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D. Cabrera DeBuc et al.
studies such as fluorescein angiography may be objectively monitored. At the same
time, it may be possible to explain why some patients respond to treatment while
others do not. OCT has significant potential both as a diagnostic tool and particularly
to objectively monitor subtle retinal changes induced by therapeutic interventions.
Thus, OCT may become a valuable tool in determining the minimum maintenance
dose of a certain drug in the treatment of retinal diseases, and may demonstrate
retinal changes that explain the recovery in some patients without angiographically
demonstrable improvement and lack of recovery in others.
In the clinical routine, measurement of retinal thickness by the OCT software
depends on the identification of the boundaries of the various cellular layers of the
retina. Once the various layers can be identified and correlated with the histological
structure of the retina, it may seem relevant to measure not only the entire thickness
of the retina, but the thickness of the various cellular layers. Moreover, measuring
the reflectance of the various retinal layers on OCT images may also be of interest.
Drexler et al. have shown in in vitro and in vivo studies that physiological processes
of the retina lead to optical density changes that can be observed by a special M-mode
OCT imaging, known as optophysiology [34, 35]. Thus, it also seems rational that
quantitative analysis of reflectance changes may provide clinically relevant information in retinal pathophysiology [16].
6.2.2 Quality, Artifacts, and Errors in Optical Coherence
Tomography Images
Several investigators have demonstrated a relatively high reproducibility of OCT
measurements [7, 8, 36–42]. However, quantitative retinal thickness data generated
by OCT could be prone to error as a result of image artifacts, operator errors, decentration errors resulting from poor fixation, and failure of accurate retinal boundary
detection by the commercial and custom-built software algorithms. Therefore, the
correct image acquisition along with the accurate and reproducible quantification of
retinal features by OCT is crucial for evaluating disease progression and response
to therapy. Usually, image analysis quality largely depends upon the quality of the
acquired signal itself. Thus, controlling and assessing the OCT image quality is of
high importance to obtain the best quantitative and qualitative assessment of retinal
morphology. At present, the commercial software of some OCT systems (e.g. Cirrus
OCT) provides a quality score, identified as the signal strength (SS) but the clinical
advantage of this parameter is not really known. The quality score is based on the
total amount of the retinal signal received by the OCT system. We note that the SS
score should not be used as an image quality score since it is basically a SS score.
Stein et al. found that SS outperformed signal-to-noise ratio (SNR) in terms of poor
image discrimination [42]. SNR is a standard parameter used to objectively evaluate the quality of acquired images. Stein et al. suggested that SS possibly provides
insight into how operators subjectively assess OCT images, and stated that SS is a
D. Cabrera DeBuc et al.
studies such as fluorescein angiography may be objectively monitored. At the same
time, it may be possible to explain why some patients respond to treatment while
others do not. OCT has significant potential both as a diagnostic tool and particularly
to objectively monitor subtle retinal changes induced by therapeutic interventions.
Thus, OCT may become a valuable tool in determining the minimum maintenance
dose of a certain drug in the treatment of retinal diseases, and may demonstrate
retinal changes that explain the recovery in some patients without angiographically
demonstrable improvement and lack of recovery in others.
In the clinical routine, measurement of retinal thickness by the OCT software
depends on the identification of the boundaries of the various cellular layers of the
retina. Once the various layers can be identified and correlated with the histological
structure of the retina, it may seem relevant to measure not only the entire thickness
of the retina, but the thickness of the various cellular layers. Moreover, measuring
the reflectance of the various retinal layers on OCT images may also be of interest.
Drexler et al. have shown in in vitro and in vivo studies that physiological processes
of the retina lead to optical density changes that can be observed by a special M-mode
OCT imaging, known as optophysiology [34, 35]. Thus, it also seems rational that
quantitative analysis of reflectance changes may provide clinically relevant information in retinal pathophysiology [16].
6.2.2 Quality, Artifacts, and Errors in Optical Coherence
Tomography Images
Several investigators have demonstrated a relatively high reproducibility of OCT
measurements [7, 8, 36–42]. However, quantitative retinal thickness data generated
by OCT could be prone to error as a result of image artifacts, operator errors, decentration errors resulting from poor fixation, and failure of accurate retinal boundary
detection by the commercial and custom-built software algorithms. Therefore, the
correct image acquisition along with the accurate and reproducible quantification of
retinal features by OCT is crucial for evaluating disease progression and response
to therapy. Usually, image analysis quality largely depends upon the quality of the
acquired signal itself. Thus, controlling and assessing the OCT image quality is of
high importance to obtain the best quantitative and qualitative assessment of retinal
morphology. At present, the commercial software of some OCT systems (e.g. Cirrus
OCT) provides a quality score, identified as the signal strength (SS) but the clinical
advantage of this parameter is not really known. The quality score is based on the
total amount of the retinal signal received by the OCT system. We note that the SS
score should not be used as an image quality score since it is basically a SS score.
Stein et al. found that SS outperformed signal-to-noise ratio (SNR) in terms of poor
image discrimination [42]. SNR is a standard parameter used to objectively evaluate the quality of acquired images. Stein et al. suggested that SS possibly provides
insight into how operators subjectively assess OCT images, and stated that SS is a