4. Discussion
Most newly discovered caries lesions are in the pits and fissures of
occlusal surfaces. Such surfaces are more challenging for lesion
activity assessment using time-resolved SWIR reflectance imaging due to
the highly convoluted topography. In the first clinical study to assess
lesion activity in vivo using time-resolved SWIR reflectance imaging,
lesions located on the occlusal surfaces of primary teeth were chosen to
ensure that most of the lesions would be active [26]. The study
showed the potential of using the delay to discriminate between active
and arrested lesions however there was considerable difficulty in
sufficiently dehydrating lesions in these areas in 30 seconds. In
addition, many of the acquired curves were noisy or incomplete
preventing the calculation of kinetic information from the curves such
as rates or %Ifin [26]. Values for ΔI% were
calculated, however there was no significant difference in ΔI% between
active and arrested lesions. In this study and in a recent in
vitro study [25], ΔI% was not significantly higher for active
lesions compared to arrested lesions. In earlier studies that utilized
simulated lesions or lesions that were confined to the outer half of
enamel, ΔI% was significantly higher for active lesions [8, 9]. In
those early studies, optical coherence tomography was used to assess
lesion severity with deeper lesions being avoided due to the limited
penetration depth of OCT. However, for this study and a recent study
[25], microCT was used to image the proximal and occlusal lesions
many of which were of greater severity.
The newly designed probe for the clinical handpiece with the angled and
focused air nozzle was as effective as the benchtop system that had the
air nozzle pointed almost directly at the tooth occlusal surfaces. A
higher air pressure of 25 psi was used instead of the 10-15 psi that was
used in previous studies, however this is still markedly lower than the
pressures used by dental air syringes that can be as high as 80 psi.
Complete dehydration of active lesions should occur within 30 seconds
for practical clinical implementation. This was achieved for all the
active lesions using both the benchtop system and the handpiece within
30 seconds. In contrast, complete dehydration can take hundreds of
seconds for arrested lesions, however the entire curve is not needed to
calculate suitable values of %Ifin and rate as they are
sufficiently different from active lesions due to the much slower rate
of dehydration for arrested lesions.
Based on this study and recent studies [25, 26], it appears that ΔI
is not a reliable parameter to discriminate between active and arrested
lesions and is not suitable for in vivo measurements since the
depth and severity of the lesions is not known ahead of time. The other
three parameters that reflect the kinetics of the dehydration process;
delay, %Ifin, and rate are less dependent on the lesion
depth and severity and are better suited for clinical use.
A previous in vitro study showed that 1950 nm was best suited for
lesion activity assessment using time-resolved reflectance imaging
[25], however compact SWIR cameras small enough for clinical use are
limited to wavelengths less than 1700 nm, thus we chose to use an SLD
operating at 1470 nm which overlaps the water absorption band centered
at 1450 nm.
In this study, we choose to use the Hill equation to model the kinetics
of dehydration as opposed to the sigmoid function that was used
previously [8, 9]. Both functions are frequently used to fit
sigmoidal shaped curves, however the Hill equation performed better for
many of the arrested curves for which the sigmoidal shape was not as
well developed, such as the arrested curve in Fig. 5. 3D scatter plots
for both the benchtop system and the handpiece using delay,
%Ifin, and rate show that the two groups are well
separated for clear discrimination between active and arrested lesions.
Clinical assessments are more challenging, and it is valuable to have
multiple parameters available for use. The calculation of delay,
%Ifin, and rate can be easily automated for rapid
calculation and 2D projection maps can be created for those parameters
in a similar fashion to what has been done previously for optical
coherence tomography [6, 28].