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Irradiations at Ultra-High Dose Rate (UHDR) regimes, exceeding 40 Gy/s in single fractions lasting less than 200 ms, have shown an equivalent antitumor effect compared to conventional radiotherapy with reduced harm to normal tissues. This work details the hardware and software modifications implemented to deliver 10 MeV UHDR electron beams with a linear accelerator Elekta SL 18 MV and the beam characteristics obtained. GafChromic EBT XD films and an Advanced Markus chamber were used for dosimetry characterization, while a silicon sensor assessed the machine's beam pulses stability and repeatability. The dose per pulse, average dose rate and instantaneous dose rate in the pulse were evaluated for four experimental settings, varying the source-to-surface distance and the beam collimation, i.e., with and without the use of a cylindrical applicator. The results showed a dose per pulse from 0.6 Gy to a few tens of Gy and an average dose rate up to 300 Gy/s. The obtained results demonstrate the possibility to perform in vitro radiobiology experiments and test new technologies for beam monitoring and dosimetry at the upgraded LINAC, thus contributing to the electron UHDR research field. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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).