MEDN62331
Imaging for Radiotherapy
15
Semester 2
Postgraduate Taught
Level 6
Alan McWilliam
Unit coordinator
School of Medical Sciences
1
FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 '
European Credit Transfer & Accumulation System Rating : 7.5
<div><p>The unit will provide the students with the following knowledge:</p><ul> <li>The unit will describe imaging in radiotherapy, the module will use the example of a lung cancer patient to describe the clinical aspects.</li> <li>Provide the understanding of why imaging is important in radiotherapy and where it fits in the patient pathway including why different imaging is required for different patient groups. </li> <li>Knowledge of the DICOM standard, including practical experience in setting up a DICOM network with multiple nodes including an image viewer. Students will perform simple operations to query networked databases and send images to be viewed.</li> <li>CT will be discussed in detail including the machine, acquisition of images and image reconstruction including non-rigid registration.</li> <li>Additional imaging modalities used in radiotherapy will be introduced; cone beam CT, 4DCT, MRI and PET. Each modality will be discussed including the machine, image acquisition and reconstruction. Practical examples will be included to provide information to compare and contrast each modality. </li> <li>Visits to scanners will be arranged (depending on clinical workload).</li> <li>Image processing will be introduced and discussed including: what is an image, window and level, filtering, image interpolation, edge detection, rendering, transformations, filtering, thresholding.</li> <li>Image registration will be introduced, explaining how the techniques described above can be used to fuse multiple images. Automated techniques and cost functions will be introduced.</li> <li>(All introduced techniques will be demonstrated in the practical sessions).</li> <li>Practical sessions will work with Python. No prior knowledge is required, students will be introduced to the language in the course – structure of language and common syntax. This will use jupyter notebooks for the exercises.</li> <li>The practical will produce code for image processing tasks as described above and the implementation of an image fusion solution. The ultimate aim will be for the students to create a package to automatically fuse two CT scans. </li> <li>The current status of image based research will be discussed to provide context of the wider field.</li></ul></div><p> </p>
<div><p>The unit will provide the students with the following knowledge:</p><ul> <li>The unit will describe imaging in radiotherapy, the module will use the example of a lung cancer patient to describe the clinical aspects.</li> <li>Provide the understanding of why imaging is important in radiotherapy and where it fits in the patient pathway including why different imaging is required for different patient groups. </li> <li>Knowledge of the DICOM standard, including practical experience in setting up a DICOM network with multiple nodes including an image viewer. Students will perform simple operations to query networked databases and send images to be viewed.</li> <li>CT will be discussed in detail including the machine, acquisition of images and image reconstruction including non-rigid registration.</li> <li>Additional imaging modalities used in radiotherapy will be introduced; cone beam CT, 4DCT, MRI and PET. Each modality will be discussed including the machine, image acquisition and reconstruction. Practical examples will be included to provide information to compare and contrast each modality. </li> <li>Visits to scanners will be arranged (depending on clinical workload).</li> <li>Image processing will be introduced and discussed including: what is an image, window and level, filtering, image interpolation, edge detection, rendering, transformations, filtering, thresholding.</li> <li>Image registration will be introduced, explaining how the techniques described above can be used to fuse multiple images. Automated techniques and cost functions will be introduced.</li> <li>(All introduced techniques will be demonstrated in the practical sessions).</li> <li>Practical sessions will work with Python. No prior knowledge is required, students will be introduced to the language in the course – structure of language and common syntax. This will use jupyter notebooks for the exercises.</li> <li>The practical will produce code for image processing tasks as described above and the implementation of an image fusion solution. The ultimate aim will be for the students to create a package to automatically fuse two CT scans. </li> <li>The current status of image based research will be discussed to provide context of the wider field.</li></ul></div><p> </p>
<div>
<p>This unit aims to provide students with knowledge of the role of imaging in radiotherapy. Students will be able to understand the processes of image acquisition and reconstruction of CT, cone beam CT, MRI and PET and the role of each in the radiotherapy patient pathway. </p><p>Students will be introduced to image processing and image fusion and will be expected to be able to apply this knowledge on clinical images within python software they develop within the module. </p></div>
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0
Other
50%
2
Written assignment (inc essay)
20%
8
Practical skills assessment
30%
<p>PC based multiple choice examination</p>
<p style="margin-left: 5.7pt">Feedback is given at the end of each session or unit</p><p style="margin-left: 5.7pt">Written feedback provided within 15 working days for all assessments</p>
N
Tutorials
35
0
115