where even subtle, sub-mm motion blur might be detrimental. We propose a purely image based motion compensation method that requires no fiducials, tracking hardware or prior images. A statistical optimization algorithm (CMA-ES) is used to estimate a motion trajectory that optimizes an objective function consisting of an image sharpness criterion augmented by a regularization term that encourages smooth motion trajectories. The objective function is evaluated using a volume of interest (VOI, e.g. a single bone and surrounding area) where the motion can be assumed to be rigid. More complex motions can be addressed by using multiple VOIs. Gradient variance was found to be a suitable sharpness metric for this application. The performance of the compensation algorithm was evaluated in simulated and experimental CBCT data, and in a clinical dataset. Motion-induced artifacts and blurring were significantly reduced across a broad range of motion amplitudes, from 0.5 mm to 10 mm. Structure similarity index (SSIM) against a static volume was used in the simulation studies to quantify the performance of the motion compensation. In studies with translational motion, the SSIM improved from 0.86 before compensation to 0.97 after compensation for 0.5 mm motion, from 0.8 to 0.94 for 2 mm motion and from 0.52 to 0.87 for 10 mm motion (~70% increase). Similar reduction of artifacts was observed in a benchtop experiment with controlled translational motion of an anthropomorphic hand phantom, where SSIM (against a reconstruction of a static phantom) improved from 0.3 to 0.8 for 10 mm motion. Application to a clinical dataset of a lower extremity showed dramatic reduction of streaks and improvement in delineation of tissue boundaries and trabecular structures throughout the whole volume. The proposed method will support new applications of extremity CBCT in areas where patient motion may not be sufficiently managed by immobilization, such as imaging under load and quantitative assessment of subchondral bone architecture.

We propose bilateral total body irradiation (TBI) utilizing a 3D printer and a 3D optical scanner. We acquired surface information of an anthropomorphic phantom with the 3D scanner and fabricated the 3D compensator with the 3D printer, which could continuously compensate for the lateral missing tissue of an entire body from the beam's eye view. To test the system's performance, we measured doses with optically stimulated luminescent dosimeters (OSLDs) as well as EBT3 films with the anthropomorphic phantom during TBI without a compensator, conventional bilateral TBI, and TBI with the 3D compensator (3D TBI). The 3D TBI showed the most uniform dose delivery to the phantom. From the OSLD measurements of the 3D TBI, the deviations between the measured doses and the prescription dose ranged from  −6.7% to 2.4% inside the phantom and from  −2.3% to 0.6% on the phantom's surface. From the EBT3 film measurements, the prescription dose could be delivered to the entire body of the phantom within  ±10% accuracy, except for the chest region, where tissue heterogeneity is extreme. The 3D TBI doses were much more uniform than those of the other irradiation techniques, especially in the anterior-to-posterior direction. The 3D TBI was advantageous, owing to its uniform dose delivery as well as its efficient treatment procedure.

Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm's growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging23501–9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage311116–28; Law and Chung 2008 European Conf. on Computer Vision; Law and Chung 2009 IEEE Trans. Image Process. 18596–612; Wang 2015 J. Neurosci. Methods24130–6) with manually optimized parameters. Our system has also been applied clinically for cerebral aneurysm development analysis. Experimental results on 10 patients' data, with two 3D CT scans per patient, show that our system's automatic diagnosis outcomes are consistent with clinicians' manual measurements.

Fibroglandular tissue volume and percent density can be estimated in unprocessed mammograms using a physics-based method, which relies on an internal reference value representing the projection of fat only. However, pixels representing fat only may not be present in dense breasts, causing an underestimation of density measurements. In this work, we investigate alternative approaches for obtaining a tissue reference value to improve density estimations, particularly in dense breasts.

Two of three investigated reference values (F1, F2) are percentiles of the pixel value distribution in the breast interior (the contact area of breast and compression paddle). F1 is determined in a small breast interior, which minimizes the risk that peripheral pixels are included in the measurement at the cost of increasing the chance that no proper reference can be found. F2 is obtained using a larger breast interior. The new approach which is developed for very dense breasts does not require the presence of a fatty tissue region. As reference region we select the densest region in the mammogram and assume that this represents a projection of entirely dense tissue embedded between the subcutaneous fatty tissue layers. By measuring the thickness of the fat layers a reference (F3) can be computed. To obtain accurate breast density estimates irrespective of breast composition we investigated a combination of the results of the three reference values. We collected 202 pairs of MRI's and digital mammograms from 119 women. We compared the percent dense volume estimates based on both modalities and calculated Pearson's correlation coefficients.

With the references F1–F3 we found respectively a correlation of $\text{R}=0.80$, $\text{R}=0.89$ and $\text{R}=0.74$. Best results were obtained with the combination of the density estimations ($\text{R}=0.90$).

Results show that better volumetric density estimates can be obtained with the hybrid method, in particular for dense breasts, when algorithms are combined to obtain a fatty tissue reference value depending on breast composition.

Early structural changes to the heart, including the chambers and the coronary arteries, provide important information on pre-clinical heart disease like cardiac failure. Currently, contrast-enhanced cardiac computed tomography angiography (CCTA) is the preferred modality for the visualization of the cardiac chambers and the coronaries. In clinical practice not every patient undergoes a CCTA scan; many patients receive only a non-contrast-enhanced calcium scoring CT scan (CTCS), which has less radiation dose and does not require the administration of contrast agent. Quantifying cardiac structures in such images is challenging, as they lack the contrast present in CCTA scans. Such quantification would however be relevant, as it enables population based studies with only a CTCS scan. The purpose of this work is therefore to investigate the feasibility of automatic segmentation and quantification of cardiac structures viz whole heart, left atrium, left ventricle, right atrium, right ventricle and aortic root from CTCS scans. A fully automatic multi-atlas-based segmentation approach is used to segment the cardiac structures. Results show that the segmentation overlap between the automatic method and that of the reference standard have a Dice similarity coefficient of 0.91 on average for the cardiac chambers. The mean surface-to-surface distance error over all the cardiac structures is $1.4\pm 1.7$ mm. The automatically obtained cardiac chamber volumes using the CTCS scans have an excellent correlation when compared to the volumes in corresponding CCTA scans, a Pearson correlation coefficient (R) of 0.95 is obtained. Our fully automatic method enables large-scale assessment of cardiac structures on non-contrast-enhanced CT scans.

Particle therapy facilities often require Monte Carlo (MC) simulations to overcome intrinsic limitations of analytical treatment planning systems (TPS) related to the description of the mixed radiation field and beam interaction with tissue inhomogeneities. Some of these uncertainties may affect the computation of effective dose distributions; therefore, particle therapy dedicated MC codes should provide both absorbed and biological doses. Two biophysical models are currently applied clinically in particle therapy: the local effect model (LEM) and the microdosimetric kinetic model (MKM). In this paper, we describe the coupling of the NIRS (National Institute for Radiological Sciences, Japan) clinical dose to the FLUKA MC code. We moved from the implementation of the model itself to its application in clinical cases, according to the NIRS approach, where a scaling factor is introduced to rescale the (carbon-equivalent) biological dose to a clinical dose level. A high level of agreement was found with published data by exploring a range of values for the MKM input parameters, while some differences were registered in forward recalculations of NIRS patient plans, mainly attributable to differences with the analytical TPS dose engine (taken as reference) in describing the mixed radiation field (lateral spread and fragmentation). We presented a tool which is being used at the Italian National Center for Oncological Hadrontherapy to support the comparison study between the NIRS clinical dose level and the LEM dose specification.

This paper demonstrates through Monte Carlo simulations that a practical positron emission tomograph with (1) deep scintillators for efficient detection, (2) double-ended readout for depth-of-interaction information, (3) fixed-level analog triggering, and (4) accurate calibration and timing data corrections can achieve a coincidence resolving time (CRT) that is not far above the statistical lower bound.

One Monte Carlo algorithm simulates a calibration procedure that uses data from a positron point source. Annihilation events with an interaction near the entrance surface of one scintillator are selected, and data from the two photodetectors on the other scintillator provide depth-dependent timing corrections. Another Monte Carlo algorithm simulates normal operation using these corrections and determines the CRT. A third Monte Carlo algorithm determines the CRT statistical lower bound by generating a series of random interaction depths, and for each interaction a set of random photoelectron times for each of the two photodetectors. The most likely interaction times are determined by shifting the depth-dependent probability density function to maximize the joint likelihood for all the photoelectron times in each set.

Example calculations are tabulated for different numbers of photoelectrons and photodetector time jitters for three 3  ×  3  ×  30 mm³ scintillators: Lu₂SiO₅:Ce,Ca (LSO), LaBr₃:Ce, and a hypothetical ultra-fast scintillator. To isolate the factors that depend on the scintillator length and the ability to estimate the DOI, CRT values are tabulated for perfect scintillator-photodetectors. For LSO with 4000 photoelectrons and single photoelectron time jitter of the photodetector J  =  0.2 ns (FWHM), the CRT value using the statistically weighted average of corrected trigger times is 0.098 ns FWHM and the statistical lower bound is 0.091 ns FWHM. For LaBr₃:Ce with 8000 photoelectrons and J  =  0.2 ns FWHM, the CRT values are 0.070 and 0.063 ns FWHM, respectively. For the ultra-fast scintillator with 1 ns decay time, 4000 photoelectrons, and J  =  0.2 ns FWHM, the CRT values are 0.021 and 0.017 ns FWHM, respectively. The examples also show that calibration and correction for depth-dependent variations in pulse height and in annihilation and optical photon transit times are necessary to achieve these CRT values.

The purpose of this study is to develop an adaptive prior knowledge guided image estimation technique to reduce the scan angle needed in the limited-angle intrafraction verification (LIVE) system for 4D-CBCT reconstruction.

The LIVE system has been previously developed to reconstruct 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to further reduce the scanning angle needed to reconstruct the 4D-CBCT images for faster intrafraction verification. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on kV-MV projections acquired in extremely limited angle (orthogonal 3°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of the respiratory motion.

The 4D digital extended-cardiac-torso (XCAT) phantom and a CIRS 008A dynamic thoracic phantom were used to evaluate the effectiveness of this technique. The reconstruction accuracy of the technique was evaluated by calculating both the center-of-mass-shift (COMS) and 3D volume-percentage-difference (VPD) of the tumor in reconstructed images and the true on-board images. The performance of the technique was also assessed with varied breathing signals against scanning angle, lesion size, lesion location, projection sampling interval, and scanning direction.

In the XCAT study, using orthogonal-view of 3° kV and portal MV projections, this technique achieved an average tumor COMS/VPD of 0.4  ±  0.1 mm/5.5  ±  2.2%, 0.6  ±  0.3 mm/7.2  ±  2.8%, 0.5  ±  0.2 mm/7.1  ±  2.6%, 0.6  ±  0.2 mm/8.3  ±  2.4%, for baseline drift, amplitude variation, phase shift, and patient breathing signal variation, respectively. In the CIRS phantom study, this technique achieved an average tumor COMS/VPD of 0.7  ±  0.1 mm/7.5  ±  1.3% for a 3 cm lesion and 0.6  ±  0.2 mm/11.4  ±  1.5% for a 2 cm lesion in the baseline drift case. The average tumor COMS/VPD were 0.5  ±  0.2 mm/10.8  ±  1.4%, 0.4  ±  0.3 mm/7.3  ±  2.9%, 0.4  ±  0.2 mm/7.4  ±  2.5%, 0.4  ±  0.2 mm/7.3  ±  2.8% for the four real patient breathing signals, respectively. Results demonstrated that the adaptive prior knowledge guided image estimation technique with LIVE system is robust against scanning angle, lesion size, location and scanning direction. It can estimate on-board images accurately with as little as 6 projections in orthogonal-view 3° angle.

In conclusion, adaptive prior knowledge guided image reconstruction technique accurately estimates 4D-CBCT images using extremely-limited angle and projections. This technique greatly improves the efficiency and accuracy of LIVE system for ultrafast 4D intrafraction verification of lung SBRT treatments.

Many real-time imaging techniques have been developed to localize a target in 3D space or in a 2D beam's eye view (BEV) plane for intrafraction motion tracking in radiation therapy. With tracking system latency, the 3D-modeled method is expected to be more accurate even in terms of 2D BEV tracking error. No quantitative analysis, however, has been reported. In this study, we simulated co-planar arc deliveries using respiratory motion data acquired from 42 patients to quantitatively compare the accuracy between 2D BEV and 3D-modeled tracking in arc therapy and to determine whether 3D information is needed for motion tracking.

We used our previously developed low kV dose adaptive MV–kV imaging and motion compensation framework as a representative of 3D-modeled methods. It optimizes the balance between additional kV imaging dose and 3D tracking accuracy and solves the MLC blockage issue.

With simulated Gaussian marker detection errors (zero mean and 0.39 mm standard deviation) and ~155/310/460 ms tracking system latencies, the mean percentage of time that the target moved  >2 mm from the predicted 2D BEV position are 1.1%/4.0%/7.8% and 1.3%/5.8%/11.6% for the 3D-modeled and 2D-only tracking, respectively. The corresponding average BEV RMS errors are 0.67/0.90/1.13 mm and 0.79/1.10/1.37 mm. Compared to the 2D method, the 3D method reduced the average RMS unresolved motion along the beam direction from ~3 mm to ~1 mm, resulting in on average only  <1% dosimetric advantage in the depth direction. Only for a small fraction of the patients, when tracking latency is long, the 3D-modeled method showed significant improvement of BEV tracking accuracy, indicating potential dosimetric advantage. However, if the tracking latency is short (~150 ms or less), those improvements are limited. Therefore, 2D BEV tracking has sufficient targeting accuracy for most clinical cases. The 3D technique is, however, still important in solving the MLC blockage problem during 2D BEV tracking.

Fluorescent nuclear track detectors (FNTDs) allow for visualization of single-particle traversal in clinical ion beams. The point spread function of the confocal readout has so far hindered a more detailed characterization of the track spots—the ion's characteristic signature left in the FNTD. Here we report on the readout of the FNTD by optical nanoscopy, namely stimulated emission depletion microscopy. It was firstly possible to visualize the track spots of carbon ions and protons beyond the diffraction limit of conventional light microscopy with a resolving power of approximately 80 nm (confocal: 320 nm). A clear discrimination of the spatial width, defined by the full width half maximum of track spots from particles (proton and carbon ions), with a linear energy transfer (LET) ranging from approximately 2–1016 keV µm⁻¹ was possible. Results suggest that the width depends on LET but not on particle charge within the uncertainties. A discrimination of particle type by width thus does not seem possible (as well as with confocal microscopy). The increased resolution, however, could allow for refined determination of the cross-sectional area facing substantial energy deposition. This work could pave the way towards development of optical nanoscopy-based analysis of radiation-induced cellular response using cell-fluorescent ion track hybrid detectors.

A 3D anatomical computational model is developed to assess thermal effects due to exposure to the electromagnetic field required to power a new investigational active implantable microvalve for the treatment of glaucoma. Such a device, located in the temporal superior eye quadrant, produces a filtering bleb, which is included in the geometry of the model, together with the relevant ocular structures. The electromagnetic field source—a planar coil—as well as the microvalve antenna and casing are also included. Exposure to the electromagnetic field source of an implanted and a non-implanted subject are simulated by solving a magnetic potential formulation, using the finite element method. The maximum SAR₁₀ is reached in the eyebrow and remains within the limits suggested by the IEEE and ICNIRP standards. The anterior chamber, filtering bleb, iris and ciliary body are the ocular structures where more absorption occurs. The temperature rise distribution is also obtained by solving the bioheat equation with the finite element method. The numerical results are compared with the in vivo measurements obtained from four rabbits implanted with the microvalve and exposed to the electromagnetic field source.