repeated measurements under unchanged con-ditions show the same results. Precision is cru-cial to the reliability of an observed change in successive measurements, but it cannot be simply improved by calibrations. In addition, conventional CT reconstruction algorithms have been optimized for detection tasks, not quantification tasks. As a result, postprocessing algorithms are typically aimed at enhancing features of interest or providing artifact-free images, at the risk of sacrificing the quanti-tative precision [4].
A possible solution to better precision of iodine quantification is the deployment of new iterative reconstruction techniques, such as adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruc-tion (MBIR) provided by GE Healthcare. It-eratively reconstructed images have lower noise compared to images from conventional
Precision of Iodine Quantification in Hepatic CT: Effects of Iterative Reconstruction With Various Imaging Parameters
Baiyu Chen1,2
Daniele Marin3
Samuel Richard2,3,4
Daniela Husarik3
Rendon Nelson3
Ehsan Samei1,2,3,5
Chen B, Samei E, Marin D, Richard S, Husarik D, Nelson R
1Medical Physics Graduate Program, Duke University, 2424 Erwin Rd, Ste 302, Durham, NC 27705. Address correspondence to B. Chen ([email protected]).
2Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, NC.
3Department of Radiology, Duke University, Durham, NC.
Iodinated contrast agents are com-monly used with hepatic CT to en-hance the tissue contrast between subtle lesions and liver parenchy-
ma, with their concentrations being of quanti-tative interest in studies such as contrast bolus injection rate optimizations, arterial or portal phase differentiations, and noninvasive quan-tifications of perfusion [1–3]. Because a le-sion’s iodine concentration is proportional to the lesion’s contrast, as measured by Houns-field unit enhancement, it can be indirectly quantified from the Hounsfield unit count of the image. The reliability of this quantifica-tion, however, relies on the accuracy and pre-cision of the quantification as a function of the imaging protocol. Accuracy is the degree to which measurements are close to the quan-tity’s true value, which can be improved with calibrations. Precision is the degree to which
Received July 24, 2012; accepted after revision October 26, 2012.
Supported in part by a grant from GE Healthcare, which also provided equipment and technical support for this study. R. Nelson and E. Samei are consultants for GE Healthcare. B. Chen received research funding from GE Healthcare. The remaining authors have no pertinent disclosures and maintained full control of the data and information submitted.
OBJECTIVE. The objective of this study was to evaluate the feasibility of using itera-tive reconstructions in hepatic CT to improve the precision of Hounsfield unit quantification, which is the degree to which repeated measurements under unchanged conditions provide consistent results.
MATERIALS AND METHODS. An anthropomorphic liver phantom with iodinated lesions designed to simulate the enhancement of hypervascular tumors during the late hepat-ic arterial phase was imaged, and images were reconstructed with both filtered back projec-tion (FBP) and iterative reconstructions, such as adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR). This protocol was further expand-ed into various dose levels, tube voltages, and slice thicknesses to investigate the effect of iter-ative reconstructions under all these conditions. The iodine concentrations of the lesions were quantified, with their precision calculated in terms of repeatability coefficient.
RESULTS. ASIR reduced image noise by approximately 35%, and improved the quan-titative precision by approximately 5%, compared with FBP. MBIR reduced noise by more than 65% and improved the precision by approximately 25% compared with the routine pro-tocol. MBIR consistently showed better precision across a thinner slice thickness, lower tube voltage, and larger patient, achieving the target precision level at a dose lower (≥ 40%) than that of FBP.
CONCLUSION. ASIR blended with 50% of FBP indicated a moderate gain in quanti-tative precision compared with FBP but could achieve more with a higher percentage. A high-er gain was achieved by MBIR. These findings may be used to reduce the dose required for reliable quantification and may further serve as a basis for protocol optimization in terms of iodine quantification.
Chen et al.Iterative Reconstruction Algorithms in Hepatic CT
Medical Physics and InformaticsOriginal Research
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filtered back projection (FBP) reconstructions [5]. In particular, MBIR includes modeling of the image acquisition system, such as the limited size of the focal spot and the detector, which provides more accurate reconstruction, as well as improved resolution [6–9]. Iodine quantification in liver images reconstructed with iterative reconstruction might take ad-vantage of these features and achieve the same precision as quantifications with FBP images at lower dose levels. Therefore, the purpose of this study was to investigate the impact of it-erative reconstruction algorithms on the pre-cision of iodine quantifications under various imaging conditions. Dose reduction potential of iterative reconstruction was also assessed in the context of precision of iodine quantifica-tion, as compared with FBP.
Materials and MethodsLiver Phantom With Iodinated Lesions
An anthropomorphic liver phantom was de-signed at our institute and custom manufactured by CIRS to simulate hypervascular tumors imaged during the late hepatic arterial phase. As shown in Figure 1, the phantom is a slab with an ellip-tical cross-section (diameter, 18 × 22 cm; thick-ness, 2.5 mm), composed of muscle background,
a liver insert, 12 spherical lesions, and the spine. The lesions were of three diameters (1.5, 1, and 0.5 cm) and two contrast levels and were placed in concentric rings (diameter, 3.5 and 7.5 cm) around the isocenter to avoid uncertainties raised from lo-cations. In addition, the phantom contained two 4-cm adipose rings that can be wrapped around the phantom, allowing the simulation of larger adults having either 4 or 8 cm of subcutaneous fat.
To simulate the attenuation of hypervascular liver tumors and normal liver parenchyma during the late hepatic arterial phase after administration of iodinated contrast material, different concentra-tions of iodine were added to the liver parenchy-ma and lesions to provide the target Hounsfield unit counts listed in Table 1. The values in Table 1 were derived in three steps: first, the Hounsfield unit counts of the liver parenchyma at 80 and 140 kVp were experimentally measured from 60 con-secutive patients who underwent clinically indi-cated dual-energy CT scans of the liver at our in-stitution, as indicated in Table 1 [5, 10]. Second, the Hounsfield unit counts of the lesions with low and high contrast against the parenchyma back-ground, as indicated in Table 1, were designed to correspond to 0.4 and 0.8 mg/mL additional iodine concentration. This allows the enhancement of the low-contrast lesion to be invisible (8 HU) at 140
kVp but visible (24 HU) at 80 kVp, because a pre-vious study has shown that a minimum of 10 HU is required to observe the lesion [11]. Finally, the rest of the data in Table 1 were mathematically interpo-lated from the previously calculated data according to tube voltage and iodine concentration.
Scanning and Reconstruction ProtocolsThe liver phantom without an additional adi-
pose ring (representing a 50-kg patient), which is referred to as the “small patient” in the rest of this work, was scanned (Discovery CT750 HD, GE Healthcare). A routine abdominal protocol at our institution was used, with 40-mm beam colli-mation, 120 kVp, 1.375 pitch, tube current deter-mined by automated tube current modulation (as determined by anteroposterior and lateral digital scout radiographs) with a 14.0-HU noise index requirement (CT dose index [CTDI], 4.5 mGy), FBP reconstruction, and 2.5-mm slice thickness.
On the basis of the routine protocol, two addi-tional reconstruction algorithms, ASIR and MBIR, were applied to the same acquisition dataset as that of FBP to assess their effect on quantification per-formance. Note that the ASIR used in this study was 50% blended with FBP to reduce the somewhat waxy appearance of iterative-reconstructed images, which is a typical clinical process to provide an im-
Fig. 1—Anthropomorphic liver phantom used for liver image simulations. A and B, Diagram (A) and photograph (B) depict phantom, which was slab with liver insert and iodinated hepatic lesions of two concentrations. C, Two adipose rings added to periphery of phantom allowed flexibility of simulating larger patient sizes.
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age perception comparable to that of the FBP im-ages to which most radiologists are accustomed. Also note that, because all three reconstruction al-gorithms were applied to the same acquisition da-taset, they shared the same CTDI values. Other imaging and reconstruction parameters were also expanded into a full parameter space to assess the effect of iterative reconstruction under various cir-cumstances, including four additional dose levels, corresponding to 75%, 50%, 25%, and 10% of the clinical dose level; two additional tube voltages, 80 and 100 kVp; and one additional reconstruc-tion slice thickness at 0.625 mm. For protocols of the same dose level but different tube voltages, the tube currents were adjusted accordingly to main-tain the isodose condition. Each protocol was re-peated three (for 100 kVp) or ten (for 80 and 120 kVp) times for the assessment of precision.
A phantom with two adipose rings (represent-ing an 80-kg patient) was also investigated in our study. This phantom, referred to as the “large pa-tient,” was scanned with a routine abdominal pro-tocol corresponding to its size, with the range of parameters expanded similarly to those of the small patient. Table 2 illustrates the full parame-ter space explored for small and large patients in this study. Note that the clinical dose level for each phantom size corresponded to a predefined noise index, not a fixed dose. As a result, the 100% dose level for the large phantom (24.5 mGy) was much higher than the 100% dose level for the small pa-tient (4.5 mGy).
Data AnalysisA customized code (MATLAB, Mathworks) was
used for data analyses. To quantify the image noise of each dataset, spherical regions of interest (ROIs) were placed on the uniform muscle region, with the SD within the ROI recorded. To quantify the iodine concentration, the contrast enhancement of the le-sions was measured from slices across the center of the lesions in each dataset (around 6 mm thick), with spherical ROIs placed on both high-contrast le-sions and the nearby liver parenchyma to record the mean number of Hounsfield units within each ROI. Only the largest 1.5-cm lesions were used, because those lesions provided the highest number of pixels
for best quantification. Spherical ROIs had a diam-eter of 1 cm to ensure sufficient confidence margins as well as sufficient number of pixels for statistical analyses (Fig. 2).
The precision of contrast was further calculated in terms of repeatability coefficient. Repeatabili-ty coefficient represents the expected absolute dif-ference between any two repeated quantifications of the same contrast, for 95% of cases [12]. There-fore, a lower repeatability coefficient (RC) indi-cates better precision and is calculated as follows:
RCj = 1.96 = 2.77σ ≈ 2.77σ2σ2Wj Wj Wj
ˆ (1),
where
= WMSj = WMSij / n =∑σWjˆ
i = 1
n
(Cijk − Cij)2
∑i = 1
n ∑k = 1
K
n(K − 1)
(2).
WMSj is the estimate of the within-repeats variance (σ2Wj) for the contrast quantified from the jth proto-col, K is the number of repeats for the jth protocol, Cijk is the contrast of the ith lesion measured from images acquired with jth protocol and kth repeat, Cij is the contrast of the ith lesion measured from images acquired with jth protocol, and averaged over all repeats, and n is the number of slices ana-lyzed in each dataset.
The two iterative reconstructions, ASIR and MBIR, were further assessed for their dose-reduc-tion ability in terms of precision. This ability was defined as the ratio between the dose required by it-
erative reconstruction and the dose required by FBP to achieve the same threshold precision. Thus, given the same patient size, tube voltage, and slice thick-ness, if iterative reconstruction and FBP methods require A and B milligrays of dose, respectively, to achieve a threshold precision, the dose reduction ability of the iterative method was computed as (1 − [A / B]) × 100%. Note that this calculation relied on a proper choice of the threshold precision (i.e., re-peatability coefficient) to represent the acceptable fluctuation in Hounsfield unit count, which was cho-sen to be 10 HU in this study because it represents the minimum perceptible enhancement by human observers [11]. This calculation also required doses A and B to be properly interpolated from the prede-termined dose levels described in Table 2. To do so, repeatability coefficient values of each reconstruc-tion algorithm were fitted as a function of dose, with A and B interpolated accordingly from the fits.
ResultsPhantom Images and Noises
Images of the liver phantom under some typical imaging and reconstruction protocols are shown in Figure 3. Overall, thicker slice, higher dose, and iterative reconstruction show reduced noise. Compared with FBP, ASIR im-ages slightly reduce the noise without obvious texture change. MBIR significantly reduces the image noise but also presents a “waxier” texture. In addition to the visual assessment, the noise was numerically assessed and listed
TABLE 1: Target Attenuation Designed for Liver Parenchyma, High-Contrast Lesion, and Low-Contrast Lesion at Four Different Tube Voltages
aData are from 60 consecutive patients who underwent clinically indicated dual-energy CT of the liver at our institution.
bData are theoretically designed Hounsfield unit numbers of the low- and high- contrast lesions, respectively, corresponding to 0.4 and 0.8 mg/mL additional iodine concentration.
TABLE 2: Scanning and Reconstruction Protocols Determined According to Routine Protocols at Our Institution for Specific Patient Sizes and Further Expanded to Investigate the Effect of Iterative Reconstruction Under Various Circumstances
Phantom Size Dose Level Tube Voltage (kVp)
Reconstruction Algorithm
Slice Thickness (mm) No. of Repetitions
Small 100% (noise index, 14.0; CTDI, 4.5 mGy), 75%, 50%, 25%, and 10%
120, 100, and 80 FBP, ASIR, and MBIR 0.625 and 2.5 3 (100 kVp) and 10 (80 and 120 kVp)
Large 100% (noise index, 24.0; CTDI, 24.5 mGy), 75%, 50%, 25%, and 10%
in Table 3. A thicker slice reduces the noise by around 35%. ASIR reduces the noise by around 30%. MBIR reduces the noise by around 65% at clinical dose level and further reduces the
noise by 80% at 10% of the clinical dose level. Different tube voltages show similar image noise in this study, because the tube current had been adjusted to maintain the same CTDI
for different tube voltages under the same pa-tient size. Different patient sizes also show similar image noise, because the tube current has been adjusted to maintain similar noise in-dexes for the two patient sizes (small patient, 12.0 HU; large patient, 14.0 HU).
ContrastThe tissue contrast of the hyperenhancing le-
sions to the nearby liver parenchyma is shown in Figure 4. The contrast seems to be largely in-dependent of the choice of reconstruction algo-rithm and dose level. Lower tube voltage yields higher contrast, as expected.
Effects of Imaging and Reconstruction Parameters on Precision
On the basis of the measured contrast, the precision of iodine concentration was further calculated. Overall, the repeatability coef-ficient decreased as dose increased, indicat-ing a smaller variance (i.e., a better precision)
Fig. 2—Small patient phantom. A, Sample slice was acquired at 80 kVp, 0.625-mm slice thickness, and 4.5-mGy CT dose index. B, To quantify iodine concentration (Hounsfield unit numbers) of lesions in panel A, spherical regions of interest (red circles) were placed on both 1.5-cm high-contrast lesions and nearby liver parenchyma, with mean Hounsfield unit numbers recorded.
Fig. 3—Sample images of liver phantom. Five columns represent four combinations of acquisition parameters and phantom size. Three rows represent three reconstruction algorithms applied to same acquisition dataset. Display window and level are 350 and 80. ASIR = adaptive statistical iterative reconstruction, CTDI = CT dose index, FBP = filtered back projection, MBIR = model-based iterative reconstruction.
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at higher dose. As mentioned in the Materials and Methods, the precision was empirically fitted to a function of dose. One example is il-lustrated in Figure 5A, where the repeatability coefficient values of a protocol with 120 kVp, 2.5-mm slice thickness, small patient, and all three reconstruction algorithms are plotted as a function of dose. Inspired by the decreasing trend, which is pronounced at an extremely low dose (< 1 mGy), still visible at low dose (1–2.5 mGy), but levels off at high dose lev-els (> 2.5 mGy), a function that closely fitted the data points was found to be RC = a − [b / (Dose−0.5 + c)] (FBP, R2 = 0.98; ASIR, R2 = 0.95; MBIR, R2 = 0.97). Other protocols not presented in Figure 5A also showed similar dose dependence, and the application of the function to all curves showed an average R2 = 0.94 ± 0.08.
Figure 5A also shows the effect of recon-struction algorithm. At the highest dose, which represents currently used clinical dose level, the precision of iterative reconstruction was not much different from that of FBP. As dose de-
creases, the two iterative reconstruction algo-rithms show distinct performances: ASIR re-mains similar to FBP, whereas MBIR shows a curve significantly lower and flatter than ASIR and FBP do (about 25% reduction in re-peatability coefficient), indicating improved precision. Overall, the superior performance of MBIR confirms its dose reduction poten-tial by providing the same quantification pre-cision at a much lower dose, which was fur-ther quantified in the next section.
The effect of reconstruction algorithm on quantification precision was further test-ed with thinner slices, where the quantum noise greatly increased. Figure 5B illustrates the precision of images with 0.625-mm slice thickness. Comparing Figure 5B to Figure 5A, which represents 2.5-mm slice thick-ness, the precision for thinner slice thick-ness greatly deteriorates for all reconstruc-tion algorithms, with an increase of 30–50% in repeatability coefficient. The advantage of MBIR, however, is more pronounced under such high image noise conditions.
The effect of iterative reconstructions was also tested at 80 and 100 kVp. Lower tube voltage is appreciated in hepatic imaging be-cause it greatly enhances the lesion contrast; however, lower tube voltage also raises the baseline of image Hounsfield unit number, along with a possible byproduct of larger fluctuations in Hounsfield unit number. Fig-ure 6 plots the precision for 80-, 100-, and 120-kVp protocols, with all other param-eters fixed at 0.625-mm slice thickness and small patient. Results indicate that 80 kVp leads to higher repeatability coefficient (i.e., worse precision for FBP and ASIR). Howev-er, for MBIR, the precision is relatively sta-ble across all tube voltages. Therefore, with MBIR, it is possible to get both enhanced im-age contrast and improved quantification pre-cision at lower tube voltage.
Finally, the comparisons between recon-struction algorithms were made at two dis-tinct patient sizes, with other parameters fixed at 120 kVp and 0.625-mm slice thick-ness (Fig. 7). The repeatability coefficient values are slightly higher for the large patient but expected because the highest dose level involved in the large patient corresponded to a noise index of 24.0 HU, whereas the high-est dose level in the small patient correspond-ed to a noise index of only 14.0 HU. In oth-er words, the image noise in the large patient was slightly higher, which slightly increased the fluctuations in quantifications. In spite of the general higher repeatability coefficient, the advantage of MBIR is preserved with the large patient size.
Dose Reduction Potential of Iterative Reconstructions
ASIR showed a precision performance very similar to that of FBP, where the dose reduction potential was not statistically significant. There-fore, only MBIR was assessed for its dose-re-duction potential. According to the fitted func-tion of each repeatability coefficient–dose relationship, the dose corresponding to 10 HU repeatability coefficient was interpolated for each protocol, as listed in Table 4. The relative dose reduction potential of MBIR was further calculated and also listed in Table 4. A dose re-duction level of at least 40% was achieved with MBIR across all protocols tested in this study.
DiscussionThe precision of iodine quantification affects
the usefulness of the quantification by affecting the reliability of a change observed in succes-sive measurements. In this article, we focused
TABLE 3: Noise of Images Acquired for Two Patient Sizes With Protocols of Various Slice Thicknesses, Reconstruction Algorithms, and Dose Levels
Protocol, Reconstruction Algorithm
Dose Levels
10% 100%
Small patient phantom
2.5-mm slice thickness
80 kVp
FBP 66 22
ASIR 45 15
MBIR 10 7
120 kVp
FBP 59 18
ASIR 42 14
MBIR 12 8
0.625-mm slice thickness, 120 kVp
FBP 98 30
ASIR 66 21
MBIR 17 11
Large patient phantom, 0.625-mm slice thickness, 120 kVp
FBP 118 37
ASIR 87 28
MBIR 31 18
Note—In this article, precision is a second-order statistic that cannot be visually assessed from the image. Therefore, Figure 3 and Table 3 should not be interpreted as bases for judging the precision, but rather as illustrations of the visual attributes of the iterative reconstruction algorithms used in this study. ASIR = adaptive statistical iterative reconstruction, FBP = filtered back projection, MBIR = model-based iterative reconstruction.
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on achieving high quantitative precision with it-erative reconstructions, which significantly re-duced the noise and, hence, the noise-induced Hounsfield unit uncertainty. The overall re-sults indicated that ASIR, one of the two it-erative reconstruction algorithms tested in this study, had a limited gain in precision as com-pared with that of FBP. MBIR, the other it-erative reconstruction algorithm, significantly improved precision and reduced the dose re-quired for a reliable quantification. This infor-mation may serve as a guide when future pro-tocol optimizations are tuned toward iodine quantification and patient dose reduction.
Dose showed its strong effect on quanti-tative precision. The precision of quantifi-cation improved as dose increased but pla-teaued at high dose levels, highlighting the importance of sufficient but not excessive dose to diminish quantification uncertainty. Therefore, we not only compared the preci-sion of iterative reconstruction and FBP at a
given dose level but also compared the dose required by iterative reconstruction and FBP to achieve a given precision. With the routine protocol used clinically at our institute (Fig. 5A), ASIR showed moderate improvement in precision. MBIR significantly improved the precision of quantification and was able to achieve the same precision at a 52% lower dose as compared with FBP. This finding has important clinical implications in that, for most patients, MBIR may be able to maintain the quantification precision with a significant dose reduction. For example, when attempt-ing to differentiate simple cysts, hemorrhag-ic cysts, papillary carcinomas, and clear cell carcinomas in the kidneys, the determina-tion of contrast-enhanced enhancement is critical. This measurement is typically per-formed by comparing the attenuation of the mass in both unenhanced and contrast-en-hanced datasets, the former of which is often obtained with a low radiation dose.
The effect of iterative reconstruction was further investigated with thinner slice thick-ness, lower tube voltage, and larger patient sizes to ensure that the advantage of iterative reconstruction is generalizable. We chose this broad range of protocols for the following rea-sons: first, although thicker slices were typi-cally used in the clinic to facilitate the work-flow (i.e., fewer images to review) and reduce image noise, slices as thin as 0.625 mm are used routinely to reconstruct off-axis images, typically in the coronal plane, as well as CT angiographic images; second, lower tube volt-age has recently been promoted for small pa-tients to reduce both the iodine and radiation dose and to improve image quality [13–15]; and third, larger patient sizes represent a sig-nificant portion of the U.S. population and are raising concerns because the increased scatter-ing in larger patients might deteriorate image quality and compromise diagnostic interpreta-tion. With thinner slice thickness, the precision
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Fig. 4—Tissue contrast of lesions with higher iodine concentration to nearby liver parenchyma. A–C, Contrast was measured at images reconstructed from filtered back projection (FBP) (A), adaptive statistical iterative reconstruction (ASIR) (B), and model-based iterative reconstruction (MBIR) (C). Each subplot further contains measurements from three peak kilovoltages and five dose levels. CTDIvol = volume CT dose index.
Fig. 5—Precision (repeatability coefficient) of all three reconstruction algorithms. A, Graph shows repeatability coefficient fitted as function of dose, with other parameters fixed at small patient, 120 kVp, and 2.5-mm slice thickness. Overall, higher dose led to better precision. ASIR = adaptive statistical iterative reconstruction, CTDIvol = volume CT dose index, FBP = filtered back projection, MBIR = model-based iterative reconstruction.B, Graph shows impact of reconstruction algorithms on precision with 0.625-mm slice thickness.
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Fig. 6—Impact of iterative reconstructions on precision at different peak kilovoltages. A–C, Graphs show precision investigated at 120 kVp (A), 100 kVp (B), and 80 kVp (C). Other parameters were fixed at 0.625-mm slice thickness and small patient size. ASIR = adaptive statistical iterative reconstruction, CTDIvol = volume CT dose index, FBP = filtered back projection, MBIR = model-based iterative reconstruction.
Fig. 7—Impact of iterative reconstructions on precision by patient size. A and B, Graphs show precision at small (A) and large (B) patient size. Other parameters were fixed at 120 kVp and 0.625-mm slice thickness. Note that highest dose levels of two patient sizes correspond to different noise index, with 14.0 HU for small patient and 24.0 HU for large patient. ASIR = adaptive statistical iterative reconstruction, CTDIvol = volume CT dose index, FBP = filtered back projection, MBIR = model-based iterative reconstruction.
TABLE 4: Threshold Dose for All Protocols and Patient Sizes and Relative Dose Reduction Ability of Model-Based Iterative Reconstruction (MBIR) Derived According to Threshold Dose
Dose, Protocol
Small Patient Large Patient
80 kVp 100 kVp 120 kVp 100 kVp 120 kVp 140 kVp
Threshold dose (mGy)a
FBP
0.625 mm 4.3 2.0 2.9 39.9b 26.0b 35.7b
2.5 mm 1.3 1.3 0.9 — — —
MBIR
0.625 mm 1.0 0.9 1.1 22.1 14.2 14.0
2.5 mm 0.7 0.5 < 0.5c — — —
Relative dose reduction (%), MBIR vs FBP
0.625 mm 75 58 61 45 46 61
2.5 mm 62 67 > 52d — — —
Note—Dashes indicate no data available. FBP = filtered back projection.aThe threshold dose is the CT dose index corresponding to a repeatability coefficient of 10 HU.bEven the maximum dose tested in this study did not fulfill the precision requirement, so the threshold dose was extrapolated rather than interpolated from the function.cEven the minimum dose tested in this study fulfilled the precision requirement, so the threshold dose was expressed as the minimum dose tested, conservatively.dTo be conservative, the dose reduction ability was calculated on the basis of the minimum dose tested in this study, which already fulfilled the precision requirement.
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W482 AJR:200, May 2013
Chen et al.
is usually deteriorated by the increased image noise. However, the gain in precision with ASIR became more noticeable. An even more pronounced improvement was observed with MBIR. The precision of MBIR with 0.625 mm was not only superior to that of FBP with 0.625 mm but even comparable to the preci-sion of FBP with 2.5-mm slices. With lower tube voltage, the precision of both FBP and ASIR deteriorated significantly because high-er Hounsfield unit values led to higher fluctua-tions in quantifications, but the precision of MBIR was stable, allowing the combination of better image contrast with better quantifica-tion. With a large patient, MBIR was signifi-cantly better than FBP and ASIR and was comparable to its precision with a small pa-tient, given that enough dose was delivered to ensure similar noise level.
Across all circumstances tested in this study, ASIR reduced noise by around 35% but did not improve quantitative precision by the same or-der (around 5% reduction in repeatability coef-ficient). Therefore, although ASIR was shown by previous studies to be very promising in detection tasks [5], it did not bring great im-provement to the quantification task used in this study. MBIR, however, reduced the noise by more than 65% and consistently improved precision (around 25% reduction in repeatabil-ity coefficient) with a dose reduction potential of 40% or more. This would lower the risk of radiation-induced cancer and, thus, would en-able more frequent follow-up CT examinations and more pediatric examinations.
This distinct behavior between ASIR and MBIR mentioned already may be partially at-tributed to the modeling process included in the MBIR reconstruction and partially to the fact that this ASIR reconstruction was 50% blended with FBP. The 50% blending ratio was chosen because it was commonly used at our institution to mitigate the waxy look of iterative recon-structed images. However, to our knowledge, this ratio is not standardized and can range from 20% to 100% in clinical practice. The precision of ASIR with blending ratio higher than 50% can be linearly approximated from the preci-sion of FBP and 50% ASIR and is expected to provide better performance
Although this study focused on the quantifi-cation of iodine concentration, the precision of iodine quantification is essentially the precision of Hounsfield unit quantification, as explained in the introduction to this article. Therefore, the dependency of precision on imaging and recon-struction parameters shown in this study can be directly applied to all Hounsfield unit quanti-
fication tasks, such as the detection of subtle hepatocellular carcinoma in the setting of cir-rhosis, the assessment of diffuse or focal depo-sition of fat or iron in the liver, or the character-ization of cystic renal lesions. Furthermore, the dependency of precision indicated in this study might also provide insights on other types of Hounsfield unit–based quantifications, such as the volume quantification of lung nodules based on intensity thresholding and the density quantification of lung airway that also heavily relies on Hounsfield units. The advantage of MBIR could be extended to this broader spec-trum of quantifications.
Nonetheless, this study has several limita-tions. First, only precision was considered as the criterion of quantitative process, whereas in clinical practices, many factors may affect the quantitative performance, such as the tex-ture and resolution of the image. Therefore, the dose reduction percentage of MBIR de-ducted in this study might be overly optimis-tic, and studies from other perspectives are required to further evaluate it. Second, some protocols only have a limited number of rep-etitions (three times). Although this was somehow compensated by the curve fitting process, the statistical power of repeatability coefficient was diminished. Finally, the two adipose rings were uniformly added to the peripheral of the phantom to simulate larg-er patients, whereas in reality, adipose tissue can accumulate predominantly within the ab-domen, particularly in men. To what extent this adipose distribution can affect the quan-tification remains the effort of future studies.
In conclusion, this study provided a frame-work for the evaluations of iterative recon-structions in quantitative hepatic imaging. ASIR did not show much gain in quantita-tive precision when it was 50% blended with FBP, but it could achieve more with a higher percentage. MBIR, a new reconstruction al-gorithm, showed a strong improvement in the precision of iodine quantifications across vari-ous doses, slice thicknesses, tube voltages, and patient sizes, and achieved the same precision at a dose 40% less than that of FBP.
