Arterial transit time (ATT), a key parameter required to calculate absolute cerebral blood flow in arterial spin labeling (ASL), is subject to much uncertainty. In this study, ASL ATTs were estimated on a per-voxel basis using data measured by both ASL and positron emission tomography in the same subjects. The mean ATT increased by 260 6 20 (standard error of the mean) ms when the imaging slab shifted downwards by 54 mm, and increased from 630 6 30 to 1220 6 30 ms for the first slice, with an increase of 610 6 20 ms over a four-slice slab when the gap between the imaging and labeling slab increased from 20 to 74 mm. When the per-slice ATTs were employed in ASL cerebral blood flow quantification and the in-slice ATT variations ignored, regional cerebral blood flow could be significantly different from the positron emission tomography measures. ATT also decreased with focal activation by the same amount for both visual and motor tasks (~80 ms). These results provide a quantitative relationship between ATT and the ASL imaging geometry and yield an assessment of the assumptions commonly used in ASL imaging. These findings should be considered in the interpretation of, and comparisons between, different ASL-based cerebral blood flow studies. The results also provide spatially specific ATT data that may aid in optimizing the ASL imaging parameters. Key words: arterial transit time; cerebral blood flow; positron emission tomography; pulsed arterial spin labeling; brain vasculature Significant efforts have been made to improve the accuracy of absolute cerebral blood flow (CBF) measurements estimated by pulsed arterial spin labeling (ASL) MRI. To improve the inversion profile and the labeling efficiency, adiabatic or hypersecant pulses and other variants have been proposed (1-4). To overcome the signal dampening due to the off-resonance effect of the inversion radiofrequency (RF) pulse, separate coils for labeling and detection have been employed (5-9). Measurement of the arterial blood proton density was suggested to minimize the nonuniformity of the brain tissue-blood partition coefficient (k) (10), and methods have been presented to reduce the sensitivity of CBF quantification to arterial transit time (ATT) effects (contamination from intravascular arterial blood water) in CBF quantification (11-13).In ASL, water in the arteries is magnetically labeled, and when it reaches the capillary bed and exchanges with the extravascular water, the labeled water induces changes in local magnetization relative to the case in which the arterial water is undisturbed. These changes are detectable by MRI and such images of the intensity changes are perfusion weighted. Absolute CBF values can be calculated based on these perfusion-weighted intensity changes. ATTs, however, are one of the key phenomena that affect the calculation of the absolute CBF from the image intensity differences. In ASL, ATT refers to the time it takes the arterial blood to travel from the labeling site to the capillaries in the tissue being imaged. By definitio...

BackgroundDue to the heterogeneity of patient’s individual respiratory motion pattern in lung stereotactic body radiotherapy (SBRT), treatment planning dose assessment using a traditional four-dimensional computed tomography (4DCT_traditional) images based on a uniform breathing curve may not represent the true treatment dose delivered to the patient. The purpose of this study was to evaluate the accumulated dose discrepancy between based on the 4DCT_traditional and true 4DCT (4DCT_true) that incorporated with the patient’s real entire breathing motion. The study also explored a novel 4D robust planning strategy to compensate for such heterogeneity respiratory motion uncertainties.MethodsSimulated and measured patient specific breathing curves were used to generate 4D targets motion CT images. Volumetric-modulated arc therapy (VMAT) was planned using two arcs. Accumulated dose was obtained by recalculating the plan dose on each individual phase image and then deformed the dose from each phase image to the reference image. The “4 D dose” (D4D) and “true dose” (Dtrue) were the accumulated dose based on the 4DCT_traditional and 4DCT_true respectively. The average worse case dose discrepancy () between D4D and Dtrue in all treatment fraction was calculated to evaluate dosimetric /planning parameters and correlate them with the heterogeneity of respiratory-induced motion patterns. A novel 4D robust optimization strategy for VMAT (4D Ro-VMAT) based on the probability density function(pdf) of breathing curve was proposed to improve the target coverage in the presence of heterogeneity respiratory motion. The data were assessed with a paired t-tests.ResultsWith increasing breathing amplitude from 5 to 20 mm, target , increased from 1.59,1.39 to 10.15%,8.66% respectively. When the standard deviation of breathing amplitude increased from 15 to 35% of the mean amplitude, , increased from 4.06,3.48 to 10.25%,6.63% respectively. The 4D Ro-VMAT plan significantly improve the target dose compared to VMAT plan.ConclusionWhen the breathing curve amplitude is more than 10 mm and standard deviation of amplitude is higher than 25% of mean amplitude, special care is needed to choose an appropriated dose accumulation approach to evaluate lung SBRT plan target coverage robustness. The proposed 4D Ro_VMAT strategy based on the pdf of patient specific breathing curve could effectively compensate such uncertainties.

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Purpose: There is an emerging interest of applying magnetic resonance imaging (MRI) to radiotherapy (RT) due to its superior soft tissue contrast for accurate target delineation as well as functional information for evaluating treatment response. MRI-based RT planning has great potential to enable dose escalation to tumors while reducing toxicities to surrounding normal tissues in RT treatments of nasopharyngeal carcinoma (NPC). Our study aims to generate synthetic CT from T2-weighted MRI using a deep learning algorithm.Methods: Thirty-three NPC patients were retrospectively selected for this study with local IRB's approval. All patients underwent clinical CT simulation and 1.5T MRI within the same week in our hospital. Prior to CT/MRI image registration, we had to normalize two different modalities to a similar intensity scale using the histogram matching method. Then CT and T2 weighted MRI were rigidly and deformably registered using intensity-based registration toolbox elastix (version 4.9). A U-net deep learning algorithm with 23 convolutional layers was developed to generate synthetic CT (sCT) using 23 NPC patients' images as the training set. The rest 10 NPC patients were used as the test set (~1/3 of all datasets). Mean absolute error (MAE) and mean error (ME) were calculated to evaluate HU differences between true CT and sCT in bone, soft tissue and overall region.Results: The proposed U-net algorithm was able to create sCT based on T2-weighted MRI in NPC patients, which took 7 s per patient on average. Compared to true CT, MAE of sCT in all tested patients was 97 ± 13 Hounsfield Unit (HU) in soft tissue, 131 ± 24 HU in overall region, and 357 ± 44 HU in bone, respectively. ME was −48 ± 10 HU in soft tissue, −6 ± 13 HU in overall region, and 247 ± 44 HU in bone, respectively. The majority soft tissue and bone region was reconstructed accurately except the interface between soft tissue and bone and some delicate structures in nasal cavity, where the inaccuracy was induced by imperfect deformable registration. One patient example was shown with almost no difference in dose distribution using true CT vs. sCT in the PTV regions in the sinus area with fine bone structures.Conclusion: Our study indicates that it is feasible to generate high quality sCT images based on T2-weighted MRI using the deep learning algorithm in patients with nasopharyngeal carcinoma, which may have great clinical potential for MRI-only treatment planning in the future.

In this study, a novel metabolomics technique based on ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry in the MS mode was used to investigate the milk metabolomics of healthy, subclinical, and clinical mastitis cows, which were classified based on somatic cell count and presentation of clinical symptoms. Meanwhile, univariate and multivariate statistical analyses were performed to identify the significant differences across the 3 groups. Compared with healthy milk samples, less glucose, d-glycerol-1-phosphate, 4-hydroxyphenyllactate, l-carnitine, sn-glycero-3-phosphocholine, citrate, and hippurate were detected in the clinical mastitic milk samples, whereas less d-glycerol-1-phosphate, benzoic acid, l-carnitine, and cis-aconitate were found in the subclinical mastitic milk samples. Meanwhile, the milk concentration of arginine and Leu-Leu increased in both the clinical and subclinical mastitis groups. Besides, less 4-hydroxyphenyllactate, cis-aconitate, lactose, and oxoglutarate were detected in the clinical than the subclinical mastitic milk samples, whereas the abundance of some oligopeptides (Leu-Ala, Phe-Pro-Ile, Asn-Arg-Ala-Ile, and Val-Phe-Val-Tyr) increased by over 7.95-fold. Our results suggest that significant variations exist across healthy and mastitis cows. The current metabolomics approach will help in better understanding the pathobiology of mastitis, although clinical validation will be required before field application.