B. Rekha Reddy1, C. Varatharaj2,
M. Ravikumar3, K.M.Ganesh4
1Dr.B. Rekha Reddy, Research Scholar, 2 Dr. C. Varatharaj, Assistant
Professor, 3 Dr. M. Ravikumar, Professor, 4 Dr. K.M. Ganesh,
Professor, all authors are affiliated with Department of Radiation Physics,
Kidwai Memorial Institute of Oncology, Hosur Road, Bangalore, Karnataka, India
Corresponding
Author: Dr. C. Varatharaj, Email:
drvaratharaj@gmail.com
Abstract
Objective: In Intensity
Modulated Radiotherapy (IMRT) planning, optimization is a computational
problem, potentially susceptible to noise and artifacts (high frequency spatial
fluctuations) producing sharp fluence peaks and valleys in millimetric spatial
scale. A solution to this problem is to smooth the beam profiles. Methods and Materials: In the
Eclipse TPS, fluence smoothing is achieved within the objective function of the
inverse treatment planning systems. Plans were developed for a 6 MV photon beam
from a Varian Clinac-DHX equipped with a 120 leaves MLC. Total of 160 dose
plans were compared and fed into the analysis process. The dose plan quality
has been analysed in terms of Statistical computation by means of two-sided
paired t-test between two smoothening levels (s25 and s75) in terms of
Homogeneity-Index, Conformity-Index, Target dose coverage and OAR dose
differences in terms of max, min and mean doses. Results: From our present study on the influence of
smoothening of fluence levels in IMRT plans results, there was a reduction in
total MU’s with no significant statistical variation in terms of mean
differences of HI Index, CI index, PTV coverages, OAR doses. Moreover, the
reduction in MU’s will help the less head leakage dose hence the lower whole-body
dose, which will help the patient to reduce the chances for secondary
malignancies. Conclusion:
Hence, we conclude that, higher fluence smoothening levels with acceptable
difference in target coverage and minimum variation of OAR should be selected
for the execution.
Keywords:
Radiotherapy, IMRT, Fluence, OAR
Author Corrected: 24th July 2018 Accepted for Publication: 27th July 2018
Introduction
Intensity-modulated
radiation therapy (IMRT) is rapidly gaining acceptance as a practical and
efficient method for generating dose distributions whose conformality and
critical organ sparing far exceed that of traditional 3D-CRT [1]. It is argued
that the increased conformality will lead to improved local control and higher
long-term survival rates as well as better quality of life for the patient.
IMRT consists of two key components: an inverse planning or optimization
algorithm to calculate the ‘‘optimal’’ beam profiles and a delivery system to
generate them. In the process of optimization, algorithm is a mathematical
objective function, which is an attempt to quantify the clinical objectives and
assign a numerical score to each plan. Literature offers a huge variety of studies, at planning or clinical
level, where a plethora of inverse planning algorithms have been investigated
[2-5] to explore IMRT performances under several points of view.
The optimisation
process is a computational problem, potentially susceptible to noise and
artifacts (high frequency spatial fluctuations) producing sharp fluence peaks
and valleys in millimetric spatial scale. [6] A solution to this problem is to
smooth the beam profiles. There are two methods of doing so: a) smoothing can
be applied outside the objective function, i.e., after the ‘‘optimal’’ profile has
been produced by the optimization process, or b) the smoothness of the profiles
can be included in the objective function as a criterion in the optimization
process. The first method is easier to implement and allows the use of
different smoothing algorithms and filters. However, it suffers from a
fundamental drawback: as smoothing is applied outside the objective function,
its dosimetric effect is not considered in the optimization process. Therefore,
the method cannot differentiate between those features of the beam profile
(peaks, valleys, and gradients) that are clinically relevant and those that are
not, i.e., those that are computational artifacts. In the second method, in
contrast, the ‘‘unsmoothness’’ of the profile negatively affects the cost function,
so that its dosimetric effect is incorporated in the optimization process [7].
In the present
study, we investigated the interplay between fluence complexity, dose
calculation algorithms, dose calculation spatial resolution and delivery
characteristics (monitor units, and dose delivery against dose prediction
agreement). A sample set of complex
planning cases were selected and tested using a commercial treatment planning
system capable of inverse optimization and equipped with tools to tune fluence
smoothness.
Materials and Methods
Planning Design: Plans were designed using the Eclipse TPS from Varian (release 8.6.11)
and its inverse Dose Volume Optimizer (DVO). Plans were developed for a 6 MV
photon beam from a Varian Clinac-DHX
equipped with a 120 leaves MLC at our Institute. In the Eclipse TPS,
fluence smoothing is achieved within the objective function of the inverse
treatment planning systems. Ten IMRT
planning cases (seven head and neck and three cervix) were selected as
representative of demanding planning requirements. All the beams were coplanar.
Leaf sequencing and delivery are based on the dynamic sliding window technique.
Fluence Description: In Eclipse, optimal fluence smoothing is part of
the DVO algorithm and it is performed along two directions: X, parallel to the
MLC movement and Y, orthogonal to it. Smoothing is applied at each optimisation
iteration by adding two smoothness-related planning objectives in the cost
function that account for the difference between neighbouring fluence values.
To appraise the effectiveness of fluence smoothing and its interplay with other
planning variables, the study was organised performing full optimisation and
dose calculation for all the combinations of the following three variables:
1)Smoothing parameters: X- and Y- Smooth described above are the
smoothening parameter ‘s’ in the following were varied simultaneously and set to 25, 50,
75 and 100 (s25, s50, s75 and s100 in the following) being the higher the
values the higher the smoothing of the fluence patterns. Routinely, in clinical
practice, X- and Y- Smooth are set in the range 0–100.
2) Dose calculation algorithm: two algorithms were used: the
Single Pencil Beam Algorithm (PBC), and the newly introduced
convolution/superposition algorithm Anisotropic Analytical Algorithm (AAA).
3) Spatial resolution of dose calculation matrix: two grids were
used: 2.5 (the minimum grid for PBC) and 5 mm. 2.5 mm is also the internal grid
size used by Eclipse to compute and store fluences.
For each case (a combination of the three above variables, for a total
of 16 plans per case) optimisation was carried out using a fixed set of dose
volume objectives. Total of 160 dose plans were compared and fed into the
analysis process.
Statistical Computation methods: The dose plan quality has been analysed in terms of
mean differences (Statistical computation by means of two-sided paired t-test)
between two smoothening levels (s25 and s75) in terms of Homogeneity index (HI
index), Conformity index (CI Index), Target dose coverages (Maximum and Minimum
dose differences) and OAR dose differences in terms of Max, min and Mean doses.
(OAR1 was spinal cord for H&N plans and bladder for cervix cases. OAR 2 was
brain stem and rectum for H&N and Cervix cases respectively). Delivery
reliability was investigated by means of standardised pre-treatment
verification methods using I’matriXX for s25 and s75 fluence levels with 0.25
spatial resolutions for both PBC and AAA algorithms, therefore a total of 40
pre-treatment verification plans were calculated in Eclipse TPS and compared
with measured fluence.
Results
In comparison of monitor units among different smoothening levels with respect to s25 level as 100%, the monitor units reduces while increasing the fluence levels. The Figure 1. and 2 shows the monitor unit comparison between different fluence levels for the grid size of 0.25 and 0.5 respectively. In the combination of PBC algorithm with 0.25 grid size, the total mean MU decreases from 1143±211 to 992±210 for s25 and s100 respectively. Similarly, AAA with 0.25 has reduced from 1161±210 to 999±241 for s25 and s100 respectively. For the grid size of 0.5, PBC algorithm it was varied from 1172±234 to 979±220 and for AAA algorithm the variation was 1202±230 to 993±238 for the smoothening levels of s25 and s100 respectively. The comparison between the grid size of 0.25 and 0.5 for PBC and AAA algorithms were shown in Figure 3.
Figure-1: Comparison of PBC and AAA calculation algorithm
for the grid
size of 0.25 in terms of monitor units between different fluence
smoothening
levels
Figure- 2: Comparison
of PBC and AAA calculation algorithm for the grid
size of 0.5 in terms of monitor units between different fluence
smoothening
levels.
Figure-
3: Comparison between the calculation grid size of 0.25 and 0.5 for PBC and AAA algorithms in terms of
monitor units.
Table-1: Difference of HI index
between fluence smoothening levels of s25 and s75 for PBC and AAA algorithms
for the grid size of 0.25.
S. No. |
PBC |
AAA |
||||
s25
(%) |
s75
(%) |
Difference |
s25
(%) |
s75
(%) |
Difference |
|
1 |
5.39 |
5.32 |
0.07 |
6.77 |
6.53 |
0.24 |
2 |
5.71 |
6.52 |
-0.81 |
7.60 |
8.04 |
-0.44 |
3 |
6.61 |
6.65 |
-0.04 |
7.07 |
7.15 |
-0.08 |
4 |
6.01 |
6.63 |
-0.62 |
6.46 |
7.29 |
-0.83 |
5 |
5.50 |
5.58 |
-0.08 |
8.38 |
8.53 |
-0.15 |
6 |
6.01 |
5.97 |
0.04 |
6.53 |
6.37 |
0.16 |
7 |
5.24 |
5.04 |
0.20 |
8.98 |
8.90 |
0.08 |
8 |
7.08 |
7.17 |
-0.09 |
7.30 |
7.23 |
0.07 |
9 |
5.55 |
5.58 |
-0.03 |
5.98 |
6.00 |
-0.02 |
10 |
2.84 |
2.74 |
0.10 |
2.60 |
2.60 |
0.00 |
MEAN |
-0.13 |
|
-0.10 |
|||
SD |
0.33 |
0.32 |
||||
p value |
0.25 |
0.36 |
Table-2: Difference of HI index
between fluence smoothening levels of s25 and s75 for PBC and AAA algorithms
for the grid size of 0. 5.
S. No. |
PBC |
AAA |
||||
s25
(%) |
s75
(%) |
Difference |
s25
(%) |
s75
(%) |
Difference |
|
1 |
5.21 |
5.00 |
0.21 |
9.04 |
8.81 |
0.23 |
2 |
5.87 |
6.98 |
-1.11 |
9.80 |
10.78 |
-0.98 |
3 |
6.97 |
6.47 |
0.50 |
7.31 |
7.09 |
0.23 |
4 |
6.01 |
7.17 |
-1.16 |
7.98 |
9.18 |
-1.20 |
5 |
6.20 |
6.43 |
-0.23 |
11.37 |
11.68 |
-0.31 |
6 |
5.80 |
5.76 |
0.04 |
7.01 |
7.01 |
0.00 |
7 |
5.29 |
5.11 |
0.18 |
6.27 |
6.24 |
0.03 |
8 |
7.43 |
7.39 |
0.04 |
9.28 |
9.29 |
-0.01 |
9 |
5.76 |
5.78 |
-0.02 |
7.40 |
7.33 |
0.07 |
10 |
2.94 |
3.00 |
-0.06 |
3.10 |
3.04 |
0.06 |
MEAN |
-0.16 |
|
-0.19 |
|||
SD |
0.55 |
0.50 |
||||
p value |
0.38 |
0.26 |
Table-3: Difference of CI index
between fluence smoothening levels of s25 and s75 for PBC and AAA algorithms
for the grid size of 0.25.
S. No. |
PBC |
AAA |
||||
s25
(%) |
s75
(%) |
Difference |
s25
(%) |
s75
(%) |
Difference |
|
1 |
2.29 |
2.36 |
-0.07 |
2.27 |
2.34 |
-0.07 |
2 |
1.75 |
1.72 |
0.03 |
2.09 |
2.12 |
-0.03 |
3 |
1.40 |
1.41 |
-0.02 |
1.38 |
1.38 |
0.00 |
4 |
1.20 |
1.17 |
0.03 |
1.18 |
1.16 |
0.02 |
5 |
1.23 |
1.26 |
-0.03 |
1.24 |
1.27 |
-0.03 |
6 |
1.99 |
2.00 |
-0.01 |
1.96 |
1.98 |
-0.02 |
7 |
1.40 |
1.43 |
-0.03 |
1.44 |
1.46 |
-0.02 |
8 |
1.11 |
1.11 |
0.00 |
1.10 |
1.10 |
0.00 |
9 |
1.09 |
1.08 |
0.01 |
1.08 |
1.08 |
0.00 |
10 |
1.96 |
2.04 |
-0.08 |
1.96 |
2.04 |
-0.08 |
MEAN |
-0.02 |
-0.02 |
||||
SD |
0.04 |
0.03 |
||||
p value |
0.20 |
0.04 |
Table-4: Difference of CI index
between fluence smoothening levels of s25 and s75 for PBC and AAA algorithms
for the grid size of 0. 5.
S. No. |
PBC |
AAA |
||||
s25 (%) |
s75 (%) |
Difference |
s25 (%) |
s75 (%) |
Difference |
|
1 |
2.23 |
2.35 |
-0.12 |
2.21 |
2.27 |
-0.06 |
2 |
1.79 |
1.79 |
0.00 |
2.35 |
2.28 |
0.07 |
3 |
1.39 |
1.39 |
0.00 |
1.40 |
1.39 |
0.00 |
4 |
1.18 |
1.16 |
0.02 |
1.14 |
1.14 |
0.00 |
5 |
1.22 |
1.23 |
-0.01 |
1.21 |
1.25 |
-0.04 |
6 |
1.93 |
1.92 |
0.01 |
1.93 |
1.95 |
-0.02 |
7 |
1.40 |
1.41 |
-0.01 |
1.44 |
1.45 |
-0.01 |
8 |
1.10 |
1.10 |
0.00 |
1.08 |
1.08 |
0.00 |
9 |
1.08 |
1.09 |
-0.01 |
1.06 |
1.06 |
0.00 |
10 |
1.95 |
2.10 |
-0.15 |
1.97 |
2.14 |
-0.17 |
MEAN |
-0.03 |
-0.02 |
||||
SD |
0.06 |
0.06 |
||||
p value |
0.19 |
0.29 |
Table-5: Target minimum dose
difference between the smoothening levels of s25 and s75 for PBC and AAA
algorithms for the grid size of 0.25.
S.No. |
PBC |
AAA |
||||
s25 (%) |
s75 (%) |
Difference |
s25 (%) |
s75 (%) |
Difference |
|
1 |
89.20 |
89.30 |
-0.10 |
89.00 |
88.40 |
0.60 |
2 |
82.10 |
80.50 |
1.60 |
84.30 |
83.60 |
0.70 |
3 |
88.70 |
89.80 |
-1.10 |
86.30 |
86.70 |
-0.40 |
4 |
83.80 |
83.30 |
0.50 |
84.20 |
83.70 |
0.50 |
5 |
77.60 |
77.70 |
-0.10 |
78.60 |
79.60 |
-1.00 |
6 |
88.20 |
87.80 |
0.40 |
87.60 |
87.80 |
-0.20 |
7 |
92.90 |
92.30 |
0.60 |
87.60 |
87.30 |
0.30 |
8 |
81.70 |
81.50 |
0.20 |
81.30 |
80.50 |
0.80 |
9 |
84.50 |
84.80 |
-0.30 |
85.40 |
85.50 |
-0.10 |
10 |
95.50 |
95.70 |
-0.20 |
95.40 |
95.70 |
-0.30 |
MEAN |
0.15 |
0.09 |
||||
SD |
0.71 |
0.58 |
||||
p value |
0.52 |
0.64 |
Table-6: Target minimum dose
difference between the smoothening levels of s25 and s75 for PBC and AAA
algorithms for the grid size of 0.5.
S.No. |
PBC |
AAA |
||||
s25 (%) |
s75 (%) |
Difference |
s25 (%) |
s75 (%) |
Difference |
|
1 |
88.40 |
88.50 |
-0.10 |
83.90 |
84.80 |
-0.90 |
2 |
82.00 |
80.20 |
1.80 |
75.00 |
73.40 |
1.60 |
3 |
85.40 |
84.40 |
1.00 |
84.60 |
86.20 |
-1.60 |
4 |
84.10 |
83.50 |
0.60 |
83.50 |
84.10 |
-0.60 |
5 |
78.30 |
77.50 |
0.80 |
75.80 |
76.20 |
-0.40 |
6 |
87.90 |
88.10 |
-0.20 |
87.80 |
86.40 |
1.40 |
7 |
90.50 |
90.20 |
0.30 |
87.40 |
87.20 |
0.20 |
8 |
81.50 |
81.20 |
0.30 |
76.60 |
77.10 |
-0.50 |
9 |
83.20 |
83.60 |
-0.40 |
84.20 |
84.10 |
0.10 |
10 |
95.90 |
95.80 |
0.10 |
95.40 |
95.50 |
-0.10 |
MEAN |
0.42 |
-0.08 |
||||
SD |
0.66 |
0.98 |
||||
p value |
0.07 |
0.80 |
Table- 7: Comparison of percentage
of pixels passing gamma between the smoothening levels of s25 and s75 for PBC
and AAA algorithms for the grid size of 0.25 using I’MatriXX fluence
measurement.
S.No. |
PBC |
AAA |
||
s25 (%) |
s75 (%) |
s25 (%) |
s75 (%) |
|
1 |
97.09 |
97.58 |
98.26 |
97.13 |
2 |
95.70 |
96.22 |
98.49 |
97.37 |
3 |
95.69 |
96.47 |
96.82 |
96.56 |
4 |
96.80 |
97.53 |
97.55 |
98.14 |
5 |
97.37 |
97.43 |
98.24 |
97.62 |
6 |
95.93 |
95.75 |
96.64 |
96.83 |
7 |
99.41 |
99.58 |
99.75 |
99.81 |
8 |
98.20 |
98.62 |
97.89 |
99.02 |
9 |
97.78 |
97.85 |
96.40 |
99.11 |
10 |
96.98 |
96.70 |
98.07 |
98.65 |
MEAN |
97.10 |
97.37 |
97.81 |
98.02 |
SD |
1.17 |
1.15 |
1.00 |
1.09 |
p value |
0.04 |
0.57 |
The mean difference in HI (%) were 0.07±0.31
(p=0.49) and 0.07±0.29 (p=0.47) for 0.25 calculation grid size of PBC and AAA
respectively as shown in Table 1. Similarly for the grid size of 0.5, the mean
differences were -0.16±0.55 (p=0.38) and -0.19±0.50 (p=0.26) as in Table 2. The
mean difference in CI index were -0.02±0.04 (p=0.20) and -0.02±0.03 (p=0.05)
for the grid size of 0.25 for PBC and AAA respectively as in Table 3. For the
grid size of 0.5, the changes were -0.03±0.06 (p=0.19) and -0.02±0.06 (p=0.29)
as shown in Table 4. The mean difference
(%) of target (PTV) minimum doses were 0.15±0.71 (p=0.52) and 0.09± 0.589(p=0.64)
for the grid sizes of 0.25 of PBC and AAA algorithms respectively as in Table
5. For the grid size of 0.5 the mean differences were 0.42±0.66 (p=0.07) and
-0.08±0.98 (p=0.80) as shown in Table 6. The quantitative analysis of delivery
accuracy from the I’matriXX measurements have been compared in terms of γ
analysis between s25 and s75 fluence levels for the grid size of 0.25 for PBC
and AAA algorithms as shown in table 7. The mean percentage of
pixel passing set gamma criterion of 3mm and 3% DD were 97.10±1.17 Vs 97.37±1.15 (p=0.05) for PBC and 97.81±1.00 Vs 98.02±1.09 (p=0.57) for AAA algorithms.
Discussion
Several authors suggested as a recommended solution to systematically
adopt planning tools and methods able to optimise smooth beam fluences [8-11];
Coselman et al [12] underlined that smoothing algorithms that are applied
post-optimisation, usually result in a degradation of the plan according to the
objective function while, when the smoothing is part of the objective function,
better results are obtainable.
In
this study we tried to evaluate the IMRT treatment planning in terms of change in total monitor units, plan
quality and delivery accuracy when the plan smoothening parameters are changed.
In
the Eclipse TPS, both structure-dose-priority weights and smoothing (X, Y)
penalty weights are embedded components within the objective function and,
thus, contribute to the total score for a particular plan. Therefore, by varying one set of parameters
relative to the other, the user can change the ratio of the two penalty
components to each other and, therefore, the relative contribution of the
smoothing penalty versus the structure-dose penalty. This is important to
realize for the Eclipse system because any characterization of the behavior of
various smoothing levels must, therefore, be associated with the range of
structure-dose-priority weights used for that plan.
Anger
et al. have evaluated the behavior of three ITPSs when varying smoothing
parameters and concluded that, Depending on the inverse treatment planning
system used, the potential benefits of optimizing fluence smoothing levels can
be significant, allowing for increases in either delivery efficiency or plan
conformality [13]. Webb proposed
[14], as a general rule of thumb for good IMRT practice, to avoid excessive
complexity and, as a metric to appraise the degree of modulation in a fluence
matrix, introduced the concept of Modulation Index, (MI) [15].
Excessive modulation leads to high numbers of MUs necessary to deliver
prescribed doses with potential
consequences on long term effects as secondary cancer induction [16], on
treatment time for individual fractions (possibly to relate to organ movement
and biological issues) and on radiation protection items. Excessive complexity
in the fluence could have a negative trade-off also against inter-fraction
tumour dynamics (e.g. hypoxic conditions, tumour stem cells migration, etc)
that could be incorporated in the planning strategies [17]. These
considerations should be linked to a rather old but still valid note of caution
published by Goitein and Niemierko [18] where they proved the principle that
the risk of treatment failure is more linked to dose deficits (severe
under-dosages to small volumes) rather than to small/moderate under-dosages to
larger volumes. A final concern that could raise from this study is the
possibility to determine a "proper" or "necessary" amount
of modulation necessary to obtain an high quality plan. This can be hardly
achieved by any study since it is obvious that, visible from the data shown
here, the relation between smoothening of fluence with their outcomes would
helpful for anyone.
From
the Varian Medical Systems user manual for Eclipse, it was evaluated “no
smoothing” (X = 0, Y = 0), “moderate smoothing” (X = 40, Y = 30), and “heavy
smoothing” (X = 90, Y = 90) for a head and neck plan phantom [19]. Although the
actual structure-dose-priority weights used were not specified for this
particular analysis, the authors recommended using between 0 and 100 because
they historically used this range in an older ITPS (CADPlan). This recommendation
was not reported to be based on any empirical testing of values outside that
range. The plan with “no smoothing” resulted in good target coverage, but they
noted “islands of hot spots.” From our present study it was noted that, there
was a maximum reduction of about 18% in total monitor units while moving from
s25 to s100 level . It was found that there was no statistical difference in HI
index, CI index , PTV coverages , OAR’s
like Spinal cord, Bladder, Brainstem & Rectum doses when the
smoothening parameters were changed.
Conclusion
Commercially
available intensity-modulated radiation therapy, inverse treatment planning
systems (ITPS) typically include a smoothing function which allows the user to
vary the complexity of delivered beam fluence patterns. Fluence complexity is strongly interconnected to
the quality and efficiency of dose delivery (and consequently also to radiation
protection issues). There have been limited publications related to variations
of fluence levels in IMRT planning and its outcome. Hence, an attempt was made
to find the variation between different levels of fluence by the combination of
calculation grid size, and different algorithms.
From our present
study on the influence of various levels of fluence smoothening in IMRT plan
results, it was found that when we increase the smoothening level to higher
level, there was a reduction in total MU’s with no significant statistical
variation in terms of mean differences of HI Index, CI index, PTV coverage, OAR
doses. Moreover, in IMRT the reduction in Monitor units will help in reducing
head leakage dose hence the lower whole body dose, which will help the patient
to reduce the chances for secondary malignancies. Hence, we conclude that,
higher fluence smoothening levels with acceptable difference in target coverage
and minimum variation of OAR should be selected for the execution.
Based
on the our findings, we recommended the workflow for eclipse TPS for IMRT to
best exploit the fluence-smoothing features of the system and also this also be
a guideline regarding what can be expected when smoothing/efficiency is altered
within the Eclipse treatment planning systems.
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