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CT Anatomy For Radiotherapy

Conclusions: Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

CT Anatomy for Radiotherapy

This item is large, and may take some time to download.DescriptionFlashcards created from the 'CT anatomy for Radiotherapy' book by Bridge and Tipper. Cards created for each subgroup of intracranial, head and neck, chest and abdomen/pelvis. Sample (from 1205 notes) Cards are customizable! When this deck is imported into the desktop program, cards will appear as the deck author has made them. If you'd like to customize what appears on the front and back of a card, you can do so by clicking the Edit button, and then clicking the Cards button. ID (hidden) 70f12e5b97a540d99a49f61596d939b6-ao-6 Header Image Footer Remarks Sources Extra 1 Extra 2 Question Mask Answer Mask Original Mask Tags ID (hidden) a5691a4733794bc08efe212f103a0492-ao-5 Header Image Footer Remarks Sources Extra 1 Extra 2 Question Mask Answer Mask Original Mask Tags ID (hidden) b5a13e6a05b54c7ea58aeb63cbeac9cc-ao-1 Header Image Footer Remarks Sources Extra 1 Extra 2 Question Mask Answer Mask Original Mask Tags After the file is downloaded, double-click on it to open it in the desktop program.

The text is aimed not only at the pre-registration radiotherapy student, but also at staff working with Image-Guided Radiotherapy equipment, CT-simulators or CT scanners. It will also be of value to radiotherapy planning staff wishing to update their knowledge of CT anatomy and advanced practitioners wishing to specialise in advanced planning or structure outlining.

This book is a product of collaboration between the diagnostic radiography and radiotherapy disciplines and it aims to provide enough detail of essential diagnostic CT interpretation skills while remaining relevant and focused on radiotherapy. I hope you find it to be a useful and enjoyable read that will nurture a growing interest in the endlessly fascinating world of cross-sectional anatomy.

This book is intended to prepare the radiotherapy professional for CT interpretation of radiotherapy planning or image-guided radiotherapy scans. This introductory chapter provides some background details related to CT equipment and principles as well as potential problems and hints to aid image interpretation. All essential structures relevant to radiotherapy are described and depicted on labelled CT images covering the whole body. Each region of the body has its own chapter and within that chapter, various anatomical systems are described along with an overview of their CT anatomy on transverse sections. Three-dimensional images have been reconstructed from CT outlines and are presented to aid understanding of the relationships between structures. Labels and outlines are provided on real CT images alongside notes relating to image interpretation and tips for identifying structures. Within a region of the body each structure has its own identifying number, enabling them to be traced throughout the CT series with ease. The structure index contains a full list of these labels. After the different systems have been discussed, the full CT anatomy of the region is presented on labelled images alongside corresponding blank CT scans. Intracranial CT images are complemented with MR and fused images where this represents common clinical practice. To aid the reader with this, there is a short introduction to some common MR imaging sequences and hints on image interpretation.

The CT images in the book were all obtained using standard radiotherapy planning protocols and use immobilisation and positioning techniques familiar to those found in radiotherapy. This distinguishes the book from other CT texts which utilise diagnostic views and patient positions. It must be borne in mind, however, that individual departments vary in positioning requirements almost as much as individual patients vary in anatomy. The reader is urged to use the images in this text to engender an understanding of how different structures relate to each other so that this knowledge can be applied to their own clinical practice.

Each chapter concludes with a short self-test to consolidate and check learning. Answers to these can be found at the back of the book. The final chapter provides an overview of alternative CT imaging systems, including megavoltage CT, with some image interpretation. The emphasis of the book, however, remains on providing interpretation skills using standard kilovoltage CT images with a view to undertaking radiotherapy planning or IGRT delivery.

Findings This quality improvement study was conducted on a set of 242 head and neck and 519 pelvic computed tomography scans acquired for radiotherapy planning at 8 distinct clinical sites with heterogeneous population groups and image acquisition settings. The proposed technology achieved levels of accuracy within interexpert variability; statistical agreement was observed for 13 of 15 structures while reducing the annotation time by a mean of 93% per scan.

Importance Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions.

This course is part of The Applied Sciences of Oncology (ASO) distance-learning course.AimTo provide a practical understanding of applied anatomy relevant to the delivery of radiotherapy in the treatment of cancer.ObjectivesUpon completion, participants should be able to:Describe the anatomical implications of cancer spread.Discuss 3D relationships of organs and their surrounding structures.Identify tissues and organs at risk from either cancer spread or treatment delivery.Demonstrate key concepts in radiological and cross sectional anatomy with reference to oncology.

In the present work we compared the spatial uncertainties associated with a MR-based workflow for external radiotherapy of prostate cancer to a standard CT-based workflow. The MR-based workflow relies on target definition and patient positioning based on MR imaging. A solution for patient transport between the MR scanner and the treatment units has been developed. For the CT-based workflow, the target is defined on a MR series but then transferred to a CT study through image registration before treatment planning, and a patient positioning using portal imaging and fiducial markers.

An "open bore" 1.5T MRI scanner, Siemens Espree, has been installed in the radiotherapy department in near proximity to a treatment unit to enable patient transport between the two installations, and hence use the MRI for patient positioning. The spatial uncertainty caused by the transport was added to the uncertainty originating from the target definition process, estimated through a review of the scientific literature. The uncertainty in the CT-based workflow was estimated through a literature review.

The purpose of this study is to investigate if a MR-only radiotherapy workflow, in accordance with figure 1b, has the potential to improve the spatial accuracy compared to the more conventional CT-based workflow (figure 1a). The estimations of the uncertainties in the different workflows are based on both a literature review and the results of our own experiments.

An open-bore MRI scanner (Siemens Espree, 1.5T) was used for the MR imaging of the patients in connection radiotherapy. For prostate patients, a T2-weighted SPACE sequence (Siemens), which is a 3D turbo spin-echo sequence with varying flip angle on the refocusing pulses, was used. The slice thickness was 1.7 mm, typical pixel-size was 1.0 1.0 mm2, and the bandwidth was 592 Hz per pixel. Distortions caused by gradient non-linearity were corrected with an algorithm based on spherical harmonic expansion of the fields generated by the gradient coils [15]. The 3D correction algorithm including representation of the coils was delivered by Siemens as a standard clinical tool integrated in the scanner software (VB15). The scanner was set in an isocentric mode, which moves the table prior to the acquisition of each sequence, to place the MR isocenter in the centre of the volume of interest.

The workflow in figure 1a involves a registration between a CT and MR study. Errors in this registration directly affect the spatial accuracy of the target definition. Registrations between MR and CT for prostate patients can be performed based on fiducial markers [14]. The trend is, however, to use mutual information (MI) registration based directly on the patient anatomy [27, 28]. The prostate position relative other anatomical structures is not fix, therefore the registration should ideally be based on the prostate with just a small margin. However, this has been reported problematic because of too limited morphological information content in the CT representation of the prostate [29, 30]. A few studies have been performed evaluating the accuracy and precision of MI registration for CT and MR studies of the prostate; the registration uncertainty has been reported to be around 2 mm [29, 31]. Roberson et al. [31] reported that registration results depend on the starting point for a specific MI optimization software. The mean difference between different stating points was up to 1 mm in the RL direction. The corresponding number for MR-MR registration was 0.4 mm in the HF direction which could indicate that the mutual information maximum is more distinct for MR-MR registration compared to CT-MR registration.

The patient positioning at treatment, with the development of image guided radiotherapy, been in focus the recent years. For prostate cancer patients the improvements in spatial treatment accuracy has been considerable. Both the CT and the MR-based workflows, shown in figure 1, rely on imaging before each fraction. Intra-fraction motion of the prostate is therefore an issue for both workflows. 041b061a72


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