SU-E-J-95: Increasing Soft Tissue Contrast by Combining New CT Technologies and Imaging Dose for Radiation Treatment Planning and Delivery Guidance

2013 ◽  
Vol 40 (6Part8) ◽  
pp. 172-172
Author(s):  
G Chen ◽  
A Tai ◽  
F Liu ◽  
H Shukla ◽  
B Erickson ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Treatment Planning ◽  
Radiation Treatment ◽  
Radiation Treatment Planning ◽  
Soft Tissue Contrast ◽  
Tissue Contrast ◽  
Imaging Dose

scholarly journals Emerging Role of MRI for Radiation Treatment Planning in Lung Cancer

2016 ◽  
Vol 15 (6) ◽  
pp. NP47-NP60 ◽  
Author(s):  
David C. P. Cobben ◽  
Hans C. J. de Boer ◽  
Rob H. Tijssen ◽  
Emma G. G. M. Rutten ◽  
Marco van Vulpen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Lung Cancer ◽  
Magnetic Resonance ◽  
Treatment Planning ◽  
Radiation Treatment ◽  
Imaging Modality ◽  
Resonance Imaging ◽  
Radiation Treatment Planning ◽  
Time Motion ◽  
Tissue Contrast

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast and allows for specific scanning sequences to optimize differentiation between various tissue types and properties. Moreover, it offers the potential for real-time motion imaging. This makes magnetic resonance imaging an ideal candidate imaging modality for radiation treatment planning in lung cancer. Although the number of clinical research protocols for the application of magnetic resonance imaging for lung cancer treatment is increasing ( www.clinicaltrials.gov ) and the magnetic resonance imaging sequences are becoming faster, there are still some technical challenges. This review describes the opportunities and challenges of magnetic resonance imaging for radiation treatment planning in lung cancer.

Stereotactic radiation treatment planning and follow-up studies involving fused multimodality imaging

2004 ◽  
Vol 101 (Supplement3) ◽  
pp. 326-333 ◽  
Author(s):  
Klaus D. Hamm ◽  
Gunnar Surber ◽  
Michael Schmücking ◽  
Reinhard E. Wurm ◽  
Rene Aschenbach ◽  
...  
Keyword(s):  
Image Fusion ◽  
Treatment Planning ◽  
Radiation Treatment ◽  
Data Sets ◽  
Slice Thickness ◽  
Radiation Treatment Planning ◽  
Content Type ◽  
Follow Up Studies ◽  
Fine Print

Object. Innovative new software solutions may enable image fusion to produce the desired data superposition for precise target definition and follow-up studies in radiosurgery/stereotactic radiotherapy in patients with intracranial lesions. The aim is to integrate the anatomical and functional information completely into the radiation treatment planning and to achieve an exact comparison for follow-up examinations. Special conditions and advantages of BrainLAB's fully automatic image fusion system are evaluated and described for this purpose. Methods. In 458 patients, the radiation treatment planning and some follow-up studies were performed using an automatic image fusion technique involving the use of different imaging modalities. Each fusion was visually checked and corrected as necessary. The computerized tomography (CT) scans for radiation treatment planning (slice thickness 1.25 mm), as well as stereotactic angiography for arteriovenous malformations, were acquired using head fixation with stereotactic arc or, in the case of stereotactic radiotherapy, with a relocatable stereotactic mask. Different magnetic resonance (MR) imaging sequences (T1, T2, and fluid-attenuated inversion-recovery images) and positron emission tomography (PET) scans were obtained without head fixation. Fusion results and the effects on radiation treatment planning and follow-up studies were analyzed. The precision level of the results of the automatic fusion depended primarily on the image quality, especially the slice thickness and the field homogeneity when using MR images, as well as on patient movement during data acquisition. Fully automated image fusion of different MR, CT, and PET studies was performed for each patient. Only in a few cases was it necessary to correct the fusion manually after visual evaluation. These corrections were minor and did not materially affect treatment planning. High-quality fusion of thin slices of a region of interest with a complete head data set could be performed easily. The target volume for radiation treatment planning could be accurately delineated using multimodal information provided by CT, MR, angiography, and PET studies. The fusion of follow-up image data sets yielded results that could be successfully compared and quantitatively evaluated. Conclusions. Depending on the quality of the originally acquired image, automated image fusion can be a very valuable tool, allowing for fast (∼ 1–2 minute) and precise fusion of all relevant data sets. Fused multimodality imaging improves the target volume definition for radiation treatment planning. High-quality follow-up image data sets should be acquired for image fusion to provide exactly comparable slices and volumetric results that will contribute to quality contol.

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