SOMATOM On.site Kernel Concept Online Training

Continue Continue Continue Continue Continue Continue Continue Continue Continue Continue Continue SOMATOM On.site Kernel Concept Online Training Master Template HILS2218 | Effective Date: 25 Mar 2022 ? SOMATOM On.site Kernel Concept Online Training This online training will introduce you to the naming logic and details of the kernel systems implemented in SOMATOM On.site. Describe the role of kernels in CT image reconstruction Specify the kernel concept 1 2 Welcome [somatom_on-site_kc_olt_01_welcome.wav] Welcome to the SOMATOM On.site Kernel Concept Online Training. This online training will introduce you to the naming logic and details of the kernel systems implemented in SOMATOM On.site. By completing this training, you’ll achieve the two listed learning objectives. ? Navigation hints Before you start, we would like to give you a few tips on how to navigate: Table with 2 columns and 4 rows Not all pages contain audio. Some pages invite you to read for yourself. All pages show a ? button in the lower-right corner. Select the ? button to get a quick guide through the navigation elements. Select the button on the left upper corner to display or hide the menu. Enjoy the course! ? Navigation hints ? Describe the role of kernels in CT image reconstruction Role of kernels in CT image reconstruction ? Role of kernels in image reconstruction Raw Data Images Reconstruction Kernel (e.g. Hr40) Role of kernels in image reconstruction [somatom_on-site_kc_olt_04_role-of-kernels.wav] In CT data acquisition, the images are reconstructed from raw data. A convolution algorithm called kernel is used for image reconstruction. It affects the appearance of image structures by performing image processing operations such as sharpening, edge detection, and more. Different kernels have been developed for specific anatomical applications including the standard kernel for soft tissue and the bone kernel for bony structures. Bone kernels produce a sharper image with higher spatial resolution. ? Kernel definition A kernel is an image reconstruction parameter. It affects image sharpness, noise, and resolution. It applies a specific mathematical algorithm to the image elements. It digitally filters the raw data during image reconstruction. Kernel definition [somatom_on-site_kc_olt_05_kernel-definition.wav] Here is a list of some important kernel attributes: A kernel is an image reconstruction parameter. It affects image sharpness, noise, and resolution. It applies a specific mathematical algorithm to the image elements. It digitally filters the raw data during image reconstruction. ? Specify the kernel concept Kernel concept ? Kernel naming system Kernel name pattern​ ​ ​ for example: H (Head), Q (Quantitative)​ for example: r (regular), v (vascular) for example: 40​ Examples of kernel names: Hr40, Hr44…​ e.g.: Hr40 Kernel naming system [somatom_on-site_kc_olt_07_kernel-naming-system.wav] The kernel name pattern consists of the elements Kernel Type, Sub Kernel Type, and Resolution Index. The Kernel Type is, for example, represented by the capital letters H (for Head), or Q (for Quantitative). The Sub Kernel Type is, for example, represented by the small letters r (for regular) or v (for vascular). The Resolution Index is indicated by a two-digit number like 40. Examples of full kernel names are: Hr40 and Hr44. ? Kernel types and subtypes Table with 3 columns and 5 rows KernelType​ SubKernelType​ Kernel Properties and Usage​ H (Head)​ c (Crisp)​ Different noise texture than the corresponding regular head kernels. Aims to provide a second type of image impression for comparative purposes. ​ r (Regular )​ Head kernels for regular imaging tasks (regular are all tasks which are not explicitly listed).​ v (Vascular)​ Head kernels for vascular imaging tasks (enhancement of vessels).​ Q (Quantitative)​ r (Regular)​ Quantitative kernels with no over- and undershoots. Are to be chosen for imaging tasks where the absolute HU value is of interest.​ Kernel types and subtypes [somatom_on-site_kc_olt_08_kernel-types-subtypes.wav] There is a selection of Sub Kernel Types available for each Kernel Type as you can see in the table. These include Sub Kernel Types c, r and v for Kernel Type H (Head). Kernel Type Q (Quantitative) only includes one option: the Sub Kernel Type r. Please refer to the table for more information on the kernel properties and usage of the listed subtypes. ? Kernel overview Position 1 : KernelType Hr40 = Pos 1 H Pos 2 r Pos 3 40 Position 2 : SubKernelType, Position 3 : ResolutionIndex S = Special Kernel H = Head Q = Quantitative Sh = Special Head 48 Hc = Head Crisp 40 - 44 Hr = Head Regular 32 - 68 Hv = Head Vascular 36 - 56 Qr = Quantitative Regular 32 - 64 Kernel overview [somatom_on-site_kc_olt_09_kernel-overview.wav] Take a moment to review this kernel overview. As you already know, the first capital letter stands for the kernel type, and the second lowercase letter stands for the sub kernel type. The resolution index, which refers to the image sharpness, is defined by the subsequent number. The higher the number, the sharper the image impression. ? Course review Congratulations. You have completed the SOMATOM On.site Kernel Concept Online Training. Select the numbered buttons below to review the material before proceeding to the final assessment. Specify the kernel concept Describe the role of kernels in CT image reconstruction 1 1 2 2 2 Course review Specify the kernel concept Table with 1 columns and 2 rows Example: Hr40 Position 1: The capital letter indicates the kernel type, here Head. Position 2: The small letter indicates the sub kernel type, here Regular. Position 3: The two-digit kernel number indicates the resolution index, here 40. Describe the role of kernels in CT image reconstruction As image reconstruction parameter, a kernel digitally filters raw data and affects image sharpness, noise, and resolution. Users can select the proper kernel depending on the clinical needs. Images reconstructed with higher kernel numbers have a higher sharpness impression and a higher resolution compared to images reconstructed with lower kernel numbers. Images reconstructed with lower kernel numbers have a smoother image impression and a lower resolution compared to images reconstructed with higher kernel numbers. Installed kernels are different between scanner groups. Disclaimer Please note that the learning material is for training purposes only. For the proper use of the software or hardware, please always use the Operator Manual or Instructions for Use (hereinafter collectively “Operator Manual”) issued by Siemens Healthineers. This material is to be used as training material only and shall by no means substitute the Operator Manual. Any material used in this training will not be updated on a regular basis and does not necessarily reflect the latest version of the software and hardware available at the time of the training. The Operator Manual shall be used as your main reference, in particular for relevant safety information like warnings and cautions. Please note: Some functions shown in this material are optional and might not be part of your system. Certain products, product related claims or functionalities (hereinafter collectively “Functionality”) may not (yet) be commercially available in your country. Due to regulatory requirements, the future availability of said Functionalities in any specific country is not guaranteed. Please contact your local Siemens Healthineers sales representative for the most current information. The reproduction, transmission or distribution of this training or its contents is not permitted without express written authority. Offenders will be liable for damages. All names and data of patients, parameters and configuration dependent designations are fictional and examples only. All rights, including rights created by patent grant or registration of a utility model or design, are reserved. Unrestricted | Published by Siemens Healthineers AG | © Siemens Healthineers AG, 2024 Siemens Healthineers HQ | Siemens Healthineers AG Siemensstr. 3 91301 Forchheim Germany Phone: +49 9191 18-0 siemens-healthineers.com ? Disclaimer Assessment Start ? This assessment will test your retention of the presented content. A passing score of 80% or higher is required to complete the course and earn your certificate. Assessment questions must be answered completely to receive full credit. Partial credit will not be given for assessment questions that require multiple answers. You may repeat the assessment as many times as needed. Assessment Kernels cannot be created by users. Hr44 is a body kernel. Kernels affect patient dose. Kernels digitally filter reconstructed image data. Which statement about kernels is correct? Question 1 of 5 Select the best answer. ? Question 1 This answer is incorrect. This answer is incorrect. Incorrect This answer is incorrect. Correct The patient dose is a little bit higher in an Hr32 image. The high contrast resolution is lower in Hr68. There is no difference in the image impression between both images. The Hr68 image has a sharper impression than the Hr32 image. Which statement about the comparison between an Hr32 image and an Hr68 image is correct? Question 2 of 5 Select the best answer. ? Question 2 Correct This answer is incorrect. This answer is incorrect. This answer is incorrect. The kernel cannot change the image sharpness. You can chose the kernel depending on the X-ray tube voltage used. The kernel is used for image reconstruction. In the conventional kernel concept, image sharpness is commonly represented by the first letter in the kernel name. Which statement is correct? Question 3 of 5 Select the best answer. ? Question 3 Correct This answer is incorrect. This answer is incorrect. This answer is incorrect. Hr40 is a body kernel. Br40 is a head kernel. Sb kernel is a head kernel. Qr32 is a quantitative kernel. Which statement is correct? Question 4 of 5 Select the best answer. ? Question 4 Correct Incorrect This answer is incorrect. This answer is incorrect. This answer is incorrect. Kernels can change the image impression. Kernels can control the radiation dose. Users cannot configure a kernel for each scan protocol. Kernels can be adjusted by the user. Which statement is correct? Question 5 of 5 Select the best answer. ? Question 5 Correct This answer is incorrect. This answer is incorrect. This answer is incorrect. Assessment results YOUR SCORE: PASSING SCORE: Review Retry Retry Continue Continue Continue %Results.ScorePercent%% %Results.PassPercent%% ? Assessment results You did not pass the course. Take time to review the assessment then select Retry to continue. Congratulations. You passed the course.. Exit To access your Certificate of Completion, select the Certificates tab from the learning activity overview page. You can also access the certificate from your PEPconnect transcript. ? You have completed the SOMATOM On.site Kernel Concept Online Training. Completion Navigation Help Select the icon above to open the table of contents. Click Next to continue. Next Welcome Slide The timeline displays the slide progression. Slide the orange bar backwards to rewind the timeline. Click Next to continue. Next Timeline Select the X to close the pop-up. Click Next to continue. Next Layer Slide Select Submit to record your response. Click the X in the upper right corner to exit the navigation help. Assessment Slide Question Bank 1 QR700012802 | Effective Date: 28 Nov 2024 1.1 Welcome 1.2 Navigation hints 1.3 Role of kernels in CT image reconstruction 1.4 Role of kernels in image reconstruction 1.5 Kernel definition 1.6 Kernel concept 1.7 Kernel naming system 1.8 Kernel types and subtypes 1.9 Kernel overview 1.10 Course review 1.11 Disclaimer 1.12 Assessment