2 resultados para QC sets max
em DigitalCommons@The Texas Medical Center
Resumo:
The motion of lung tumors during respiration makes the accurate delivery of radiation therapy to the thorax difficult because it increases the uncertainty of target position. The adoption of four-dimensional computed tomography (4D-CT) has allowed us to determine how a tumor moves with respiration for each individual patient. Using information acquired during a 4D-CT scan, we can define the target, visualize motion, and calculate dose during the planning phase of the radiotherapy process. One image data set that can be created from the 4D-CT acquisition is the maximum-intensity projection (MIP). The MIP can be used as a starting point to define the volume that encompasses the motion envelope of the moving gross target volume (GTV). Because of the close relationship that exists between the MIP and the final target volume, we investigated four MIP data sets created with different methodologies (3 using various 4D-CT sorting implementations, and one using all available cine CT images) to compare target delineation. It has been observed that changing the 4D-CT sorting method will lead to the selection of a different collection of images; however, the clinical implications of changing the constituent images on the resultant MIP data set are not clear. There has not been a comprehensive study that compares target delineation based on different 4D-CT sorting methodologies in a patient population. We selected a collection of patients who had previously undergone thoracic 4D-CT scans at our institution, and who had lung tumors that moved at least 1 cm. We then generated the four MIP data sets and automatically contoured the target volumes. In doing so, we identified cases in which the MIP generated from a 4D-CT sorting process under-represented the motion envelope of the target volume by more than 10% than when measured on the MIP generated from all of the cine CT images. The 4D-CT methods suffered from duplicate image selection and might not choose maximum extent images. Based on our results, we suggest utilization of a MIP generated from the full cine CT data set to ensure a representative inclusive tumor extent, and to avoid geometric miss.
Resumo:
Olfactory glomeruli are the loci where the first odor-representation map emerges. The glomerular layer comprises exquisite local synaptic circuits for the processing of olfactory coding patterns immediately after their emergence. To understand how an odor map is transferred from afferent terminals to postsynaptic dendrites, it is essential to directly monitor the odor-evoked glomerular postsynaptic activity patterns. Here we report the use of a transgenic mouse expressing a Ca(2+)-sensitive green fluorescence protein (GCaMP2) under a Kv3.1 potassium-channel promoter. Immunostaining revealed that GCaMP2 was specifically expressed in mitral and tufted cells and a subpopulation of juxtaglomerular cells but not in olfactory nerve terminals. Both in vitro and in vivo imaging combined with glutamate receptor pharmacology confirmed that odor maps reported by GCaMP2 were of a postsynaptic origin. These mice thus provided an unprecedented opportunity to analyze the spatial activity pattern reflecting purely postsynaptic olfactory codes. The odor-evoked GCaMP2 signal had both focal and diffuse spatial components. The focalized hot spots corresponded to individually activated glomeruli. In GCaMP2-reported postsynaptic odor maps, different odorants activated distinct but overlapping sets of glomeruli. Increasing odor concentration increased both individual glomerular response amplitude and the total number of activated glomeruli. Furthermore, the GCaMP2 response displayed a fast time course that enabled us to analyze the temporal dynamics of odor maps over consecutive sniff cycles. In summary, with cell-specific targeting of a genetically encoded Ca(2+) indicator, we have successfully isolated and characterized an intermediate level of odor representation between olfactory nerve input and principal mitral/tufted cell output.