Single-Ion Counting with an Ultra-Thin-Membrane Silicon Carbide Sensor.

In recent times, ion implantation has received increasing interest for novel applications related to deterministic material doping on the nanoscale, primarily for the fabrication of solid-state quantum devices. For such applications, precise information concerning the number of implanted ions and their final position within the implanted sample is crucial. In this work, we present an innovative method for the detection of single ions of MeV energy by using a sub-micrometer ultra-thin silicon carbide sensor operated as an in-beam counter of transmitted ions. The SiC sensor signals, when compared to a Passivated Implanted Planar Silicon detector signal, exhibited a 96.5% ion-detection confidence, demonstrating that the membrane sensors can be utilized for high-fidelity ion counting. Furthermore, we assessed the angular straggling of transmitted ions due to the interaction with the SiC sensor, employing the scanning knife-edge method of a focused ion microbeam. The lateral dimension of the ion beam with and without the membrane sensor was compared to the SRIM calculations. The results were used to discuss the potential of such experimental geometry in deterministic ion-implantation schemes as well as other applications.

Performance of LGAD strip detectors for particle counting of therapeutic proton beams.

Objective. The performance of silicon detectors with moderate internal gain, named low-gain avalanche diodes (LGADs), was studied to investigate their capability to discriminate and count single beam particles at high fluxes, in view of future applications for beam characterization and on-line beam monitoring in proton therapy.Approach. Dedicated LGAD detectors with an active thickness of 55µm and segmented in 2 mm2strips were characterized at two Italian proton-therapy facilities, CNAO in Pavia and the Proton Therapy Center of Trento, with proton beams provided by a synchrotron and a cyclotron, respectively. Signals from single beam particles were discriminated against a threshold and counted. The number of proton pulses for fixed energies and different particle fluxes was compared with the charge collected by a compact ionization chamber, to infer the input particle rates.Main results. The counting inefficiency due to the overlap of nearby signals was less than 1% up to particle rates in one strip of 1 MHz, corresponding to a mean fluence rate on the strip of about 5 × 107p/(cm2·s). Count-loss correction algorithms based on the logic combination of signals from two neighboring strips allow to extend the maximum counting rate by one order of magnitude. The same algorithms give additional information on the fine time structure of the beam.Significance. The direct counting of the number of beam protons with segmented silicon detectors allows to overcome some limitations of gas detectors typically employed for beam characterization and beam monitoring in particle therapy, providing faster response times, higher sensitivity, and independence of the counts from the particle energy.

Calibration method and performance of a time-of-flight detector to measure absolute beam energy in proton therapy.

BACKGROUND: The beam energy is one of the most significant parameters in particle therapy since it is directly correlated to the particles' penetration depth inside the patient. Nowadays, the range accuracy is guaranteed by offline routine quality control checks mainly performed with water phantoms, 2D detectors with PMMA wedges, or multi-layer ionization chambers. The latter feature low sensitivity, slow collection time, and response dependent on external parameters, which represent limiting factors for the quality controls of beams delivered with fast energy switching modalities, as foreseen in future treatments. In this context, a device based on solid-state detectors technology, able to perform a direct and absolute beam energy measurement, is proposed as a viable alternative for quality assurance measurements and beam commissioning, paving the way for online range monitoring and treatment verification. PURPOSE: This work follows the proof of concept of an energy monitoring system for clinical proton beams, based on Ultra Fast Silicon Detectors (featuring tenths of ps time resolution in 50 µm active thickness, and single particle detection capability) and time-of-flight techniques. An upgrade of such a system is presented here, together with the description of a dedicated self-calibration method, proving that this second prototype is able to assess the mean particles energy of a monoenergetic beam without any constraint on the beam temporal structure, neither any a priori knowledge of the beam energy for the calibration of the system. METHODS: A new detector geometry, consisting of sensors segmented in strips, has been designed and implemented in order to enhance the statistics of coincident protons, thus improving the accuracy of the measured time differences. The prototype was tested on the cyclotron proton beam of the Trento Protontherapy Center (TPC). In addition, a dedicated self-calibration method, exploiting the measurement of monoenergetic beams crossing the two telescope sensors for different flight distances, was introduced to remove the systematic uncertainties independently from any external reference. RESULTS: The novel calibration strategy was applied to the experimental data collected at TPC (Trento) and CNAO (Pavia). Deviations between measured and reference beam energies in the order of a few hundreds of keV with a maximum uncertainty of 0.5 MeV were found, in compliance with the clinically required water range accuracy of 1 mm. CONCLUSIONS: The presented version of the telescope system, minimally perturbative of the beam, relies on a few seconds of acquisition time to achieve the required clinical accuracy and therefore represents a feasible solution for beam commission, quality assurance checks, and online beam energy monitoring.

Radiation Hardness Study of Silicon Carbide Sensors under High-Temperature Proton Beam Irradiations.

Silicon carbide (SiC), thanks to its material properties similar to diamond and its industrial maturity close to silicon, represents an ideal candidate for several harsh-environment sensing applications, where sensors must withstand high particle irradiation and/or high operational temperatures. In this study, to explore the radiation tolerance of SiC sensors to multiple damaging processes, both at room and high temperature, we used the Ion Microprobe Chamber installed at the Ruder Boskovic Institute (Zagreb, Croatia), which made it possible to expose small areas within the same device to different ion beams, thus evaluating and comparing effects within a single device. The sensors tested, developed jointly by STLab and SenSiC, are PIN diodes with ultrathin free-standing membranes, realized by means of a recently developed doping-selective electrochemical etching. In this work, we report on the changes of the charge transport properties, specifically in terms of the charge collection efficiency (CCE), with respect to multiple localized proton irradiations, performed at both room temperature (RT) and 500 °C.

Accuracy assessment of the CNAO dose delivery system in the initial period of clinical activity and impact of later improvements on delivered dose distributions.

PURPOSE: A retrospective analysis of the dose delivery system (DDS) performances of the initial clinical operation at CNAO (Centro Nazionale di Adroterapia Oncologica) is reported, and compared with the dose delivery accuracy following the implementation of a position feedback control. METHODS: Log files and raw data of the DDS were analyzed for every field of patients treated with protons and carbon ions between January 2012 and April 2013 (~3800 fields). To investigate the DDS accuracy, the spot positions and the number of particles per spot measured by the DDS and prescribed by the treatment planning system were compared for each field. The impact of deviations on dose distributions was studied by comparing, through the gamma-index method, 2 three-dimensional (3D) physical dose maps (one for prescribed, one for measured data), generated by a validated dose computation software. The maximum gamma and the percentage of points with gamma ≤ 1 (passing volume) were studied as a function of the treatment day, and correlated with the deviations from the prescription in the measured number of particles and spot positions. Finally, delivered dose distributions of same treatment plans were compared before and after the implementation of a feedback algorithm for the correction of small position deviations, to study the effect on the delivery quality. A double comparison of prescribed and measured 3D maps, before and after feedback implementation, is reported and studied for a representative treatment delivered in 2012, redelivered on a polymethyl methacrylate (PMMA) block in 2018. RESULTS: Systematic deviations of spot positions, mainly due to beam lateral offsets, were always found within 1.5 mm, with the exception of the initial clinical period. The number of particles was very stable, as possible deviations are exclusively related to the quantization error in the conversion from monitor counts to particles. For the chosen representative patient treatment, the gamma-index evaluation of prescribed and measured dose maps, before and after feedback implementation, showed a higher variability of maximum gamma for the 2012 irradiation, with respect to the reirradiation of 2018. However, the 2012 passing volume is >99.8% for the sum of all fields, which is comparable to the value of 2018, with the exception of one day with 98.2% passing volume, probably related to an instability of the accelerating system. CONCLUSIONS: A detailed retrospective analysis of the DDS performances in the initial period of CNAO clinical activity is reported. The spot position deviations are referable to beam lateral offset fluctuations, while almost no deviation was found in the number of particles. The impact of deviations on dose distributions showed that the position feedback implementation and the increased beam control capability acquired after the first years of clinical experience led to an evident improvement in the DDS stability, evaluated in terms of gamma-index as a measure of the impact on dose distributions. However, the clinical effect of the maximum gamma variability found in the 2012 representative irradiation is mitigated by averaging along the number of fractions, and the high percentage of passing volumes confirmed the accuracy of the delivery even before the feedback implementation.

A Monte Carlo approach to the microdosimetric kinetic model to account for dose rate time structure effects in ion beam therapy with application in treatment planning simulations.

PURPOSE: Advanced ion beam therapeutic techniques, such as hypofractionation, respiratory gating, or laser-based pulsed beams, have dose rate time structures which are substantially different from those found in conventional approaches. The biological impact of the time structure is mediated through the ß parameter in the linear quadratic (LQ) model. The aim of this study was to assess the impact of changes in the value of the ß parameter on the treatment outcomes, also accounting for noninstantaneous intrafraction dose delivery or fractionation and comparing the effects of using different primary ions. METHODS: An original formulation of the microdosimetric kinetic model (MKM) is used (named MCt-MKM), in which a Monte Carlo (MC) approach was introduced to account for the stochastic spatio-temporal correlations characteristic of the irradiations and the cellular repair kinetics. A modified version of the kinetic equations, validated on experimental cell survival in vitro data, was also introduced. The model, trained on the HSG cells, was used to evaluate the relative biological effectiveness (RBE) for treatments with acute and protracted fractions. Exemplary cases of prostate cancer irradiated with different ion beams were evaluated to assess the impact of the temporal effects. RESULTS: The LQ parameters for a range of cell lines (V79, HSG, and T1) and ion species (H, He, C, and Ne) were evaluated and compared with the experimental data available in the literature, with good results. Notably, in contrast to the original MKM formulation, the MCt-MKM explicitly predicts an ion and LET-dependent ß compatible with observations. The data from a split-dose experiment were used to experimentally determine the value of the parameter related to the cellular repair kinetics. Concerning the clinical case considered, an RBE decrease was observed, depending on the dose, ion, and LET, exceeding up to 3% of the acute value in the case of a protraction in the delivery of 10 min. The intercomparison between different ions shows that the clinical optimality is strongly dependent on a complex interplay between the different physical and biological quantities considered. CONCLUSIONS: The present study provides a framework for exploiting the temporal effects of dose delivery. The results show the possibility of optimizing the treatment outcomes accounting for the correlation between the specific dose rate time structure and the spatial characteristic of the LET distribution, depending on the ion type used.

Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard.

OBJECTIVE: To evaluate the sensitivity of multiparametric magnetic resonance imaging (mp-MRI) for detecting prostate cancer foci, including the largest (index) lesions. PATIENTS AND METHODS: In all, 115 patients with biopsy confirmed prostate cancer underwent mp-MRI before radical prostatectomy. A single expert radiologist recorded all prostate cancer foci including the index lesion 'blinded' to the pathologist's biopsy report. Stained whole-mount histological sections were used as the reference standard. All lesions were contoured by an experienced uropathologist who assessed their volume and pathological Gleason score. All lesions with a volume of >0.5 mL and/or pathological Gleason score of >6 were defined as clinically significant prostate cancer. Multivariate analysis was used to ascertain the characteristics of lesions identified by MRI. RESULTS: In all, 104 of 115 index lesions were correctly diagnosed by mp-MRI (sensitivity 90.4%; 95% confidence interval [CI] 83.5-95.1%), including 98/105 clinically significant index lesions (93.3%; 95% CI 86.8-97.3%), among which three of three lesions had a volume of <0.5 mL and Gleason score of >6. Overall, mp-MRI detected 131/206 lesions including 13 of 68 'insignificant' prostate cancers. The multivariate logistic regression modelling showed that pathological Gleason score (odds ratio [OR] 11.7, 95% CI 2.3-59.8; P = 0.003) and lesion volume (OR 4.24, 95% CI 1.3-14.7; P = 0.022) were independently associated with the detection of index lesions at MRI. CONCLUSIONS: This study shows that mp-MRI has a high sensitivity for detecting clinically significant prostate cancer index lesions, while having disappointing results for the detection of small-volume, low Gleason score prostate cancer foci. Thus, mp-MRI could be used to stratify patients according to risk, allowing better treatment selection.

A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging.

Multiparametric (mp)-Magnetic Resonance Imaging (MRI) is emerging as a powerful test to diagnose and stage prostate cancer (PCa). However, its interpretation is a time consuming and complex feat requiring dedicated radiologists. Computer-aided diagnosis (CAD) tools could allow better integration of data deriving from the different MRI sequences in order to obtain accurate, reproducible, non-operator dependent information useful to identify and stage PCa. In this paper, we present a fully automatic CAD system conceived as a 2-stage process. First, a malignancy probability map for all voxels within the prostate is created. Then, a candidate segmentation step is performed to highlight suspected areas, thus evaluating both the sensitivity and the number of false positive (FP) regions detected by the system. Training and testing of the CAD scheme is performed using whole-mount histological sections as the reference standard. On a cohort of 56 patients (i.e. 65 lesions) the area under the ROC curve obtained during the voxel-wise step was 0.91, while, in the second step, a per-patient sensitivity of 97% was reached, with a median number of FP equal to 3 in the whole prostate. The system here proposed could be potentially used as first or second reader to manage patients suspected to have PCa, thus reducing both the radiologist's reporting time and the inter-reader variability. As an innovative setup, it could also be used to help the radiologist in setting the MRI-guided biopsy target.

A fully automatic multiscale 3-dimensional Hessian-based algorithm for vessel detection in breast DCE-MRI.

OBJECTIVES: The objectives of this study were to develop a fully automatic method for detecting blood vessels in dynamic contrast-enhanced magnetic resonance imaging of the breast on the basis of a multiscale 3-dimensional Hessian-based algorithm and to evaluate the improvement in reducing the number of vessel voxels incorrectly classified as parenchymal lesions by a computer-aided diagnosis (CAD) system. MATERIALS AND METHODS: The algorithm has been conceived to work on images obtained with different sequences, different acquisition parameters, such as the use of fat-saturation, and different contrast agents. The analysis was performed on 28 dynamic contrast-enhanced magnetic resonance imaging examinations, with 39 malignant (28 principal and 11 satellite) and 8 benign lesions, acquired at 2 centers using 2 different 1.5-T magnetic resonance scanners, radiofrequency coils, and contrast agents (14 studies from group A and 14 studies from group B). The method consists of 2 main steps: (a) the detection of linear structures on 3-dimensional images, with a multiscale analysis based on the second-order image derivatives and (b) the exclusion of non-vessel enhancements based on their morphological properties through the evaluation of the covariance matrix eigenvalues. To evaluate the algorithm performances, the identified vessels were converted into a 2-dimensional vasculature skeleton and then compared with manual tracking performed by an expert radiologist. When assessing the outcome of the algorithm performances in identifying vascular structures, the following terms must be considered: the correct-detection rate refers to pixels identified by both the algorithm and the radiologist, the missed-detection rate refers to pixels detected only by the radiologist, and the incorrect-detection rate refers to pixels detected only by the algorithm. The Wilcoxon rank sum test was used to assess differences between the performances of the 2 subgroups of images obtained from the different scanners. RESULTS: For the testing set, which is composed of 28 patients from 2 different clinical centers, the median correct-detection rate was 89.1%, the median missed-detection rate was 10.9%, and the median incorrect-detection rate was 27.1%. The difference between group A and group B was not significant (P > 0.25). The exclusion of vascular voxels from the lesion detection map of a CAD system leads to a reduction of 68.4% (30.0%) (mean [SD]) of the total number of false-positives because of vessels, without a significant difference between the 2 subgroups (P = 0.50). CONCLUSIONS: The system showed promising results in detecting most vessels identified by an expert radiologist on both fat-saturated and non-fat-saturated images obtained from different scanners with variable temporal and spatial resolutions and types of contrast agent. Moreover, the algorithm may reduce the labeling of vascular voxels as parenchymal lesions by a CAD system for breast magnetic resonance imaging, improving the CAD specificity and, consequently, further stimulating the use of CAD systems in clinical workflow.

Performance of a fully automatic lesion detection system for breast DCE-MRI.

PURPOSE: To describe and test a new fully automatic lesion detection system for breast DCE-MRI. MATERIALS AND METHODS: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed. RESULTS: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). CONCLUSION: The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

A fully automatic method to register the prostate gland on T2-weighted and EPI-DWI images.

Prostate adenocarcinoma (PCa) is the most frequent noncutaneous cancer among men in developed countries. Magnetic Resonance (MR) has been used to detect PCa and several clinical trials report on the accuracy of the test. Multiparametric MR imaging (mpMRI) is defined as the integration of information from different morphological and functional datasets. mpMRI could be used to increase the performances of prostate MR, therefore allowing a more accurate assessment of the tumor gland extent, while reducing reporting time and interobserver variability. The first step to perform such a multiparametric analysis is to correct for voluntary and involuntary movements during the acquisitions, as well as for image distortion in the Diffusion Weighted (DWI) images. The aim of this work is to present a fully automatic registration algorithm between T2w and DWI images, able to realign the images and to correct the distortions in the DWI. Results showed a good overlap after registration and a strong decrease of mean surface distance in both the central gland and peripheral zone. These promising results suggest that the algorithm could be integrated in a CAD system which will combine the pharmacokinetic parameters derived from DCE-MRI, T2w MRI and DWI MR to generate one comprehensive value assessing the risk of malignancy. However to perform such a multiparametric analysis, it is necessary to correct for voluntary and involuntary (breathing, heart beating) movements during the DCE-MRI acquisition, and to realign also the DCE-MRI sequence to the T2w sequence.

A fully automatic algorithm for segmentation of the breasts in DCE-MR images.

Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79 ± 0.09, recall=0.95 ± 0.02, precision=0.82 ± 0.1).