3D printer applications in the biomedical sciences and medical imaging are expanding and will have an increasing impact on the practice of medicine. Orthopedic and reconstructive surgery has been an obvious area for development of 3D printer applications as the segmentation of bony anatomy to generate printable models is relatively straightforward. There are important issues that should be addressed when using 3D printed models for applications that may affect patient care; in particular the dimensional accuracy of the printed parts needs to be high to avoid poor decisions being made prior to surgery or therapeutic procedures. In this work, the dimensional accuracy of 3D printed vertebral bodies derived from CT data for a cadaver spine is compared with direct measurements on the ex-vivo vertebra and with measurements made on the 3D rendered vertebra using commercial 3D image processing software. The vertebra was printed on a consumer grade 3D printer using an additive print process using PLA (polylactic acid) filament. Measurements were made for 15 different anatomic features of the vertebral body, including vertebral body height, endplate width and depth, pedicle height and width, and spinal canal width and depth, among others. It is shown that for the segmentation and printing process used, the results of measurements made on the 3D printed vertebral body are substantially the same as those produced by direct measurement on the vertebra and measurements made on the 3D rendered vertebra.
To obtain clearance for the use of a new flat-panel indirect detection imager, the FDA required the manufacturer to provide evidence of the image quality. To this end, two sets of observer studies were conducted, one in which images from the detector was compared side by side with images from an approved detector, and a second set in which each individual image was scored for image contrast, noise, and resolution. Statistical analysis of the results showed that there was not a significant difference in the image quality produced by the two detectors. FDA 510k clearance was granted in May 2013.
The goal was to analyze the influence of blurring of artificial lesions on observer performance during AFC experiments
in chest CT images. Lesion images were generated by scanning Teflon rods of multiple sizes (3/16", 1/4", 5/16", 3/8",
and 1/2") in a General Electric VCT scanner. Images were reconstructed using Bone and Detail reconstruction
algorithms and cropped for use in AFC experiments. Three sets of artificial lesions (simple disks) were generated
mathematically at the same sizes as the Teflon lesions, with two of the sets blurred with 3x3 and 5x5 averaging kernels.
All lesions were scaled to have the same maximum intensity. Approximately 180 normal chest CT images (both Bone
and Detail algorithm) were collected under IRB exemption for use in 2-AFC experiments. Two observers conducted
AFC experiments using the Teflon lesions with the appropriate CT images, and using the artificial lesions in both sets of
CT images. A performance metric was calculated that allowed comparison of experimental results. For Bone algorithm
images, the Teflon and un-blurred lesions produced equivalent performance. Performance was significantly worse using
the blurred lesions. For the Detail algorithm images, un-blurred lesion performance was significantly better than with the
Teflon lesion. The performance using the 3x3-blurred lesions was the closest to the Teflon lesion performance, though it
was slightly worse. Using these results, it is possible to design artificial lesions of any size for use in AFC experiments
that will result in observer performance equivalent to that when using lesions derived from physical phantoms.
In this study, we evaluated the ability of an observer to identify abnormal foci on CT and how that ability is
affected by changing the search field size from a whole abdomen to the liver region alone. A 2-Alternate
Forced Choice (2 AFC) experimental paradigm was used to quantify observer detection performance. Each
AFC experiment yielded the intensity needed to achieve 92% accuracy in lesion detection (I92%).
Abdominal images were obtained at an x-ray tube voltage of 120 kV with a CTDIvol of 20 mGy. Circular
lesions were generated by projecting spheres onto the image plane, followed by blurring function. Five
lesion sizes (5 mm, 7 mm, 10 mm, 12 mm, and 15 mm), and four readers who were extensively trained in
AFC methodology, were used in the 2AFC experiments. Each experiment was repeated 4 times to improve
the experimental precision, as well as to provide an estimate of experimental uncertainty. For each
observer, the experimental order of the 40 experiments was randomized to eliminate learning curve and/or
observer fatigue. We measured contrast detail slopes for both Abdomen and Liver search field size, and
determined ratio of I92% value for Abdomen search field to the corresponding I92% for the Liver search field
(i.e., Rabd:liv). Values of Rabd:liv provides quantitative indicator of the relative difficulty of detection lesions
in the whole Abdomen relative to lesion detection restricted to the Liver. The slope of the contrast detail
curve for the Abdomen search field was -0.03, whereas the corresponding slope for the Liver search field
was -0.18. Rabd:liv ranged between 1.3 and 1.6, with an average of 1.4 ± 0.1. The value of Rabd:liv
monotonically increased from 1.35 for 5 mm lesions to nearly 1.6 for the largest 15 mm lesion. The results
of our study indicate that limiting the area of search to the liver on a CT of the abdomen improves the
detection of mass lesions. This finding is almost certainly related to the fact that the liver provides a
relatively homogenous background for identifying abnormalities, while the rest of the abdomen is much
more heterogeneous. The clinical relevance of our findings is that CT can have limitations for detection of
mass lesions outside of the liver, and sizes of masses necessary for detection are larger outside the liver
The purpose of this study was to quantify how reducing x-ray beam intensity (i.e., mAs) affects lesion
detection performance in abdominal CT examinations. A simulation package (Syngo Explorer) was used to
reconstruct 4-mm thick CT images of a patient undergoing a standard abdominal exam. Simulations were
performed at four x-ray beam intensities of 100%, 70%, 50%, and 25%. Four observers were used to
perform a series of two Alternate Forced Choice (2-AFC) experiments that measure the lesion contrast
(I92%) corresponding to a detection accuracy of 92%. Four lesion sizes were used ranging from 5 mm to 12
mm. Results were plotted as log(I92%) versus log(mAs) to quantify how changes in x-ray intensity affect
lesion detection, as well as log(I92%) versus log(size) to generate contrast-detail curves. The fitted slope of
noise in reconstructed images as a function of relative CT x-ray beam intensity was -0.25, which is about
half the value of -0.5 expected for an ideal quantum noise limited imaging system. For lesion sizes between
5 mm and 10 mm, slopes of log(I92%) versus log(mAs) curves were very similar for all four observers, and
ranged between -0.10 and -0.17. For 5 mm sized lesions, doubling the x-ray beam intensity improved
detection performance by about 13%, whereas for 7 and 10 mm lesions, doubling the x-ray intensity
improved detection performance by about 7%. For the 12 mm lesion there were no consistent patterns for
all four readers, which may be related to the lack of a standardized viewing distance. The average slope for
the four contrast detail curves was -0.41 ± 0.09, which is substantially less than the value of -1.0 predicted
for an ideal observer operating with a quantum noise limited images. For our abdominal CT images,
doubling of the lesion size resulted in improvements in lesion detection of ~ 30%.
The purpose of this work was to analyze the influence of background structure on the slopes of contrast-detail (CD)
curves in CT images acquired in uniform phantoms, anthropomorphic head phantoms, and in clinical head CT images.
Alternative forced-choice (AFC) studies were performed using CT images acquired in uniform (water) phantoms,
anthropomorphic (RANDO and ATOM) phantoms, and clinical head scans. The AFC experiments measure the lesion
contrast (I92%) that corresponds to 92% detection efficiency. The AFC experimental results were plotted as a function of
lesion size to produce CD curves, and the slopes of the curves determined when plotted on log-log axes. The Rose
model of detection predicts a slope of -1.0 for disk lesions in uniform backgrounds and white noise. CD curve slopes
showed a progression that depends on the complexity of the background structure in the CT images. For uniform water
phantom images, the slope averaged -0.9, which is close to that predicted by the Rose model. For the anthropomorphic
phantoms, the slope averaged -0.56, and for the patient scans the average slope was -0.20. The slope of CD curves
depends strongly on the background structure of the images in which the lesions are embedded, with increased
background structure leading to decreased CD curve slopes. The Rose model reasonably predicts the slopes for CD
curves acquired in uniform phantoms, but is a poor predictor of slopes in clinical head images.
The purpose of this study was to quantitatively evaluate the effect of reducing radiation dose (i.e., mAs) on
lesion detection in head CT examinations. We used a simulation package (Syngo Explorer) to reconstruct
5-mm thick CT images of the brain of one patient pertaining to the centrum semiovale, the basal ganglia,
and the sella turcica. Lesion detection was measured using two Alternate Forced Choice (2-AFC)
experiments that measure the lesion contrast (I92%) corresponding to a detection accuracy of 92%. Two
observers performed experiments to investigate detection of low contrast lesions with four sizes ranging
from 3 mm to 10 mm and at four x-ray beam intensities ranging from 105 mAs to 300 mAs. Results were
plotted as log[I92%] versus log[mAs], and the slopes were measured for each lesion size. Lowering the mAs
always reduced lesion detection performance in all images, and for all lesion sizes. Average slopes of the
I92% versus mAs curves were -0.23 for 3 mm lesions, -0.16 for 4.5 mm lesions, and ~-0.11 for the 7 and 10
mm lesions. For the smallest lesions investigated (3 mm), doubling the x-ray intensity improved lesion
detection performance by ~ 15%, whereas for the largest sized lesions (7 and 10 mm), doubling the tube
current improved lesion detection performance by ~ 7%. The observed improvements in detection
performance are markedly lower than predicted by the Rose model where a doubling of the tube current
would be expected to improve detection performance by 29% at all lesion sizes.
The purpose of this study was to generate contrast detail (CD) curves for low contrast mass lesions
embedded in images obtained in head and neck CT examinations. Axial head and neck CT slice images
were randomly chosen from patients at five different levels. All images were acquired at 120 kV, and
reconstructed using a standard soft tissue reconstruction filter. For each head CT image, we measured
detection of low contrast mass lesions using a 2 Alternate Forced Choice (2-AFC) experimental paradigm.
In an AFC experiment, an observer identifies the lesion location in one of two regions of interest. After
performing 128 sequential observations, it is possible to compute the lesion contrast corresponding to a
92% accuracy of lesion detection (i.e., I92%). Five lesion sizes were investigated ranging from 4 mm to 12.5
mm, with the experimental order randomized to eliminate learning curve as well as observer fatigue.
Contrast detail curves were generated by plotting log[I92%] versus log[lesion size]. Experimental slopes
ranged from ~ -0.1 to ~ -0.4. The slope of the CD curve was directly related to the complexity of the
anatomical structure in the head CT image. As the apparent anatomical complexity increased, the slope of
the corresponding CD curve was reduced. Results from our pilot study suggest that anatomical structure is
of greater importance than quantum mottle, and that the type of anatomical background structure is an
important determinant of lesion detection in CT imaging.
We evaluated three strategies for optimizing the x-ray tube voltage in chest CT examinations: (1) keeping patient dose
constant and maximizing contrast to noise ratios (CNR); (2) keeping CNR constant and minimizing patient effective
dose (E); (3) maximizing CNR2/E. Lung and soft tissue Hounsfield unit values, together with the corresponding image
noise, were measured in a Rando phantom at x-ray tube voltages between 80 and 140 kV. A CT dosimetry software
package (ImPACT) was used to compute effective doses as a function of CT x-ray tube voltage for adult patients
undergoing chest CT examinations. CNR and patient dose in chest CT examinations both increase with increasing x-ray
tube voltage at a fixed mAs. All optimization strategies provided similar qualitative results, which showed the best
imaging performance was achieved at the lowest x-ray tube voltage (80 kV). Optimization using constant CNR or
effective dose is preferred since these methods provide explicit choices of optimal kV/mAs combinations, as well as
quantitative data on how changing kV would modify CNR and/or patient dose. The CNR2/Dose figure of merit does not
offer explicit choices of kV/mAs for performing CT examinations, and changes in FOM value are more difficult to relate
to changes in imaging performance or patient dose.
In this study, we investigated differences in detection performance for twelve observers who each
generated a CT contrast detail curve. An anthropomorphic newborn phantom's abdomen was imaged using
a GE Light Speed CT scanner (4-slice). Alternate Forced Choice (AFC) experiments were performed with
lesions sizes ranging from 2.5 to 12.5 mm to determine the intensity needed to achieve 92% correct (I92%).
Following training, twelve readers consisting of (2 technologists, 4 college students, 4 medical students,
and 2 radiology residents) generated a single contrast detail curve. Eight readers produced approximately
linear contrast detail curves while the remaining four readers required a second order polynomial fit
because of reduced performance when detecting the largest (i.e., 12.5 mm) lesion. For the three smallest
lesions, the coefficient of variation between the twelve readers was ~12%, which increases with increasing
lesion size to ~23% for 12.5 mm lesion size. The ratio of the maximum I92% to minimum I92% values was
~1.6 for the smallest lesions, which increased to a factor of ~2.1 for the 12.5 mm lesion. Our results show
that minimizing inter-reader variability in our AFC experiments could be achieved by eliminating the
largest lesion that cause detection problems in one third of observers. The combined experimental data
showed that the slope of the contrast detail curve was -0.42, lower than the value of -1.0 predicted by the
Rose model, suggesting that the noise texture in CT associated with both quantum mottle and anatomic
structure is an important factor affecting detection of these lesions.
In this study, we investigated the effect of CT reconstruction filters in abdominal CT images of a male
anthropomorphic phantom. A GE Light Speed CT 4-slice scanner was used to scan the abdomen of an adult
Rando phantom. Cross sectional images of the phantom were reconstructed using four reconstruction
filters: (1) soft tissue with the lowest noise; (2) detail (relative noise 1.7); (3) bone (relative noise 4.5); and
(4) edge (relative noise 7.7). A two Alternate Forced Choice (AFC) experimental paradigm was used to
estimate the intensity needed to achieve 92% correct (i.e., I92%). Four observers measured detection
performance for five lesions with size ranging from 2.5 to 12.5 mm for each of these four reconstruction
filters. Contrast detail curves obtained in images of an anthropomorphic phantom were not straight lines,
but best fitted to a second order polynomial. Results from four readers show similar trends with modest
inter-observer differences with the measured coefficient of variation of the absolute performance levels of
~22%. All reconstruction filters had similar shaped contrast detail curves except for smallest details where
the frequency response of filters differed most significantly. Increasing the noise level always reduced
detection performance, and a doubling of image noise resulted in an average drop in detection performance
of ~20%. The key findings of this study are that (a) the Rose model can provide reasonable predictions as
to how changes in lesion size affect observer detection; (b) the shape of CT contrast detail curves is
affected only very slightly with reconstruction filter; (c) changes in reconstruction filter noise can predict
qualitative changes in observer detection performance, but are poor direct predictors of the quantitative
changes of imaging performance.
The purpose of this study was to investigate how output (mAs) and x-ray tube voltage (kV) affect lesion detection in CT imaging. An adult Rando phantom was scanned on a GE LightSpeed CT scanner at x-ray tube voltages from 80 to 140 kV, and outputs from 90 to 360 mAs. Axial images of the abdomen were reconstructed and viewed on a high quality monitor at a soft tissue display setting. We measured detection of 2.5 to 12.5 mm sized lesions using a 2 Alternate Forced Choice (2-AFC) experimental paradigm that determined lesion contrast (I) corresponding to a 92% accuracy (I92%) of lesion detection. Plots of log(I92%) versus log(lesion size) were all approximately linear. The slope of the contrast detail curve was ~ -1.0 at 90 mAs, close to the value predicted by the Rose model, but monotonically decreased with increasing mAs to a value of ~ -0.7 at 360 mAs. Increasing the x-ray tube output by a factor of four improved lesion detection by a factor of 1.9 for the smallest lesion (2.5 mm), close to the value predicted by the Rose model, but only by a factor of 1.2 for largest lesion (12.5 mm). Increasing the kV monotonically decreased the contrast detail slopes from -1.02 at 80 kV to -0.71 at 140 kV. Increasing the x-ray tube voltage from 80 to 140 kV improved lesion detection by a factor of 2.8 for the smallest lesion (2.5 mm), but only by a factor of 1.7 for largest lesion (12.5 mm). We conclude that: (i) quantum mottle is an important factor for low contrast lesion detection in images of anthropomorphic phantoms; (ii) x-ray tube voltage has a much greater influence on lesion detection performance than x-ray tube output; (iii) the Rose model only predicts CT lesion detection performance at low x-ray tube outputs (90 mAs) and for small lesions (2.5 mm).
The purpose of this study was to develop a concise way to summarize radiographic contrast detail curves.
We obtained experimental data that measured lesion detection in CT images of a 5-year-old
anthropomorphic phantom. Five lesion diameters (2.5 to 12.5 mm) were investigated, and contrast detail
(CD) curves were generated at each of five tube current-exposure time product (mAs) values using twoalternative
forced-choice (2-AFC) studies. A performance index for each CD curve was calculated as the
area under the curve bounded by the maximum and minimum lesion sizes, with this value being normalized
by the range of lesion sizes used. We denote this quantity, which is mathematically equal to the mean
value of the CD curve, as the contrast-detail performance index (PCD). This quantity is inspired by the area
under the curve (Az) that is used as a performance index in ROC studies, though there are important
differences. PCD, like Az, allows for the reduction in the dimensionality of experimental results, simplifying
interpretation of data while discarding details of the respective curve (CD or ROC). Unlike Az, PCD
decreases with increasing performance, and the range of values is not fixed as for Az (i.e. 0 < Az < 1). PCD
is proportional to the average SNR for the lesions used in the 2-AFC experiments, and allows relative
performance comparisons as experimental parameters are changed. For the CT data analyzed, the PCD
values were 0.196, 0.166, 0.146, 0.132, and 0.121 at mAs values of 30, 50, 70, 100, and 140, respectively.
This corresponds to an increase in performance (i.e. decrease in required contrast) relative to the 30 mAs
PCD value of 62%, 48%, 33%, and 18% for the 140, 100, 70, and 50 mAs data, respectively.
In this study, we investigated lesion detection using a computed radiography (CR) imaging system in the absence of any anatomical background. Parameters investigated were the lesion size, display window width, and radiation exposure. Uniform CR exposures were obtained for an air kerma at the receptor between approximately 1 μGy and 10 μGy, which are typical values for radiographic imaging. We added simulated spherical lesions ranging from 1 to 5.5 mm. Observer detection performance was measured using a 4 Alternate Forced Choice (4-AFC) method that determines the lesion intensity (contrast) needed to achieve 92% accuracy (I92%). We generated contrast detail curves (I92% vs lesion size) for the various radiation doses, and display contrast settings that were varied by a factor of approximately eight. All contrast-detail curves showed a linear relationship between detection threshold intensity (I92%) and lesion size. Measured contrast detail slopes were -0.65 at 1 μGy, and -0.71 at 10 μGy. Increasing the radiation exposure by a factor of ten improved detection of 1 mm size lesions by a factor of 2.25 and 5.5 mm lesions by a factor of 1.8. Decreasing display contrast by a factor of four reduced lesion visibility by ~25% for exposures > 5 μGy, and by ~10% for exposures of 1 μGy. Reductions in lesion visibility with lower display contrast were similar for all lesion sizes. Detection performance generally improves when the radiation dose and/or display contrast are increased. At the lowest radiation dose (1 μGy), display contrast is less important than at doses above 5 μGy.
The purpose of this study was to compare traditional and task dependent assessments of CT image quality. Chest CT examinations were obtained with a standard protocol for subjects participating in a lung cancer-screening project. Images were selected for patients whose weight ranged from 45 kg to 159 kg. Six ABR certified radiologists subjectively ranked these images using a traditional six-point ranking scheme that ranged from 1 (inadequate) to 6 (excellent). Three subtle diagnostic tasks were identified: (1) a lung section containing a sub-centimeter nodule of ground-glass opacity in an upper lung (2) a mediastinal section with a lymph node of soft tissue density in the mediastinum; (3) a liver section with a rounded low attenuation lesion in the liver periphery. Each observer was asked to estimate the probability of detecting each type of lesion in the appropriate CT section using a six-point scale ranging from 1 (< 10%) to 6 (> 90%). Traditional and task dependent measures of image quality were plotted as a function of patient weight. For the lung section, task dependent evaluations were very similar to those obtained using the traditional scoring scheme, but with larger inter-observer differences. Task dependent evaluations for the mediastinal section showed no obvious trend with subject weight, whereas there the traditional score decreased from ~4.9 for smaller subjects to ~3.3 for the larger subjects. Task dependent evaluations for the liver section showed a decreasing trend from ~4.1 for the smaller subjects to ~1.9 for the larger subjects, whereas the traditional evaluation had a markedly narrower range of scores. A task-dependent method of assessing CT image quality can be implemented with relative ease, and is likely to be more meaningful in the clinical setting.
The purpose of this study was to investigate how tissue x-ray attenuation coefficients, and their uncertainties, vary with x-ray tube voltage in different sized patients. Anthropomorphic phantoms (newborn, 10 year old, adult) were scanned a GE LightSpeed scanner at four x-ray tube voltages. Measurements were made of tissue attenuation in the head, chest and abdomen regions, as well as the corresponding noise values. Tissue signal to noise ratios (SNR) were obtained by dividing the average attenuation coefficient by the corresponding standard deviation. Soft tissue attenuation coefficients, relative to water, showed little variation with patient location or x-ray voltage (< 0.5%), but increasing the x-ray tube voltage from 80 to 140 kV reduced bone x-ray attenuation by ~14%. All tissues except adult bone showed a reduction of noise with increasing x-ray tube voltage (kV); the noise was found to be proportional to kVn and the average value of n for all tissues was -1.19 ± 0.57. In pediatric patients at a constant x-ray tube voltage, SNR values were approximately independent of the body region, but the adult abdomen soft tissue SNR values were ~40% lower than the adult head. SNR values in the newborn were more than double the corresponding SNR soft tissue values in adults. SNR values for lung and bone were generally lower than those for soft tissues. For soft tissues, increasing the x-ray tube voltage from 80 to 140 kV increased the SNR by an average of ~90%. Data in this paper can be used to help design CT imaging protocols that take into account patient size and diagnostic imaging task.
We have developed a patient dosimetry tool that will permit the optimization of image quality in CT. Published Monte Carlo CT dosimetry data were used to generate values of the energy imparted to an anthropomorphic phantom undergoing head and body CT examinations. Energy imparted factors E 5,n were computed for irradiation of 5 mm thick slabs of the anthropomorphic phantom, which were normalized to the free-in-air dose to muscle at the CT isocenter obtained in the absence of any phantom or patient. Calculations of E 5,n were obtained for CT scanners from five vendors and for 208 contiguous slabs of the anthropomorphic phantom ranging from the top of the head to the upper leg region. Values of E 5,n were relatively constant in the abdomen and chest region, but there was a large inter-scanner variability, with a mean of 170 ± 50 mJ/Gy for five vendors when E 5,n values were averaged over the whole trunk. The mean E 5,n for the neck region was 100 ± 20 mJ/Gy, which increased to 110 ± 30 mJ/Gy for the head region. Adding 0.25 mm Cu filtration increased the value of the normalized energy imparted value E 5,n by an average of 24%. Relative to 100 kVp, increasing the x-ray tube voltage by 20 and 30 kVp increased the normalized energy imparted value E 5,n by 10% and 14%, respectively. Patient energy imparted is useful for studying optimization strategies with respect to x-ray technique factors.
A 4-Alternative Forced Choice (4-AFC) experimental paradigm was used to measure observer performance of lesion detection. Each 4-AFC experiment yields a lesion contrast that corresponds to a detection accuracy of 92%, I(92%). Experiments were performed to investigate how imaging performance varied with display level setting (window level) at a constant image contrast (window width). Three observers were used to investigate detection performance with window level using a high quality monitor calibrated three different ways (i.e., DICOM, gamma = 1.5, and gamma = 5.0). There were large inter-observer differences in absolute level of performance, with the detection threshold for the three readers varying by nearly a factor of two. For the DICOM display, the detection threshold was linearly related to image level setting. For one reader, detection performance was independent of level, whereas for the other two readers performance dropped by 30% and 11% over the range of level values investigated. Curves changed from linear for the DICOM display to curvilinear for two gamma monitor display settings. In addition, the absolute level of performance for each reader changed with monitor display setting. When the display gamma was 1.5, observer performance was generally reduced, whereas when the display gamma was 5.0, observer performance was generally better. Our data show that the choice of monitor display is an important parameter that significantly affects lesion detection performance. Adoption of the DICOM display standard will permit the direct inter-comparison of data acquired in different laboratories, as well as clinical practice.
We investigated how size and lesion location affect detection of simulated mass lesions in chest radiography. Simulated lesions were added to the center of 10 cm x 10 cm regions of digital chest radiographs, and used in 4-Alternative Forced-Choice (4-AFC) experiments. We determined the lesion contrast required to achieve a 92% correct detection rate I(92%). The mass size was manipulated to range from 1 to 10 mm, and we investigated lesion detection in the lung apex, hilar region, and in the sub-diaphragmatic region. In these experiments, the observer obtained I(92%) from randomized repeats obtained at each of seven lesion sizes, with the results plotted as I(92%) versus lesion size. In addition we investigated the effect of using the same background in the four 4-AFC experiments (twinned) and random backgrounds from the same anatomical region taken from 20 different radiographs. In all three anatomical regions investigated, the slopes of the contrast detail curve for the random background experiments were negative for lesion sizes less than 2.5, 3.5, and 5.5 mm in the hilar (slope of -0.26), apex (slope of -0.54), and sub-diaphragmatic (slope of -0.53) regions, respectively. For lesion sizes greater than these, the slopes were 0.34, 0.23, and 0.40 in the hilar, apex, and sub-diaphragmatic regions, respectively. The positive slopes for portions of the contrast-detail curves in chest radiography are a result of the anatomical background, and show that larger lesions require more contrast for visualization.
The objective of this study was to identify the x-ray tube voltage that results in optimum performance for abdominal CT imaging for a range of imaging tasks and patient sizes. Theoretical calculations were performed of the contrast to noise ratio (CNR) for disk shaped lesions of muscle, fat, bone and iodine embedded in a uniform water background. Lesion contrast was the mean Hounsfield Unit value at the effective photon energy, and image noise was determined from the
total radiation intensity incident on the CT x-ray detector. Patient size ranging from young infants (10 kg) to oversized adults (120 kg), with CNR values obtained for x-ray tube voltages ranging from 80 to 140 kV. Patients of varying sizes were modeled as an equivalent cylinder of water, and the mean section dose (D) was determined for each selected x-ray tube kV value at a constant mAs. For each patient size and lesion type, we identified an optimal kV as the x-ray tube
voltage that yields a maximum value of the figure of merit (CNR2/D). Increasing the x-ray tube voltage from 80 to 140 kV reduced lesion contrast by 11% for muscle, 21% for fat, 35% for bone and 52% for iodine, and these reductions were approximately independent of patient size. Increasing the x-ray tube voltage from 80 to 140 kV increased a muscle lesion CNR relative to a uniform water background by a factor of 2.6, with similar trends observed for fat (2.3), bone (1.9) and iodine (1.4). The improvement in lesion CNR with increasing x-ray tube voltage was highest for the largest sized patients. Increasing the x-ray tube voltage from 80 to 140 kV increased the patient dose by a factor of between 5.0 and 6.2 depending on the patient size. For small sized patients (10 and 30 kg) and muscle lesions, best performance is obtained at 80 kV; however, for adults (70 kg) and oversized adults (120 kg), the best performance would be obtained at 140 kV. Imaging fat lesions was best performed at 80 kV for all patients except for oversized adults, where 140 kV
offers the best imaging performance. For high Z lesions of bone and iodine, imaging performance generally degrades with increasing kV for all patient sizes, with the degree of degradation largest for the smallest patients. We conclude that 80 kV is optimal with respect to radiation dose in abdominal CT for all pediatric patients. For adults, 80 kV is the x-ray voltage of choice for high Z lesions, whereas 140 kV would generally be the voltage of choice of lesions that have an atomic number similar to that of water.
We investigated how the x-ray tube kV and mAs affected the detection of simulated lesions with diameters between 0.24 and 12 mm. Digital mammograms were acquired with and without mass lesions, permitting a difference image to be generated corresponding to the lesion alone. Isolated digital lesions were added at a reduced intensity to non-lesion images, and used in Four-Alternate Forced Choice (4-AFC) experiments to determine the lesion intensity that corresponded to an accuracy of 92% (I92%). Values of I92% were determined at x-ray tube output values ranging from 40 to 120 mAs, and x-ray tube voltages ranging from 24 to 32 kV. For mass lesions larger than ~0.8 mm, there was no significant change in detection peformance with changing mAs. Doubling of the x-ray tube output from 60 to 120 mAs resulted in an average change in I92% of only +3.8%, whereas the Rose model of lesion detection predicts a reduction in the experimental value of I92% of -29%. For the 0.24 mm lesion, however, reducing the x-ray beam mAs from 100 to 40 mAs reduced the average detection performance by ~60%. Contrast-detail curves for lesions with diameter ≥ 0.8 mm had a slope of ~+0.23, whereas the Rose model predicts a slope of -0.5. For lesions smaller than ~0.8 mm, contrast-detail slopes were all negative with the average gradient increasing with decreasing mAs value. Increasing the x-ray tube voltage from 24 to 32 kV at a constant display contrast resulted in a modest improvement in low contrast lesion detection performance of ~10%. Increasing the display window width from 2000 to 2500 reduced the average observer performance by ~6%. Our principal finding is that radiographic technique factors have little effect on detection performance for lesions larger than ~0.8 mm, but that the visibility of smaller lesions is affected by quantum mottle in qualitative agreement with the predictions of the Rose model.
Patients undergoing head CT examinations with iodinated contrast received 100 cc of Iohexol 240 injected intravenously by hand. We developed a software package to align non-contrast and contrast head CT images, and obtain the “difference image” consisting of the iodine enhancement within a given lesion. This “difference image” of the iodine enhancement was added to the non-contrast study at reduced intensities. The signal to noise ratio (SNR) for detecting the added iodine was taken to be directly proportional to the concentration of contrast in the lesion. The visibility of the lesion enhancement in this composite image was compared with the original contrast image using a six-point scale ranging from 5 (no observable difference) to 0 (unacceptable). Two radiologists evaluated head CT images of eleven metastatic lesions. The iodine concentration required to generate an image quality rank of 3, deemed to be satisfactory for diagnosis (S), was determined. We also performed a Receiver Operating Characteristic (ROC) study to identify the iodine concentration corresponding to an area under the ROC curve of 0.95, which corresponds to the detection threshold (D) for each lesion. Reducing the intensity to 50% resulted in an average image qualtiy score S of 3, suggesting that it may be possible to reduce the administered iodine by a half in head CT examinations with no significant loss of diagnostic performance. The average iodine concentration at the detection threshold D was 16%. The average S:D ratio was 3.8 ± 1.7, and was similar for both readers. The value of S was independent of enhancement characteristics, whereas the detection threshold D correlated inversely with the size and intensity of the iodine enhancement. The resultant S:D ratio correlated with the lesion area (r2 = 0.31), mean lesion intensity (r2 = 0.44), and the product of the mean lesion intensity and the lesion area (r2 = 0.37). Our results indicate that the SNR of enhancing lesions in head CT that is needed to satisfy radiologists is about a factor of four greater than the SNR required for iodine detection.
We investigated how current biological uncertainties relating to the radiation risks for breast cancer impact on the optimization of x-ray tube potential (kV) in digital mammography. Digital images were taken of an accreditation phantom using voltages between 24 and 34 kV, and output between 5 and 500 mAs. The average glandular dose (D) at each x-ray tube voltage was determined at each technique setting. Image contrast of a 4 mm thick acrylic disk and the corresponding noise were used to determine the lesion contrast to noise ratio (CNR), which was taken as a relative measure of image quality when the selected x-ray technique factors (i.e., kV/mAs) are varied. The optimal kV for the detection of this simulated mass lesion was determined by maximizing a figure of merit (FOM), the ratio of CNR2/D. The kV that maximized the traditional FOM occurred at 27.3 kVp. The implication for optimization strategies was also analyzed for a radiation risk that is proportional to Dn; a value of n = 0 would correspond to no additional radiation risk, and n = 2 would correspond to a quadratic dose response curve. The x-ray voltage that yielded the highest generalized FOM value was 34 kV for n < 0.25, and 24 kV for n > 1.5. These results show that uncertainties in the form of the dose response curve for radiation induced breast cancer markedly influence the FOM parameter used for optimizing digital radiography imaging systems such as mammography.
We investigated how the thickness of a mass lesion at the observer detection threshold varied with lesion location in the breast. A digital mammography system was used to acquire radiographs of an anthropomorphic breast phantom. Mammograms were acquired with and without mass lesions, thereby permitting a difference image to be generated corresponding to the lesion alone. This isolated lesion was added at a reduced intensity to a non-lesion digital mammogram during a 4-Alternate Forced-Choice (4-AFC) experiment. The lesion intensity that corresponded to a 92% correct performance level in the 4-AFC experiments was determined (I92%). Values of I92% were obtained at different locations in the anthropomorphic phantom, thereby permitting the importance of breast thickness and structured background on lesion detection to be investigated. Lesion detection (I92%) was found to be best in high signal intensity regions (black) and ~25% lower in the low signal regions (white). Lesion detection also appeared to depend on the characteristics of the structured background. The experimental results showed a good correlation with a computation that used a convolution of the lesion and the local background region in the mammogram.
An expectation maximization (EM) algorithm has been developed based on a model of radiographic imaging that accounts for scatter radiation and resolution degradation. Digital radiographs of a chest phantom were acquired, and the amount of scatter in several regions was computed using the known radiographic exposure and the known material properties. Contrast and noise were measured in a step wedge in the phantom. The phantom images were processed by the EM algorithm for up to 8 iterations, and the image intensity and noise values were measured at each iteration step. These values were used to compute the scatter reduction properties of the algorithm and its effect on the contrast-to-noise ratio. The algorithm removed over 90% of the scattered radiation in the image. Image noise values were reduced an average of 50% in the first iteration, but then increased to values equal or above unprocessed images. The contrast-to- noise ratio was initially increased substantially, but gradually decreased as further iterations caused the image noise to increase. With proper selection of processing parameters, this algorithm could provide considerable qualitative enhancement of clinical images with a single iteration as well as numerically accurate scatter reduction.
In this study, we investigated which photon energy results in the lowest patient does when the image contrast to nosie ratio (CNR) is kept constant. This optimum photon energy was obtained for a range of patient sizes, as well as for the detection of lesions with atomic number ranging from 6.5 to 53. Mono-energetic photons from 20 keV to 140 keV were investigated with an x-ray detector that had 100 percent quantum detection efficiency. Patients were modeled as slabs of water with a thickness that ranged from 5 to 30 cm. Image contrast was computed from the x-ray attenuation of small lesions consisting of low Z materials as well as higher Z materials. Relative values of the CNR as a function of the x-ray photon energy were obtained by assuming that the image noise was proportional to the square root of the incident number of x-ray photons under scatter free conditions. The energy imparted to the patient as a function of photon energy was obtained using published data of the absorbed percentage of the incident energy fluence. For each patient thickness and lesion composition, the CNR was kept constant by appropriate adjustment of the x-ray beam intensity, and the corresponding x-ray photon energy that resulted in the minimum patient dose was determined. The optimum photon energy for detection ga low Z lesion increased monotonically from approximately 62 keV for small patients to approximately 78 keV for large patients. For high Z lesions, optimum photon energies were approximately 34 keV for small patients, and increased to approximately 40 keV for large patients. The optimum photon energy was found to vary by about a factor of two over the range of patient thickness investigated. The optimum photon energy also varied by a factor of two for the range of detection tasks investigated.
In this study, we investigated how changing the kVp and mAs used to acquire digital mammograms affects detection of mammographic lesions. A Lorad Full Field Digital Mammography system was used to expose an anthropomorphic breast phantom at x-ray tube voltages ranging from 24 to 32 kVp and output factors ranging from 20 to 120 mAs. Lesions were added at various intensities to digital mammograms, and lesion visibility was assessed using a subjective probability of the lesion being present, with the image contrast varying from visible to invisible. Four observers ranked the visibility of a large mass lesion (2 cm x 1.3 cm) and a calcification lesion with a diameter of ~1mm. Visibility of both lesions was constant between 40 mAs and 120 mAs (constant 28 kVp), but the visibility of both lesions was significantly lower at 20 mAs. For clinically relevant radiographic techniques, quantum noise does not appear to affect observer performance for detection of lesions in the size range of 1mm to 2cm. At a constant mAs, there was a trend showing a reduction in calcification visibility with increasing kVp, but this was not statistically significant (p=0.057).
The performance of a digital radiography system that included a prototype flat panel detector (StingRay) was compared with a 400 speed screen-film system. The flat panel detector consisted of a 500 micrometers thick CsI scintillator with an image matrix size of 3k2. The limiting spatial resolution of screen-film (approximately 4 line pairs/mm) was superior to that of the flat panel detector (approximately 2.5 line pairs/mm). The digital detector had an excellent linearity response (r2 equals 0.997), a dynamic range of 20,000:1, and saturated at a radiation exposure of 60 mR.
This study optimized softcopy display of digital radiographic images acquired using a prototype flat panel detector. Six look up table (LUT) shapes were evaluated, consisting of linear, logarithmic, exponential, 1-exp, sigmoidal, and reverse sigmoidal. Representative digital radiographs covering five body regions (skull, neck, chest, knee and foot) were reviewed on a monitor. Images were assessed on a scale of 1 to 10, with a score of 1 indicating an uninterpretable examination, and a score of 10 indicating a perfect image. The difference between the final and initial image quality scores were ((Delta) ), which corresponds to the improvement achievable by the selected LUT. Major improvements in image quality were achieved using LUTs of the type 1-exp. A comprehensive analysis was made of four versions of the 1-exp LUT applied to forty clinical images. Use of the one version of this 1-exp LUT resulted in the best achievable image quality in 95% of the cases (38/40), with an average (Delta) score of 3.4. These results demonstrate that a logarithmic style LUT can significantly improve image quality in comparison to linear LUTs. Of particular importance was the fact that a single LUT achieved excellent image quality for a broad range of clinical radiographs.