Irregular Breathing Classification from Multiple Patient Datasets - Prediction and Classification of Respiratory Motion - page 106

radiation beams are only on when respiration is within predefined amplitude or
phase [ 10 ]. Since margins are smaller with more conformal therapies, breathing
irregularities might become more important unless there is a system in place that
can stop the beam in the presence of breathing irregularities. Real-time tumor-
tracking, where the prediction of irregularities really becomes relevant [ 2 ], has yet
to be clinically established.
The motivation and purpose of respiratory motion classification for irregular
breathing patterns are that the irregular respiratory motion can impact the dose
calculation for patient treatments [ 11 , 12 ]. A highly irregularly breathing patient
may be expected to have a much bigger internal target volume (ITV) than a regular
breathing patient, where ITV contains the macroscopic cancer and an internal
margin to take into account the variations due to organ motions [ 12 ]. Thus, the
detection of irregular breathing motion before and during the external beam
radiotherapy is desired for optimizing the safety margin [ 11 ]. Only a few clinical
studies, however, have shown a deteriorated outcome with increased irregularity of
breathing patterns [ 2 , 8 , 11 ], probably due to the lack of technical development in
this topic. Other reasons confounding the clinical effect of irregular motion such as
variations in target volumes or positioning uncertainties also influence the clas-
sification outcomes [ 2 , 11 - 13 ]. The newly proposed statistical classification may
provide clinically significant contributions to optimize the safety margin during
external beam radiotherapy based on the breathing regularity classification for the
individual patient. An expected usage of the irregularity detection is to adapt the
margin value, i.e., the patients classified with regular breathing patterns would be
treated with tight margins to minimize the target volume. For patients classified
with irregular breathing patterns safety margins may need to be adjusted based on
the irregularity to cope with baseline shifts or highly fluctuating amplitudes that
are not covered by standard safety margins [ 11 , 12 ].
There exists a wide range of diverse respiration patterns in human subjects
[ 11 - 17 ]. However, the decision boundary to distinguish the irregular patterns from
diverse respirations is not clear yet [ 7 , 13 ]. For example, some studies defined only
two (characteristic and uncharacteristic [ 11 ]) or three (small, middle, and large
[ 12 ]) types of irregular breathing motions based on the breathing amplitude to
access the target dosimetry [ 11 , 12 ]. In this study, respiratory patterns can be
classified as normal or abnormal patterns based on a regular ratio ðÞ representing
how many regular/irregular patterns exist within an observation period [ 7 ]. The key
point of the classification as normal or abnormal breathing patterns is how to extract
the dominant feature from the original breathing datasets [ 18 - 24 ]. For example, Lu
et al. calculated a moving average curve using a fast Fourier transform to detect
respiration amplitudes [ 7 ]. Some studies showed that the flow volume curve with
neural networks can be used for the classification of normal and abnormal respi-
ratory patterns [ 15 , 16 ]. However, spirometry data are not commonly used for
abnormal breathing detection during image-guided radiation therapy [ 15 ].
To detect irregular breathing, we present a method that retrospectively classifies
breathing patterns using multiple patients-breathing data originating from a
Cyberknife treatment facility [ 25 ]. The multiple patients-breathing data contain