FLASH radiotherapy with electrons: issues related to the production, monitoring, and dosimetric characterization of the beam

Various in vivo experimental works carried out on different animals and organs have shown that it is possible to reduce the damage caused to healthy tissue still preserving the therapeutic efficacy on the tumor tissue, by drastically reducing the total time of dose delivery (<200 ms). This effect, called the FLASH effect, immediately attracted considerable attention within the radiotherapy community, due to the possibility of widening the therapeutic window and treating effectively tumors which appear radioresistant to conventional techniques. Despite the experimental evidence, the radiobiological mechanisms underlying the FLASH effect and the beam parameters contributing to its optimization are not yet known in details. In order to fully understand the FLASH effect, it might be worthy to investigate some alternatives which can further improve the tools adopted so far, in terms of both linac technology and dosimetric systems. This work investigates the problems and solutions concerning the realization of an electron accelerator dedicated to FLASH therapy and optimized for in vivo experiments. Moreover, the work discusses the saturation problems of the most common radiotherapy dosimeters when used in the very high dose-per-pulse FLASH conditions and provides some preliminary experimental data on their behavior

Tecniche di segmentazione applicate a immagini biomediche

Nel presente lavoro di tesi sono state analizzate e sviluppate alcune tecniche di segmentazione di immagini digitali, che sono state poi applicate ad immagini biomediche ottenute mediante risonanza magnetica. Negli ultimi anni si è reso necessario l’utilizzo di algoritmi per elaborare ed analizzare le moltissime immagini provenienti da diverse tecniche di diagnosti- ca. In particolare, l’implementazione di algoritmi per il riconoscimento delle strutture anatomiche e di altre regioni di interesse ricopre un ruolo importan- te nell’assistere ed automatizzare alcuni dei compiti svolti dai radiologi. Si parla, quindi, di algoritmi per la segmentazione di immagini, i quali vengono utilizzati in molte applicazioni biomediche quali la quantificazione dei volumi e degli spessori delle strutture di interesse, la diagnosi, la localizzazione delle patologie e lo studio di strutture anatomiche. In generale, la segmentazione rientra nell’ambito dell’elaborazione digitale delle immagini ed è il processo per cui l’immagine di partenza viene suddivisa in diverse regioni significative, i cui pixel (Picture Element) hanno una o più caratteristiche in comune (ad esempio colore, intensità, texture, ecc.). Sono moltissimi i metodi di segmentazione di immagini biomediche pre- senti in letteratura, e la scelta di quale metodo applicare risulta spesso molto complessa dal momento che nessuno di essi è adeguato per tutti i tipi di immagine e, allo stesso tempo, non tutti i metodi sono ugualmente validi su una stessa immagine. Numerosi studi sulla segmentazione di immagini biomediche fanno riferi- mento ad immagini di risonanza magnetica (Magnetic Resonance Imaging, MRI), una delle tecniche di diagnostica più utilizzate in campo medico, la quale consente di produrre immagini ad alta definizione dell’interno del corpo umano in maniera non invasiva e senza l’utilizzo di radiazioni ionizzanti. Gli scanner per MRI utilizzati attualmente in ambito clinico generano campi magnetici statici da 1.5 e 3 T, ma, a partire dai primi anni del 2000, sono disponibili, per scopi di ricerca, anche scanner con campo magnetico statico B0 ≥ 7 T; per campi di questo tipo si parla di risonanza magnetica a campo ultra-alto (Ultra High Field, UHF). L’interesse per l’UHF MRI cresce anno dopo anno grazie soprattutto ai miglioramenti in termini di rapporto segnale–rumore (Signal to Noise Ratio, SNR), rapporto contrasto–rumore (Contrast to Noise Ratio, CNR) e risoluzione spaziale che, insieme, permettono di identificare dettagli anatomici indistinguibili con i normali scanner per uso clinico. Il lavoro di tesi è stato svolto nell’ambito di una collaborazione tra diversi enti, tra cui il Dipartimento di Fisica dell’Università di Pisa, l’Istituto Nazionale di Fisica Nucleare (INFN), l’Azienda Ospedaliero–Universitaria Pisana (AOUP) e la fondazione IMAGO 7 di Calambrone (PI), primo centro di ricerca italiano per risonanza magnetica a campo ultra-alto anche per studi in–vivo sull’uomo. Grazie a questa collaborazione è stato possibile effettuare due diversi studi sulla segmentazione di immagini di risonanza magnetica. In particolare, una prima parte del lavoro riguarda l’analisi di immagini UHF MRI del ginocchio, mentre la seconda parte del lavoro riguarda l’analisi di immagini UHF MRI del cervello. Entrando nello specifico, viene presentato lo studio relativo alla segmenta- zione delle cartilagini del ginocchio. Infatti, l’UHF MRI applicata al ginocchio offre notevoli miglioramenti nella visualizzazione delle patologie della car- tilagine e nella segmentazione della stessa, pertanto buona parte di questo lavoro di tesi è centrato sullo sviluppo di una procedura di segmentazio- ne semi–automatica delle tre cartilagini del ginocchio (femorale, tibiale e rotulea). Di grande interesse nell’ambito dell’UHF MRI è anche l’analisi delle immagini del cervello. L’alta risoluzione che si ottiene con scanner a 7 T permette di individuare anche le sottostrutture dell’ippocampo, aprendo così la strada verso diverse applicazioni cliniche nello studio delle malattie neurodegenerative. Sono state, quindi, analizzate ed applicate alle immagini UHF MRI disponibili diverse tecniche di segmentazione delle sottostrutture ippocampali utilizzando due software dedicati (ASHS e FreeSurfer). Per quanto riguarda i risultati, il lavoro effettuato sulle cartilagini del ginocchio ha permesso di effettuare analisi volumetriche delle cartilagini di soggetti sani e di soggetti affetti da artrosi (malattia articolare conseguente ad un deterioramento della cartilagine). È stato effettuato un confronto con una segmentazione manuale delle tre cartilagini effettuata da un radiologo esperto, mediante la quale è stato possibile stimare l’accuratezza dei risultati ottenuti con il metodo semi–automatico, e lo stesso radiologo ha effettuato una valutazione qualitativa di tutte le segmentazioni. Per la valutazione dei risultati sono stati inoltre utilizzati il coefficiente di similarità Dice (Dice similarity coefficient, DSC), la sensibilità (frazione dei pixel correttamente identificati come appartenenti all’oggetto da segmentare) e la specificità (frazione dei pixel correttamente identificati come appartenenti allo sfondo). Per quanto riguarda lo studio delle immagini del cervello, invece, non sono stati ottenuti dei risultati quantitativi poiché le immagini disponibili non soddisfano adeguatamente le richieste dei software utilizzati. Sulla base delle analisi effettuate sono stati quindi identificati i parametri di acquisizione da utilizzare per eventuali sviluppi futuri

Radiomics in quantitative cardiac magnetic resonance T1 and T2 mapping: repeatability of myocardial features and their sensitivity to image preprocessing

Radiomics is a novel tool that involves the extraction of a large number of morphological and textural quantitative characteristics (i.e., radiomic features) from digital medical images. This quantitative information could then be used for diagnostic, prognostic or predictive purposes exploiting statistical or machine learning methods. Recently, in cardiac magnetic resonance (CMR) imaging, T1 and T2 mapping techniques have enabled a quantitative assessment of myocardial tissue characteristics. Specifically, these techniques can be employed to evaluate myocardial diseases that alter the composition of myocardial tissue, and possibly its T1 and T2 relaxation times. Hence, T1 and T2 maps might be particularly suitable for radiomic analysis. Despite the increasing interest in radiomics, its proper application deserves some caution. In this regard, a preliminary assessment of the repeatability of the radiomic features estimation is recommended. Moreover, each step of the radiomic workflow (i.e., image acquisition and reconstruction, image segmentation, image preprocessing, image filtering, and feature extraction) has the potential to influence features estimation. Hence, researchers involved in this field are focusing on the standardization of the radiomic workflow, in order to possibly use radiomic features as imaging biomarkers (IB). Given that CMR radiomics is in its infancy, there is a need to thoroughly evaluate these aspects for each specific application. Accordingly, the purpose of this work was to investigate, for the first time, the repeatability of myocardial radiomic features from quantitative CMR T1 and T2 mapping along with their sensitivity to image preprocessing and image filtering

A Voxel-Based Assessment of Noise Properties in Computed Tomography Imaging with the ASiR-V and ASiR Iterative Reconstruction Algorithms

Given the inherent characteristics of nonlinearity and nonstationarity of iterative reconstruction algorithms in computed tomography (CT) imaging, this study aimed to perform, for the first time, a voxel-based characterization of noise properties in CT imaging with the ASiR-V and ASiR algorithms as compared with conventional filtered back projection (FBP). Multiple repeated scans of the Catphan-504 phantom were carried out. CT images were reconstructed using FBP and ASiR/ASiR-V with different blending levels of reconstruction (20%, 40%, 60%, 80%, 100%). Noise maps and their nonuniformity index (NUI) were obtained according to the approach proposed by the report of AAPM TG-233. For the homogeneous CTP486 module, ASiR-V/ASiR allowed a noise reduction of up to 63.7%/52.9% relative to FBP. While the noise reduction values of ASiR-V-/ASiR-reconstructed images ranged up to 33.8%/39.9% and 31.2%/35.5% for air and Teflon contrast objects, respectively, these values were approximately 60%/50% for other contrast objects (PMP, LDPE, polystyrene, acrylic, Delrin). Moreover, for all contrast objects but air and Teflon, ASiR-V showed a greater noise reduction potential than ASiR when the blending level was ≥40%. While noise maps of the homogenous CTP486 module showed only a slight spatial variation of noise (NUI < 5.2%) for all reconstruction algorithms, the NUI values of iterative-reconstructed images of the nonhomogeneous CTP404 module increased nonlinearly with blending level and were 19%/15% and 6.7% for pure ASiR-V/ASiR and FBP, respectively. Overall, these results confirm the potential of ASiR-V and ASiR in reducing noise as compared with conventional FBP, suggesting, however, that the use of pure ASiR-V or ASiR might be suboptimal for specific clinical applications

A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre-A phantom study.

Nowadays, given the technological advance in CT imaging and increasing heterogeneity in characteristics of CT scanners, a number of CT scanners with different manufacturers/technologies are often installed in a hospital centre and used by various departments. In this phantom study, a comprehensive assessment of image quality of 5 scanners (from 3 manufacturers and with different models) for head CT imaging, as clinically used at a single hospital centre, was hence carried out. Helical and/or sequential acquisitions of the Catphan-504 phantom were performed, using the scanning protocols (CTDIvol range: 54.7-57.5 mGy) employed by the staff of various Radiology/Neuroradiology departments of our institution for routine head examinations. CT image quality for each scanner/acquisition protocol was assessed through noise level, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD) and non-uniformity index analyses. Noise values ranged from 3.5 HU to 5.7 HU across scanners/acquisition protocols. NPS curves differed in terms of peak position (range: 0.21-0.30 mm-1). A substantial variation of CNR values with scanner/acquisition protocol was observed for different contrast inserts. The coefficient of variation (standard deviation divided by mean value) of CNR values across scanners/acquisition protocols was 18.3%, 31.4%, 34.2%, 30.4% and 30% for teflon, delrin, LDPE, polystyrene and acrylic insert, respectively. An appreciable difference in MTF curves across scanners/acquisition protocols was revealed, with a coefficient of variation of f50%/f10% of MTF curves across scanners/acquisition protocols of 10.1%/7.4%. A relevant difference in LCD performance of different scanners/acquisition protocols was found. The range of contrast threshold for a typical object size of 3 mm was 3.7-5.8 HU. Moreover, appreciable differences in terms of NUI values (range: 4.1%-8.3%) were found. The analysis of several quality indices showed a non-negligible variability in head CT imaging capabilities across different scanners/acquisition protocols. This highlights the importance of a physical in-depth characterization of image quality for each CT scanner as clinically used, in order to optimize CT imaging procedures

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. Results: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57–0.60; sensitivity = 0.56, 95% CI 0.54–0.58; specificity = 0.61, 95% CI 0.59–0.63; accuracy = 0.58, 95% CI 0.57–0.59). Conclusions: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. Results: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57–0.60; sensitivity = 0.56, 95% CI 0.54–0.58; specificity = 0.61, 95% CI 0.59–0.63; accuracy = 0.58, 95% CI 0.57–0.59). Conclusions: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications

Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping

Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing—in terms of voxel size resampling, discretization, and filtering—on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson’s or Spearman’s correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson’s and Spearman’s dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features’ stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps