Pediatric MDCT

Rationale and Objectives

The purpose of this study was to evaluate the effect of reduced tube current (dose) on lung nodule detection in pediatric multidetector array computed tomography (MDCT).

Materials and Methods

The study included normal clinical chest MDCT images of 13 patients (aged 1–7 years) scanned at tube currents of 70 to 180 mA. Calibrated noise addition software was used to simulate cases as they would have been acquired at 70 mA (the lowest original tube current), 35 mA (50% reduction), and 17.5 mA (75% reduction). Using a validated nodule simulation technique, small lung nodules of 3 to 5 mm in diameter were inserted into the cases, which were then randomized and rated independently by three experienced pediatric radiologists for nodule presence on a continuous scale ranging from zero (definitely absent) to 100 (definitely present). The observer data were analyzed to assess the influence of dose on detection accuracy using the Dorfman-Berbaum-Mets method for multiobserver, multitreatment receiver-operating characteristic (ROC) analysis and the Williams trend test.

Results

The areas under the ROC curves were 0.95, 0.91, and 0.92 at 70, 35, and 17.5 mA, respectively, with standard errors of 0.02 and interobserver variability of 0.02. The Dorfman-Berbaum-Mets method and the Williams trend test yielded P values for the effect of dose of .09 and .05, respectively.

Conclusion

Tube current (dose) has a weak effect on the detection accuracy of small lung nodules in pediatric MDCT. The effect on detection accuracy of a 75% dose reduction was comparable to interobserver variability, suggesting a potential for dose reduction.

Because of the superior resolution of modern multidetector array computed tomography (MDCT), chest computed tomography (CT) examination for the detection of lung nodules is often standard for pediatric cancer staging and surveillance. In such examinations, the presence of even one small lung nodule may have tremendous prognostic and therapeutic implications . However, chest CT involves the irradiation of many radiosensitive organs, including the thyroids, lungs, breasts, stomach, esophagus, and bone marrow; these organs are at risk for radiation-induced cancer later in life . Therefore, reducing radiation dose from chest CT is an important area of investigation .

Several previous studies on the pediatric population have suggested that diagnostic-quality images of the lung could be obtained at significantly reduced tube currents. However, those studies were preference based; images at reduced tube currents were evaluated by assigning subjective image quality scores to known anatomic structures. The results of such studies do not necessarily reflect the actual performance of radiologists in terms of lung nodule detection at reduced tube currents. Performance-based evaluations have been hindered by two major challenges: (1) the low occurrence of isolated small lung nodules in the pediatric population makes it difficult to research with real lung nodules, and (2) ethical concerns prohibit repeated scans to be performed on the same patients at different tube currents.

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Materials and methods

Clinical Cases

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Noise Simulation

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To enlarge the scale of our study, three copies of each case were made to create a total of 117 series (13 patients × 3 dose levels × 3 copies) to serve as background for nodule simulation.

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Nodule Simulation

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Observer Experiment

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Data Analysis

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Results

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$Figure 2, Receiver-operating characteristic curves for (a) observer 1, (b) observer 2, and (c) observer 3 at three nominal tube current (dose) levels: 70, 35, and 17.5 mA. FPF, false-positive fraction; TPF, true-positive fraction.$

Table 1

Areas Under the Receiver-Operating Characteristic Curves (±0.02)

Observer Tube Current (mA) 1 2 3 Average 70 0.97 0.97 0.92 0.95 35 0.89 0.93 0.91 0.91 17.5 0.94 0.91 0.91 0.92

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Discussion

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Conclusion

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Appendix

Evaluation and calibration of GE noise addition tool

Evaluation Methods

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Calibration

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ln(σreal)=α0+α1d+α2ln(mAreal)+α3d2+α4ln2(mAreal)+α5dln(mAreal) ln

(

real

)

(

real

)

(

real

)

(

real

)

and

ln(σsim)=β0+β1d+β2ln(mAsim)+β3d2+β4ln2(mAsim)+β5dln(mAsim), ln

(

sim

)

(

sim

)

(

sim

)

(

sim

)

using commercial surface fitting software (OriginPro 8 version 8.0725; OriginLab Corporation, Northampton, MA). In the above equations, σ real and σ sim are the real and simulated noise magnitude, mA real and mA sim are the real and simulated tube currents, and d is phantom diameter. Coefficients α i and β i ( i = 0, 1,…, 5) extracted from the fits were then used to calculate the nominal simulation tube current that provided the same noise magnitude as that at the desired actual tube current or, conversely, to calculate the actual tube current that was represented by a nominal simulation tube current inputted to the noise tool. After tube current calibration, the accuracy of the simulated noise magnitude was reevaluated.

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Evaluation and Calibration Results

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Table A1

Scan Parameters Used to Acquire Images From Six Water Phantoms ∗

Bowtie Filter Diameter (cm) Pitch Slice Thickness (mm) † DFOV (cm) Small 12.7 0.984 3.75 20 15.3 0.984 3.75 25 17.8 1.375 5 25 20.0 1.375 5 25 Medium 17.8 1.375 5 25 20.0 1.375 5 25 23.3 1.375 5 36 27.0 1.375 5 32 Large 20.0 1.375 5 25 23.3 1.375 5 36 27.0 1.375 5 32

DFOV, display (reconstruction) field of view.

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Table A2

Minimum Tube Current-Time Product (mAs) ∗

Tube Potential (kVp) Bowtie Filter Diameter (cm) 80 100 120 140 Small 12.7 4 4 4 4 15.3 8 4 4 4 17.8 8 8 4 4 20.0 16 8 4 4 Medium 17.8 16 8 4 4 20.0 24 8 4 4 23.3 24 16 8 4 27.0 56 16 8 4 Large 20.0 32 16 8 4 23.3 32 16 8 4 27.0 56 24 8 8

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Table A3

Coefficients of Polynomial Equations A1 and A2 Describing Real and Simulated Noise in Images of Water Phantoms as Functions of Phantom Diameter and Tube Current

Noise Real ∗ Bowtie Filter Tube Potential Coefficient Adjusted R 2 α 0 α 1 α 2 α 3 α 4 α 5 Small 80 4.42809 0.01476 −0.63593 0.00742 0.05724 −0.02681 0.990 100 4.05954 −0.00583 −0.52538 0.00490 0.01714 −0.00877 0.993 120 3.97994 −0.02059 −0.52299 0.00471 0.01276 −0.00576 0.995 140 3.94392 −0.02886 −0.52743 0.00440 0.00723 −0.00249 0.996 Medium 80 1.64157 0.26954 −0.42099 −0.00048 0.03194 −0.02331 0.975 100 1.54965 0.28661 −0.61874 −0.00187 0.05493 −0.02037 0.986 120 1.24914 0.27993 −0.55677 −0.00275 0.03529 −0.01306 0.990 140 1.26746 0.25753 −0.51762 −0.00282 0.02260 −0.00886 0.993 Large 80 10.17070 −0.12202 −1.94568 0.00783 0.18631 −0.02452 0.998 100 5.52959 −0.05413 −0.56413 0.00526 0.05182 −0.02125 0.999 120 4.96463 −0.03127 −0.54763 0.00382 0.03951 −0.01467 0.999 140 4.66454 −0.01537 −0.56091 0.00271 0.02667 −0.00824 0.999 Simulated β 0 β 1 β 2 β 3 β 4 β 5 Small 80 5.32980 −0.09420 −0.76424 0.00530 0.02829 0.00722 0.993 100 5.07604 −0.07876 −0.61616 0.00394 −0.00854 0.01022 0.995 120 4.63546 −0.05051 −0.61640 0.00314 −0.00548 0.00891 0.994 140 4.47683 −0.05519 −0.64763 0.00343 0.00350 0.00791 0.994 Medium 80 5.03943 0.03963 −0.99079 −0.00007 0.02920 0.01576 0.993 100 2.76751 0.18154 −0.72131 −0.00265 0.01716 0.00602 0.996 120 2.17034 0.21300 −0.70031 −0.00336 0.01650 0.00520 0.997 140 3.25378 0.13269 −0.84735 −0.00227 0.02278 0.01020 0.997 Large 80 7.45078 −0.22402 −0.93278 0.00613 0.05620 0.01045 0.997 100 4.80621 0.00321 −0.86714 0.00105 0.05076 0.00580 0.999 120 4.18627 0.02800 −0.79986 0.00060 0.04929 0.00331 0.999 140 3.77092 0.04403 −0.79241 0.00027 0.05152 0.00289 0.999

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FLOAT NOT FOUND

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Pediatric MDCT

Rationale and Objectives

Materials and Methods

Results

Conclusion

Materials and methods

Clinical Cases

Noise Simulation

Nodule Simulation

Observer Experiment

Data Analysis

Results

Discussion

Conclusion

Appendix

Evaluation and calibration of GE noise addition tool

Evaluation Methods

Calibration

Evaluation and Calibration Results

References

Further Reading

Atlas of Brain Function

Breast Mass Segmentation in Mammography Using Plane Fitting and Dynamic Programming

Computed Radiography-based Mammography with 50-μm Pixel Size