The Use of Medical Images in Planning and Delivery of Radiation Therapy

Kalet, Ira J.; Austin-Seymour, Mary

doi:10.1136/jamia.1997.0040327

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…A common method to describe the dosimetric influence of a beam is the projection of the VOIs from the beam node on a plane perpendicular to the line from beam node to beam target, that is, the beam's eye view. 20 A similar method has been applied in a case-based approach,16 where, among other features, similarity of beam nodes is established by projections of VOIs on a plane perpendicular to the line from beam node to PTV centroid. We describe various properties of a beam by projections onto this plane and create a separate gray-scale image for each property.…”

Section: C Feature Generationmentioning

confidence: 99%

Feasibility and analysis of CNN‐based candidate beam generation for robotic radiosurgery

Gerlach

Fürweger

Hofmann³

et al. 2020

Medical Physics

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Purpose: Robotic radiosurgery offers the flexibility of a robotic arm to enable high conformity to the target and a steep dose gradient. However, treatment planning becomes a computationally challenging task as the search space for potential beam directions for dose delivery is arbitrarily large. We propose an approach based on deep learning to improve the search for treatment beams. Methods: In clinical practice, a set of candidate beams generated by a randomized heuristic forms the basis for treatment planning. We use a convolutional neural network to identify promising candidate beams. Using radiological features of the patient, we predict the influence of a candidate beam on the delivered dose individually and let this prediction guide the selection of candidate beams. Features are represented as projections of the organ structures which are relevant during planning. Solutions to the inverse planning problem are generated for random and CNN-predicted candidate beams. Results: The coverage increases from 95.35% to 97.67% for 6000 heuristically and CNN-generated candidate beams, respectively. Conversely, a similar coverage can be achieved for treatment plans with half the number of candidate beams. This results in a patient-dependent reduced averaged computation time of 20.28%-45.69%. The number of active treatment beams can be reduced by 11.35% on average, which reduces treatment time. Constraining the maximum number of candidate beams per beam node can further improve the average coverage by 0.75 percentage points for 6000 candidate beams. Conclusions: We show that deep learning based on radiological features can substantially improve treatment plan quality, reduce computation runtime, and treatment time compared to the heuristic approach used in clinics.

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Section: C Feature Generationmentioning

confidence: 99%

Feasibility and analysis of CNN‐based candidate beam generation for robotic radiosurgery

Gerlach

Fürweger

Hofmann³

et al. 2020

Medical Physics

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“…where 1 c and 2 c were defined previously. With this formulation, weight β is no longer a constant; it changes its value during the course of contour evolution.…”

Section: Level-set Contour-finding Algorithm With An Adaptive Weightmentioning

confidence: 99%

Method of image fusion for radiotherapy

2007

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Patient setup error is one of the major causes of tumor position uncertainty in radiotherapy for extracranial targets, which can result in a decreased radiation dose to the tumor and an increased dose to the normal tissues. Therefore, it is a common practice to verify the patient setup accuracy by comparing portal images with a digitally reconstructed radiograph (DRR) reference image. This paper proposes a practical method of portal image and DRR fusion for patient setup verification. As a result of the mean intensity difference between the inside and outside of the actual radiation region in the portal image, the image fusion in this work is fulfilled by applying an image registration process to the contents inside or outside of the actual radiation region in the portal image and the relevant contents that are extracted, accordingly, from the DRR image. The image fusion can also be fulfilled statistically by applying two separate image registration processes to the inside and outside of the actual radiation regions. To segment the image registration contents, automatic or semiautomatic region delineation schemes are employed that aim at minimizing users' operation burden, while at the same time maximizing the use of human intelligence. To achieve an accurate and fast delineation, this paper proposes using adaptive weight in the conventional level-set contourfinding algorithm for the automatic delineation scheme, as well as the use of adaptive banding in the conventional Intelligent Scissors algorithm for the semiautomatic delineation scheme.

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“…Because of the anatomic accuracy, CT scanning remains the primary imaging modality for target delineation and treatment planning, including the use of CT-simulators for image acquisition; in addition, CT Hounsfield numbers are required by dose calculation algorithms [20,26,27,30]. Supplemental target localization information may be obtained from combined use of MRI and PET both of which may be fused with CT images.…”

Section: Tissue Imaging For Target Volume Definitionmentioning

confidence: 99%