## 3 Results  
In our preliminary experiments, we found that the intensities of the photoacoustic signals varied with the surface conditions of the sample surface. Therefore, we tried to distinguish between caries and healthy (normal) parts of teeth using not the intensity but the shape of the frequency spectra of the photoacoustic signals. Figure 5a shows the frequency spectra of normal and pigmented parts of a tooth calculated from the waveforms obtained from the simulated caries inside the tooth and the normal part. To suppress the effect of noise, we calculated the centered moving average values and these curves were normalized to the peak intensity. The difference between the intensities of the normalized frequency spectra from caries and the normal part was calculated and the result is shown in Figure 5b, which shows that the difference spectrum has positive values from 0.5 MHz to 1.5 MHz. In addition, we investigated the effects of stains or caries on the occlusal surface by comparing the frequency spectra generated in simulated caries lesions inside the teeth with those generated at occlusal surfaces colored with the pigment. Figure 6 shows an appearance of  
Figure 4: Schematic and photo of a photoacoustic imaging system using a bundle of hollow-optical fibers. Figure 5: (**a**) Frequency spectra of photoacoustic waves from normal and pigmented parts of a tooth; and (**b**) the difference between the signals from the normal and pigmented parts. with the pigment. Figure 7a shows the frequency spectra from the pigment inside the teeth and the stained occlusal surface. The difference between these spectra is shown in Figure 7b, which indicates that the frequency components between 0.4 MHz and 1.2 MHz are higher in the waves from the caries lesions inside the teeth. Figure 8a shows the frequency spectra of the photoacoustic signals obtained from the 5% and 50% liquid samples with black inks. In the spectrum of the 5% concentration liquid, there is a peak at approximately 1 MHz with a spread to 4 MHz. The peak shifted to 2 MHz in the 50% liquid and the broad peak spanned 5 MHz. As shown in the differential spectrum in Figure 8b, the difference had negative values in the range of 0-1.5 MHz and became positive in the range of 1.5-4.5 MHz. We applied this method to the imaging of simulated hidden caries, and the result is shown in Figure 9. In this experiment, the detected frequency spectra were integrated in the region from 0.5 MHz to 1.2 MHz and the integrated intensities were plotted so that darker spots showed stronger signals. In the experiments, laser light scattered from the dental tissue sometimes hits the acoustic probe directly and the photoacoustic waves from the probe itself are observed. We investigated waveforms with this type of noise and found that acoustic waves from the probe were not observed more than  
Figure 8: (**a**) Frequency spectra of photoacoustic waves from liquid samples with 5% and 50% black ink and (**b**) the difference between the two spectra. Figure 6: Appearance of occlusal surface stained with a brown pigment. Figure 7: (**a**) Frequency spectra of photoacoustic waves from pigment inside a tooth and from a pigmented tooth surface; and (**b**) the difference between the two spectra. 1.5 \(\upmu\)s after the laser radiation. Therefore, we analyzed the waveforms by only focusing on the signals after 1.5 \(\upmu\)s.  
Figure 9a shows the appearance of a sample with simulated caries formed 1.5 mm below the enamel surface. The diameter of the simulated caries was 2.5 mm. It was confirmed that no visible dark spot was observed on the enamel surface. Figure 9b shows a photoacoustic image of the same sample obtained using our proposed system. One can see that the simulated caries inside the tooth is clearly observed and that the size of the dark spot is the same as the size of the simulated caries. Figure 9c shows a photoacoustic image of the same tooth sample before applying the pigment.