Table of contents

Despite several attractive properties, the poor timing performance of compound semiconductor detectors such as CdTe and CdZnTe has hindered their use in commercial PET imaging systems. The standard method of pulse timing with such detectors is to employ a constant-fraction discriminator at the output of a timing filter which is fed by the pulses from a charge-sensitive preamplifier. The method has led to a time resolution of about 10 ns at full-width at half-maximum (FWHM) with 1 mm thick CdTe detectors. This paper presents a detailed investigation on the parameters limiting the timing performance of Ohmic contact planar CdTe detectors with the standard pulse timing method. The jitter and time-walk errors are studied through simulation and experimental measurements and it is revealed that the best timing results obtained with the standard timing method suffer from a significant loss of coincidence events (~50%). In order to improve the performance of the detectors with full detection efficiency, a new digital pulse timing method based on a simple pattern recognition technique was developed. A time resolution of 3.29  ±  0.10 ns (FWHM) in the energy range of 300–650 keV was achieved with an Ohmic contact planar CdTe detector (5  ×  5  ×  1 mm³). The digital pulse processing method was also used to correct for the charge-trapping effect and an improvement in the energy resolution from 4.83  ±  0.66% to 2.780  ±  0.002% (FWHM) at 511 keV was achieved. Further improvement of time resolution through a moderate cooling of the detector and the application of the method to other detector structures are also discussed.

Motion correction of 4D dynamic contrast enhanced MRI (DCE-MRI) series is required for diagnostic evaluation of liver lesions. The registration, however, is a challenging task, owing to rapid changes in image appearance. In this study, two different registration approaches are compared; a conventional pairwise method applying mutual information as metric and a groupwise method applying a principal component analysis based metric, introduced by Huizinga et al (2016). The pairwise method transforms the individual 3D images one by one to a reference image, whereas the groupwise registration method computes the metric on all the images simultaneously, exploiting the temporal information, and transforms all 3D images to a common space. The performance of the two registration methods was evaluated using 70 clinical 4D DCE-MRI series with the focus on the liver. The evaluation was based on the smoothness of the time intensity curves in lesions, lesion volume change after deformation and the smoothness of spatial deformation. Furthermore, the visual quality of subtraction images (pre-contrast image subtracted from the post contrast images) before and after registration was rated by two observers. Both registration methods improved the alignment of the DCE-MRI images in comparison to the non-corrected series. Furthermore, the groupwise method achieved better temporal alignment with smoother spatial deformations than the pairwise method. The quality of the subtraction images was graded satisfactory in 32% of the cases without registration and in 77% and 80% of the cases after pairwise and groupwise registration, respectively. In conclusion, the groupwise registration method outperforms the pairwise registration method and achieves clinically satisfying results. Registration leads to improved subtraction images.

Ionization cluster size distributions produced in the sensitive volume of an ion-counting wall-less nanodosimeter by monoenergetic carbon ions with energies between 45 MeV and 150 MeV were measured at the TANDEM-ALPI ion accelerator facility complex of the LNL-INFN in Legnaro. Those produced by monoenergetic helium ions with energies between 2 MeV and 20 MeV were measured at the accelerator facilities of PTB and with a ²⁴¹Am alpha particle source. C₃H₈ was used as the target gas. The ionization cluster size distributions were measured in narrow beam geometry with the primary beam passing the target volume at specified distances from its centre, and in broad beam geometry with a fan-like primary beam. By applying a suitable drift time window, the effective size of the target volume was adjusted to match the size of a DNA segment. The measured data were compared with the results of simulations obtained with the PTB Monte Carlo code PTra. Before the comparison, the simulated cluster size distributions were corrected with respect to the background of additional ionizations produced in the transport system of the ionized target gas molecules. Measured and simulated characteristics of the particle track structure are in good agreement for both types of primary particles and for both types of the irradiation geometry. As the range in tissue of the ions investigated is within the typical extension of a spread-out Bragg peak, these data are useful for benchmarking not only 'general purpose' track structure simulation codes, but also treatment planning codes used in hadron therapy. Additionally, these data sets may serve as a data base for codes modelling the induction of radiation damages at the DNA-level as they almost completely characterize the ionization component of the nanometric track structure.

For the independent validation of treatment plans, we developed a fully automated Monte Carlo (MC)-based patient dose calculation system with the tool for particle simulation (TOPAS) and proton therapy machine installed at the National Cancer Center in Korea to enable routine and automatic dose recalculation for each patient. The proton beam nozzle was modeled with TOPAS to simulate the therapeutic beam, and MC commissioning was performed by comparing percent depth dose with the measurement. The beam set-up based on the prescribed beam range and modulation width was automated by modifying the vendor-specific method. The CT phantom was modeled based on the DICOM CT files with TOPAS-built-in function, and an in-house-developed C++ code directly imports the CT files for positioning the CT phantom, RT-plan file for simulating the treatment plan, and RT-structure file for applying the Hounsfield unit (HU) assignment, respectively. The developed system was validated by comparing the dose distributions with those calculated by the treatment planning system (TPS) for a lung phantom and two patient cases of abdomen and internal mammary node. The results of the beam commissioning were in good agreement of up to 0.8 mm²${\rm g}^{-1}$ for B8 option in both of the beam range and the modulation width of the spread-out Bragg peaks. The beam set-up technique can predict the range and modulation width with an accuracy of 0.06% and 0.51%, respectively, with respect to the prescribed range and modulation in arbitrary points of B5 option (128.3, 132.0, and 141.2 mm²${\rm g}^{-1}$ of range). The dose distributions showed higher than 99% passing rate for the 3D gamma index (3 mm distance to agreement and 3% dose difference) between the MC simulations and the clinical TPS in the target volume. However, in the normal tissues, less favorable agreements were obtained for the radiation treatment planning with the lung phantom and internal mammary node cases. The discrepancies might come from the limitations of the clinical TPS, which is the inaccurate dose calculation algorithm for the scattering effect, in the range compensator and inhomogeneous material. Moreover, the steep slope of the compensator, conversion of the HU values to the human phantom, and the dose calculation algorithm for the HU assignment also could be reasons of the discrepancies. The current study could be used for the independent dose validation of treatment plans including high inhomogeneities, the steep compensator, and riskiness such as lung, head & neck cases. According to the treatment policy, the dose discrepancies predicted with MC could be used for the acceptance decision of the original treatment plan.

Modern radiotherapy of female breast cancers often employs high dose rate brachytherapy, where a radioactive source is moved inside catheters, implanted in the female breast, according to a prescribed treatment plan. Source localization relative to the patient's anatomy is determined with solenoid sensors whose spatial positions are measured with an electromagnetic tracking system. Precise sensor dwell position determination is of utmost importance to assure irradiation of the cancerous tissue according to the treatment plan. We present a hybrid data analysis system which combines multi-dimensional scaling with particle filters to precisely determine sensor dwell positions in the catheters during subsequent radiation treatment sessions. Both techniques are complemented with empirical mode decomposition for the removal of superimposed breathing artifacts. We show that the hybrid model robustly and reliably determines the spatial positions of all catheters used during the treatment and precisely determines any deviations of actual sensor dwell positions from the treatment plan. The hybrid system only relies on sensor positions measured with an EMT system and relates them to the spatial positions of the implanted catheters as initially determined with a computed x-ray tomography.

Scatter coincidences contain hidden information about the activity distribution on the positron emission tomography (PET) imaging system. However, in conventional reconstruction, the scattered data cause the blurring of images and thus are estimated and subtracted from detected coincidences. List mode format provides a new aspect to use time of flight (TOF) and energy information of each coincidence in the reconstruction process. In this study, a novel approach is proposed to reconstruct activity distribution using the scattered data in the PET system. For each single scattering coincidence, a scattering angle can be determined by the recorded energy of the detected photons, and then possible locations of scattering can be calculated based on the scattering angle. Geometry equations show that these sites lie on two arcs in 2D mode or the surface of a prolate spheroid in 3D mode, passing through the pair of detector elements. The proposed method uses a novel and flexible technique to estimate source origin locations from the possible scattering locations, using the TOF information. Evaluations were based on a Monte-Carlo simulation of uniform and non-uniform phantoms at different resolutions of time and detector energy. The results show that although the energy uncertainties deteriorate the image spatial resolution in the proposed method, the time resolution has more impact on image quality than the energy resolution. With progress of the TOF system, the reconstruction using the scattered data can be used in a complementary manner, or to improve image quality in the next generation of PET systems.

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

The feasibility of sample interval modulation (SLIM) magnetic resonance elastography (MRE) for the in vivo mouse brain is assessed, and an alternative SLIM-MRE encoding method is introduced. In SLIM-MRE, the phase accumulation for each motion direction is encoded simultaneously by varying either the start time of the motion encoding gradient (MEG), SLIM-phase constant (SLIM-PC), or the initial phase of the MEG, SLIM-phase varying (SLIM-PV). SLIM-PC provides gradient moment nulling, but the mutual gradient shift necessitates increased echo time (TE). SLIM-PV requires no increased TE, but exhibits non-uniform flow compensation. Comparison was to conventional MRE using six C57BL/6 mice. For SLIM-PC, the Spearman's rank correlation to conventional MRE for the shear storage and loss modulus images were 80% and 76%, respectively, and likewise for SLIM-PV, 73% and 69%, respectively. The results of the Wilcoxon rank sum test showed that there were no statistically significant differences between the spatially averaged shear moduli derived from conventional-MRE, SLIM-PC, and SLIM-PV acquisitions. Both SLIM approaches were comparable to conventional MRE scans with Spearman's rank correlation of 69%–80% and with 3 times reduction in scan time. The SLIM-PC method had the best correlation, and SLIM-PV may be a useful tool in experimental conditions, where both measurement time and T2 relaxation is critical.

Various types of treatment units, such as CyberKnife, TomoTherapy and C-arm linear accelerators (LINACs) are operated using flattening filter free (FFF) photon beams. Their reference dosimetry, however, is currently based on codes of practice that provide data which were primarily developed and tested for high-energy photon beams with flattening filter (WFF). The aim of this work was to introduce equivalent uniform square field sizes of FFF beams to serve as a basis of a unified reference dosimetry procedure applicable to all aforementioned FFF machines. For this purpose, in-house determined experimental data together with published data of the ratio of doses at depths of 20 cm and 10 cm in water (D_20,10) were used to characterize the depth dose distribution of 6 and 10 MV WFF and FFF beams. These data were analyzed for field sizes ranging from 2  ×  2 cm² to 40  ×  40 cm². A scatter function that takes the lateral profiles of the individual beams into account was fitted to the experimental data. The lateral profiles of the WFF beams were assumed to be uniform, while those of the FFF beams were approximated using fourth or sixth order polynomials. The scatter functions of the FFF beams were recalculated using a uniform lateral profile (the same as the physical profile of the WFF beams), and are henceforth denoted as virtual uniform FFF beams (VUFFF). The field sizes of the VUFFF beams having the same scatter contribution as the corresponding FFF beams at a given field size were defined as the equivalent uniform square field (EQUSF) size. Data from four different LINACs with 18 different beams in total, as well as a CyberKnife beam, were analyzed. The average values of EQUSFs over all investigated LINACs of the conventional 10  ×  10 cm² reference fields of 6 MV and 10 MV FFF beams for C-arm LINACs and machine-specific reference fields for CyberKnife and TomoTherapy were 9.5 cm, 9 cm, 5.0 cm and 6.5 cm respectively. The standard deviation of the mean of these EQUSFs was below 0.1 cm. It has been shown that with the introduction of a VUFFF beam, EQUSFs can be consistently defined for a variety of energies and collimations. These EQUSFs can form the basis for a unified reference dosimetry protocol for all different types of FFF machines.

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging.

A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors.

The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96.

Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

Gold nanoparticles (GNPs) injected in a body for dose enhancement in radiation therapy are known to form clusters. We investigated the dependence of dose enhancement on the GNP morphology using Monte-Carlo simulations and compared the model predictions with experimental data. The cluster morphology was approximated as a body-centred cubic (BCC) structure by placing GNPs at the 8 corners and the centre of a cube with an edge length of 0.22–1.03 µm in a 4  ×  4  ×  4 µm³ water-filled phantom. We computed the dose enhancement ratio (DER) for 50 and 260 kVp photons as a function of the distance from the cube centre for 12 different cube sizes. A 10 nm-wide concentric shell shaped detector was placed up to 100 nm away from a GNP at the cube centre. For model validation, simulations based on BCC and nanoparticle random distribution (NRD) models were performed using parameters that corresponded to the experimental conditions, which measured increases in the relative biological effect due to GNPs. We employed the linear quadratic model to compute cell surviving fraction (SF) and sensitizer enhancement ratio (SER). The DER is inversely proportional to the distance to the GNPs. The largest DERs were 1.97 and 1.80 for 50 kVp and 260 kVp photons, respectively. The SF predicted by the BCC model agreed with the experimental value within 10%, up to a 5 Gy dose, while the NRD model showed a deviation larger than 10%. The SERs were 1.21  ±  0.13, 1.16  ±  0.11, and 1.08  ±  0.11 according to the experiment, BCC, and NRD models, respectively. We most accurately predicted the GNP radiosensitization effect using the BCC approximation and suggest that the BCC model is effective for use in nanoparticle dosimetry.

Calcifications are products of mineralization whose presence is usually associated with pathological conditions. The minerals mostly seen in several diseases are calcium oxalate (CaC₂O₄), calcium carbonate (CaCO₃) and hydroxyapatite (HAp). Up to date, there is no in vivo method that could discriminate between minerals. To this aim, a dual energy x-ray method was developed in the present study. An analytical model was implemented for the determination of the Calcium/Phosphorus mass ratio (${{{m}_{{\rm Ca}}}}/{{{m}_{{\rm P}}}}\;$). The simulation was carried out using monoenergetic and polyenergetic x-rays and various calcification thicknesses (100–1000 $\mu {\rm m}$) and types (CaC₂O₄, CaCO₃, HAp). The experimental evaluation of the method was performed using the optimized irradiation conditions obtained from the simulation study. X-ray tubes, combined with energy dispersive and energy integrating (imaging) detectors, were used for the determination of the ${{{m}_{{\rm Ca}}}}/{{{m}_{{\rm P}}}}\;$ in phantoms of different mineral types and thicknesses. Based on the results of the experimental procedure, statistical significant difference was observed between the different types of minerals when calcification thicknesses were 300 $\mu {\rm m}$ or higher.

In digital breast tomosynthesis (DBT), the high-attenuation metallic clips marking a previous biopsy site in the breast cause errors in the estimation of attenuation along the ray paths intersecting the markers during reconstruction, which result in interplane and inplane artifacts obscuring the visibility of subtle lesions. We proposed a new metal artifact reduction (MAR) method to improve image quality. Our method uses automatic detection and segmentation to generate a marker location map for each projection (PV). A voting technique based on the geometric correlation among different PVs is designed to reduce false positives (FPs) and to label the pixels on the PVs and the voxels in the imaged volume that represent the location and shape of the markers. An iterative diffusion method replaces the labeled pixels on the PVs with estimated tissue intensity from the neighboring regions while preserving the original pixel values in the neighboring regions. The inpainted PVs are then used for DBT reconstruction. The markers are repainted on the reconstructed DBT slices for radiologists' information. The MAR method is independent of reconstruction techniques or acquisition geometry. For the training set, the method achieved 100% success rate with one FP in 19 views. For the test set, the success rate by view was 97.2% for core biopsy microclips and 66.7% for clusters of large post-lumpectomy markers with a total of 10 FPs in 58 views. All FPs were large dense benign calcifications that also generated artifacts if they were not corrected by MAR. For the views with successful detection, the metal artifacts were reduced to a level that was not visually apparent in the reconstructed slices. The visibility of breast lesions obscured by the reconstruction artifacts from the metallic markers was restored.

As a result of the shallow depth of focus of the optical imaging system, the use of standard filtered back projection in optical projection tomography causes space-variant tangential blurring that increases with the distance to the rotation axis. We present a novel optical tomographic image reconstruction technique that incorporates the point spread function of the imaging lens in an iterative reconstruction. The technique is demonstrated using numerical simulations, tested on experimental optical projection tomography data of single fluorescent beads, and applied to high-resolution emission optical projection tomography imaging of an entire zebrafish larva. Compared to filtered back projection our results show greatly reduced radial and tangential blurring over the entire $5.2\times5.2$ mm² field of view, and a significantly improved signal to noise ratio.

Nowadays there is a rising interest towards exploiting new therapeutical beams beyond carbon ions and protons. In particular, ¹⁶O ions are being widely discussed due to their increased LET distribution. In this contribution, we report on the first experimental verification of biologically optimized treatment plans, accounting for different biological effects, generated with the TRiP98 planning system with ¹⁶O beams, performed at HIT and GSI. This implies the measurements of 3D profiles of absorbed dose as well as several biological measurements. The latter includes the measurements of relative biological effectiveness along the range of linear energy transfer values from  ≈20 up to  ≈750 keV μm⁻¹, oxygen enhancement ratio values and the verification of the kill-painting approach, to overcome hypoxia, with a phantom imitating an unevenly oxygenated target. With the present implementation, our treatment planning system is able to perform a comparative analysis of different ions, according to any given condition of the target. For the particular cases of low target oxygenation, ¹⁶O ions demonstrate a higher peak-to-entrance dose ratio for the same cell killing in the target region compared to ¹²C ions. Based on this phenomenon, we performed a short computational analysis to reveal the potential range of treatment plans, where ¹⁶O can benefit over lighter modalities. It emerges that for more hypoxic target regions (partial oxygen pressure of  ≈0.15% or lower) and relatively low doses (≈4 Gy or lower) the choice of ¹⁶O over ¹²C or ⁴He may be justified.

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units (${\rm HU}$) in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous μ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into ${\rm HU}$ was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of $4~{\rm mm}$ corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image.

The role of multi-parametric (mp)MRI in the diagnosis and treatment of prostate cancer has increased considerably. An alternative to visual inspection of mpMRI is the evaluation using histogram-based (first order statistics) parameters and textural features (second order statistics). The aims of the present work were to investigate the relationship between benign and malignant sub-volumes of the prostate and textures obtained from mpMR images. The performance of tumor prediction was investigated based on the combination of histogram-based and textural parameters. Subsequently, the relative importance of mpMR images was assessed and the benefit of additional imaging analyzed. Finally, sub-structures based on the PI-RADS classification were investigated as potential regions to automatically detect maligned lesions. Twenty-five patients who received mpMRI prior to radical prostatectomy were included in the study. The imaging protocol included T2, DWI, and DCE. Delineation of tumor regions was performed based on pathological information. First and second order statistics were derived from each structure and for all image modalities. The resulting data were processed with multivariate analysis, using PCA (principal component analysis) and OPLS-DA (orthogonal partial least squares discriminant analysis) for separation of malignant and healthy tissue. PCA showed a clear difference between tumor and healthy regions in the peripheral zone for all investigated images. The predictive ability of the OPLS-DA models increased for all image modalities when first and second order statistics were combined. The predictive value reached a plateau after adding ADC and T2, and did not increase further with the addition of other image information. The present study indicates a distinct difference in the signatures between malign and benign prostate tissue. This is an absolute prerequisite for automatic tumor segmentation, but only the first step in that direction. For the specific identified signature, DCE did not add complementary information to T2 and ADC maps.

Respiration-introduced tumor location uncertainty is a challenge in the precise lung biopsy for lung lesions. Current statistical modeling approaches hardly capture the complex local respiratory motion information. In this study, we formulate a statistical respiratory motion model using biplane x-ray images to improve the accuracy of motion field estimation by efficiently preserving local motion details for specific patients. Given CT data sets of 18 healthy subjects at end-expiratory and end-inspiratory breathing phases, the respiratory motion field is constructed based on deformation vector fields which are extracted from these CT data sets, and a lung contour motion repository respiratory is generated dependent on displacements of boundary control points. By varying the sparse weight coefficients of the statistical sparse motion field presentation (SMFP) method, the newly-input motion field is approximately presented by a sparse linear combination of a subset of the motion repository. The SMFP method is employed twice in the coefficient optimization process. Finally, these non-zero coefficients are fine-tuned to maximize the similarity between the projection image of reconstructed volumetric images and the current x-ray image. We performed the proposed method for estimating respiratory motion field on ten subject datasets and compared the result with the PCA method. The maximum average target registration error of the PCA-based and the SMFP-based respiratory motion field estimation are 3.1(2.0) and 2.9(1.6) mm, respectively. The maximum average symmetric surface distance of two methods are 2.5(1.6) and 2.4(1.3) mm, respectively.

In the past, hypothetical spherical target volumes and ideally conformal dose distributions were analyzed to establish the safety of planning target volume (PTV) margins. In this work we extended these models to estimate how alternative methods of shaping dose distributions could lead to clinical improvements. Based on a spherical clinical target volume (CTV) and Gaussian distributions of systematic and random geometrical uncertainties, idealized 3D dose distributions were optimized to exhibit specific stochastic properties. A nearby spherical organ at risk (OAR) was introduced to explore the benefit of non-spherical dose distributions.

Optimizing for the same minimum dose safety criterion as implied by the generally accepted use of a PTV, the extent of the high dose region in one direction could be reduced by half provided that dose in other directions is sufficiently compensated. Further reduction of this unilateral dosimetric margin decreased the target dose confidence, however the actual minimum CTV dose at 90% confidence typically exceeded the minimum PTV dose by 20% of prescription.

Incorporation of smooth dose-effect relations within the optimization led to more concentrated dose distributions compared to the use of a PTV, with an improved balance between the probability of tumor cell kill and the risk of geometrical miss, and lower dose to surrounding tissues. Tumor control rate improvements in excess of 20% were found to be common for equal integral dose, while at the same time evading a nearby OAR. These results were robust against uncertainties in dose-effect relations and target heterogeneity, and did not depend on 'shoulders' or 'horns' in the dose distributions.

A depth encoding PET detector module using semi-monolithic scintillation crystal single-ended readout by a SiPM array was built and its performance was measured. The semi-monolithic scintillator detector consists of 11 polished LYSO slices measuring 1  ×  11.6  ×  10 mm³. The slices are glued together with enhanced specular reflector (ESR) in between and outside of the slices. The bottom surface of the slices is coupled to a 4  ×  4 SiPM array with a 1 mm light guide and silicon grease between them. No reflector is used on the top surface and two sides of the slices to reduce the scintillation photon reflection. The signals of the 4  ×  4 SiPM array are grouped along rows and columns separately into eight signals. Four SiPM column signals are used to identify the slices according to the center of the gravity of the scintillation photon distribution in the pixelated direction. Four SiPM row signals are used to estimate the y (monolithic direction) and z (depth of interaction) positions according to the center of the gravity and the width of the scintillation photon distribution in the monolithic direction, respectively. The detector was measured with 1 mm sampling interval in both the y and z directions with electronic collimation by using a 0.25 mm diameter ²²Na point source and a 1  ×  1  ×  20 mm³ LYSO crystal detector. An average slice based energy resolution of 14.9% was obtained. All slices of 1 mm thick were clearly resolved and a detector with even thinner slices could be used. The y positions calculated with the center of gravity method are different for interactions happening at the same y, but different z positions due to depth dependent edge effects. The least-square minimization and the maximum likelihood positioning algorithms were developed and both methods improved the spatial resolution at the edges of the detector as compared with the center of gravity method. A mean absolute error (MAE) which is defined as the probability-weighted mean of the absolute value of the positioning error is used to evaluate the spatial resolution. An average MAE spatial resolution of ~1.15 mm was obtained in both y and z directions without rejection of the multiple scattering events. The average MAE spatial resolution was ~0.7 mm in both y and z directions after the multiple scattering events were rejected. The timing resolution of the detector is 575 ps. In the next step, long rectangle detector will be built to reduce edge effects and improve the spatial resolution of the semi-monolithic detector. Thick detector up to 20 mm will be explored and the positioning algorithms will be further optimized.

In this paper, a novel source model based on a magnetic vector potential for the assessment of induced electric field strength in a human body exposed to the low-frequency (LF) magnetic field of an electrical appliance is presented. The construction of the vector potential model requires only a single-component magnetic field to be measured close to the appliance under test, hence relieving considerable practical measurement effort—the radial basis functions (RBFs) are adopted for the interpolation of discrete measurements; the magnetic vector potential model can then be directly constructed by summing a set of simple algebraic functions of RBF parameters. The vector potentials are then incorporated into numerical calculations as the equivalent source for evaluations of the induced electric field in the human body model. The accuracy and effectiveness of the proposed model are demonstrated by comparing the induced electric field in a human model to that of the full-wave simulation. This study presents a simple and effective approach for modelling the LF magnetic source. The result of this study could simplify the compliance test procedure for assessing an electrical appliance regarding LF magnetic exposure.