The role of ultrafast sequences MRI in the diagnostic evaluation and in the therapeutic option in female stress urinary incontinence.

Abstract 58

Single Event Upset tests and failure rate estimation for a front-end ASIC adopted in high-flux-particle therapy applications

none8A 64 channels Application Specific Integrated Circuit, named TERA09, designed in a 0.35 m technology for particle therapy applications, has been characterized for Single Event Upset probability. TERA09 is a current-to-frequency converter that offers a wide input range, extending from few nA to hundreds of A, with linearity deviations in the order of a few percent. This device operates as front-end readout electronics for parallel plate ionization chambers adopted in clinical applications. This chip is going to be located beside the monitor chamber, thus not directly exposed to the particle beam. For this reason, no radiation hardening techniques were adopted during the microelectronics design. The intent of the test reported in this paper is to predict the TERA09 upset rate probability in a real application scenario. Due to the fact that TERA09 has an extended digital area with registers and counters, it is interesting to estimate the effect of the secondary neutron field produced during the treatment. The radiation damage test took place at the SIRAD facility of the Italian National Institute for Nuclear Physics in Padova, Italy. The SIRAD facility allows to study the CMOS upset rate as a function of the energy deposited during irradiation. By irradiating the chip with ions of different Linear Energy Transfer, it is possible to calculate the single event effect cross-section as a function of the deposited energy. It resulted that the minimum deposited energy in a CMOS silicon sensitive volume of , responsible for a Single Event Upset probability higher than zero, is 690 keV. In the last part of the paper, we calculated the expected upset probability in a typical clinical environment, knowing the fluence of secondary, backward-emitted neutrons. Considering as an example a treatment room located at the CNAO particle therapy center in Pavia, the expected upset rate for TERA09 is events/year. Using a redundant and independent monitor chamber, the upset probability expected during one detector readout is lower than , as explained in the document.noneFausti, F.; Mazza, G.; Giordanengo, S.; Hammad Ali, O.; Manganaro, L.; Monaco, V.; Sacchi, R.; Cirio, R.Fausti, F.; Mazza, G.; Giordanengo, S.; Hammad Ali, O.; Manganaro, L.; Monaco, V.; Sacchi, R.; Cirio, R

Apparent Diffusion Coefficient Assessment of Brain Development in Normal Fetuses and Ventriculomegaly

Diffusion neuro-MRI has benefited significantly from sophisticated pre-processing procedures aimed at improving image quality and diagnostic. In this work, diffusion-weighted imaging (DWI) was used with artifact correction and the apparent diffusion coefficient (ADC) was quantified to investigate fetal brain development. The DWI protocol was designed in order to limit the acquisition time and to estimate ADC without perfusion bias. The ADC in normal fetal brains was compared to cases with isolated ventriculomegaly (VM), a common fetal disease whose DWI studies are still scarce. DWI was performed in 58 singleton fetuses (Gestational age (GA) range: 19–38w) at 1.5T. In 31 cases, VM was diagnosed on ultrasound. DW-Spin Echo EPI with b-values = 50, 200, 700 s/mm2 along three orthogonal axes was used. All images were corrected for noise, Gibbs-ringing, and motion artifacts. The signal-to-noise ratio (SNR) was calculated and the ADC was measured with a linear least-squared algorithm. A multi-way ANOVA was used to evaluate differences in ADC between normal and VM cases and between second and third trimester in different brain regions. Correlation between ADC and GA was assessed with linear and quadratic regression analysis. Noise and artifact correction considerably increased SNR and the goodness-of-fit. ADC measurements were significantly different between second and third trimester in centrum semiovale, frontal white matter, thalamus, cerebellum and pons of both normal and VM brains (p ≤ 0.03). ADC values were significantly different between normal and VM in centrum semiovale and frontal white matter (p ≤ 0.02). ADC values in centrum semiovale, thalamus, cerebellum and pons linearly decreased with GA both in normal and VM brains, while a quadratic relation with GA was found in basal ganglia and occipital white matter of normal brains and in frontal white matter of VM (p ≤ 0.02). ADC values in all fetal brain regions were lower than those reported in literature where DWI with b = 0 was performed. Conversely, they were in agreement with the results of other authors who measured perfusion and diffusion contributions separately. By optimizing our DWI protocol we achieved an unbiased quantification of brain ADC in reasonable scan time. Our findings suggested that ADC can be a useful biomarker of brain abnormalities associated with VM

Apparent diffusion coefficient assessment of brain development in normal fetuses and ventriculomegaly

Diffusion neuro-MRI has benefited significantly from sophisticated pre-processing procedures aimed at improving image quality and diagnostic. In this work, diffusion-weighted imaging (DWI) was used with artifact correction and the apparent diffusion coefficient (ADC) was quantified to investigate fetal brain development. The DWI protocol was designed in order to limit the acquisition time and to estimate ADC without perfusion bias. The ADC in normal fetal brains was compared to cases with isolated ventriculomegaly (VM), a common fetal disease whose DWI studies are still scarce. DWI was performed in 58 singleton fetuses (Gestational age (GA) range: 19–38w) at 1.5T. In 31 cases, VM was diagnosed on ultrasound. DW-Spin Echo EPI with b-values = 50, 200, 700 s/mm2 along three orthogonal axes was used. All images were corrected for noise, Gibbs-ringing, and motion artifacts. The signal-to-noise ratio (SNR) was calculated and the ADC was measured with a linear least-squared algorithm. A multi-way ANOVA was used to evaluate differences in ADC between normal and VM cases and between second and third trimester in different brain regions. Correlation between ADC and GA was assessed with linear and quadratic regression analysis. Noise and artifact correction considerably increased SNR and the goodness-of-fit. ADC measurements were significantly different between second and third trimester in centrum semiovale, frontal white matter, thalamus, cerebellum and pons of both normal and VM brains (p ≤ 0.03). ADC values were significantly different between normal and VM in centrum semiovale and frontal white matter (p ≤ 0.02). ADC values in centrum semiovale, thalamus, cerebellum and pons linearly decreased with GA both in normal and VM brains, while a quadratic relation with GA was found in basal ganglia and occipital white matter of normal brains and in frontal white matter of VM (p ≤ 0.02). ADC values in all fetal brain regions were lower than those reported in literature where DWI with b = 0 was performed. Conversely, they were in agreement with the results of other authors who measured perfusion and diffusion contributions separately. By optimizing our DWI protocol we achieved an unbiased quantification of brain ADC in reasonable scan time. Our findings suggested that ADC can be a useful biomarker of brain abnormalities associated with VM