Adaptive
Management of Patient Anatomical Variation Using Image Feedback
Di Yan, DSc.
Radiation
Oncology,
Image guided adaptive radiotherapy (IGART) is an
application of dynamic feedback control in radiation treatment, which utilizes
the image feedback of patient-specific anatomical & biological variations
to frequently evaluate treatment quality and optimize the treatment plan if
necessary by including the variations in the design of dose distribution. IGART
expands the imaging/correct/delivery sequence of image guided intervention to a
broader and more efficient methodology, in which evolving knowledge of
patient-specific anatomical & biological variations assessed by
multiple patient image feedback is used to dynamically optimize the treatment. A
typical IGART model consists of two parts, (1) a patient-specific variation
process as well as the corresponding estimator for process parameter and dose
estimation, and (2) an adaptive controller to manage patient variations. This
lecture outlines the IGART model for patient anatomical variation management
alone.
Patient anatomical variation
during the radiotherapy includes inter- and intra-treatment variations of patient
anatomical shape and position caused by patient setup, beam placement, and patient
organ physiological motion and deformation. In addition, tumor and normal organ
dose response can also induce significant variations in tissue shape and
position during the treatment course. Random process has been used to model patient-specific
anatomical variation in radiotherapy. Two major parameters, the mean and the
standard deviation of the variation, have been used to characterize this
variation process. These two parameters could be either time-invariant
indicating a stationary random process, or time-variant indicting a
non-stationary random process. It will be relatively easy to estimate the parameters
in a stationary process, but less straightforward to perform the estimation if
the variation process is non-stationary. Probability distribution function
(pdf) of patient-specific anatomical variation can also be estimated occasionally
for certain type of variation i.e. patient respiratory induced organ variation.
However, pdf estimation could not be reasonably accomplished for most
patient-specific variations due to the limit number of samples in image
feedback. Therefore, it is practically important to use these two process
parameters directly to estimate the cumulative dose.
Adaptive
controller provides an optimal control law that maps the estimated process
parameters into the space of control or adjustment variables, such as machine
output intensity map, patient position and etc. In theory of nonlinear
stochastic control, the control law can be derived using the dynamic
programming and solving the Bellman equation numerically. However, the numerical
solution exists only for very simple cases. For radiotherapy process, a
simplified and idealized control law can only be used for the education
purpose, but not in the clinic implementation to manage the actual patient
treatment. A practical controller in IGART can consists of a decision-making
function to determine if the ongoing treatment plan needs to be modified, and a
4D adaptive planning engine to modify the ongoing treatment plan.
Patient
anatomical variation in radiotherapy can be systematically managed using image
feedback adaptive treatment technique. The fundamental difference between
adaptive treatment technique and other image-guided techniques is the use of
patient-specific treatment information. Adaptive technique intends to use all patient-specific
dose information - including what has been delivered in the previous
treatments, what can be delivered at the present treatment, and what would be delivered
in future treatments - in the design of treatment plan. Therefore, treatment
plan designed in adaptive radiotherapy is called 4D adaptive plan, which is in
principle a treatment control law to manage treatment process. The objectives
in adaptive planning optimization are constructed based on a selection of
control strategies that could be either the online or
the offline with one control action, multiple actions or continue actions.
Selection of control strategy and number of control actions is, of course,
dependent on the nature of patient variation process as well as the estimation
uncertainties, and has to be determined considering also the clinical practice.