ct image features of the fbp reconstruction algorithm in

Research ArticleCT Image Features of the FBP Reconstruction Algorithm in theEvaluation of Fasting Blood Sugar Level of Diabetic PulmonaryTuberculosis Patients and Early Diet Nursing

Lili Hong ,1 Liling Lin ,2 Jingping Chen ,1 and Biyu Wu 3

1Pulmonary and Critical Care Medicine (PCCM), Quanzhou First Hospital, Quanzhou, 362000 Fujian, China2Hospital Infection-Control Office, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000 Fujian, China3Department of Nursing, Quanzhou First Hospital, Quanzhou, 362000 Fujian, China

Correspondence should be addressed to Biyu Wu; [email protected]

Received 18 September 2021; Revised 22 October 2021; Accepted 2 November 2021; Published 18 November 2021

Academic Editor: Osamah Ibrahim Khalaf

Copyright © 2021 Lili Hong et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The study was aimed at exploring the application value of the CT image based on a filtered back projection (FBP) algorithm in thediagnosis of patients with diabetes complicated with tuberculosis and at analyzing the influence of dietary nursing on patients withdiabetes complicated with tuberculosis. In this study, the FBP algorithm was used to optimize CT images to effectively obtainreconstructed ROI images. Then, the deviation from measurement values of reconstructed images at different pixel levels wasanalyzed. 138 patients with diabetes complicated with tuberculosis were selected as research subjects to compare the number oflung segments involved and the CT imaging manifestations at different fasting glucose levels. All patients were divided into thecontrol group (routine drug treatment) and observation group (diet intervention on the basis of drug treatment) by randomnumber table method, and the effect of different nursing methods on the improvement of patients’ clinical symptoms wasdiscussed. The results showed that the distance measurement value decreased with the increase in pixel level, there was nosignificant difference in the number of lung segments involved in patients with different fasting glucose levels (P > 0:05), andthere were statistically significant differences in the incidence of segmental lobar shadow, bronchial air sign, wall-less cavity,thick-walled cavity, pulmonary multiple cavity, and bronchial tuberculosis in patients with different fasting glucose levels ( P <0:05). Compared with the control group, 2 h postprandial blood glucose level in the observation group was significantlyimproved (P < 0:05), there was a statistical significance in the number with reduced pleural effusion and the number withreduced tuberculosis foci in the two groups (P < 0:05), and the level of hemoglobin in the observation group was 7:1 ± 1:26,significantly lower than that in the control group (8:91 ± 2:03, P < 0:05). It suggested that the changes of CT images based onthe FBP reconstruction algorithm were correlated with fasting blood glucose level. Personalized diet nursing intervention canimprove the clinical symptoms of patients, which provides a reference for the clinical diagnosis and treatment of patients withdiabetes complicated with tuberculosis.

1. Introduction

Diabetes and tuberculosis are common diseases, and theirlong-term existence will cause chronic damage or failure ofmultiple organs [1, 2]. According to statistics, the prevalenceof tuberculosis in diabetic patients is 4 to 8 times higher thanthat in the general population. The metabolism disorder ofsugar, fat, and protein in diabetic patients leads to a declinein immunity, and the increased blood sugar provides nutri-tion for the growth and reproduction of mycobacterium. As

a result, the diabetic patients are easy to develop tuberculosis[3–5]. Of diabetic patients suffering from tuberculosis,approximately 10%-20% of patients have no respiratorysymptoms, and about 80% of patients have rapid onset anddisease progress, poor curative effects, and a high drug resis-tance rate [6]. Compared with simple pulmonary tuberculo-sis, the scope of lung lesions is wide and there are manycaseous lesions, and thus, the treatment is difficult and theprognosis is poor [7, 8]. At present, the clinical treatment ofdiabetes patients with tuberculosis is mainly the drug

HindawiComputational and Mathematical Methods in MedicineVolume 2021, Article ID 1101930, 11 pageshttps://doi.org/10.1155/2021/1101930

https://orcid.org/0000-0003-2559-8582

https://orcid.org/0000-0001-6567-221X

https://orcid.org/0000-0003-4097-7134

https://orcid.org/0000-0002-3951-5971

https://creativecommons.org/licenses/by/4.0/



https://doi.org/10.1155/2021/1101930

treatment and diet intervention to control the patient’s bloodglucose level [9, 10].

The examination methods of diabetes complicated withtuberculosis mainly include X-ray and CT, but the X-ray isinferior to CT in determining the activity of tuberculosis[11]. Multislice spiral CT overcomes the image overlapproblem in traditional X-ray examinations thanks to its highdensity and spatial resolution and significantly increases thedetection rate of lesions. However, its images show variabil-ity and complexity when diagnosing atypical diseases such asdiabetes and tuberculosis [12, 13]. CT images of patientswith diabetes and tuberculosis mostly show segmental andleaf-shaped lesions, consolidation, and cavities formed bythe dissolution and necrosis of caseous material, and the cav-ity is connected to the surrounding bronchus [14, 15]. Aftereffective treatment, the CT image shows that the scope of thelesion is reduced and the exudative boundary becomes clear,but there exist a few cord-like and irregular thin-lineshadows [16, 17].

The data obtained by the CT system in the two-dimensional plane is processed, there will inevitably beerrors in the installation process, and it needs to be cali-brated through the template. Some scholars have proposedCT image iterative reconstruction algorithms, FBP algo-rithms, and quantitative analysis of optical tomographymolecular imaging to analyze the quality of image recon-struction under different filters and sparseness. The FBPalgorithm has the characteristics of simple operation andshort running time when processing CT images, but it hasthe problem of high radiation dose and needs to be furtheroptimized [19]. In order to reduce the harm of X-rays tothe human body, the CT image is reconstructed, the ROIprojection data is used to increase or decrease the projectiondata of the region of interest (ROI), and the constructor isused to reconstruct the overall or partial image [20]. Forthe convolutional backprojection calculation of image recon-struction, the ROI area can be selected, and it is not neces-sary to scan the whole to obtain all the projection data [21,22]. Based on the above research, in this study, the FBP algo-rithm was used to reconstruct CT images, and different waysof nursing were adopted in the two groups, to discuss theinfluence of different nursing methods on the patient condi-tion, expected to provide a basis for the diagnosis and treat-ment of diabetes complicated with tuberculosis.

2. Materials and Methods

2.1. Research Subjects. In this study, 138 patients with diabe-tes and tuberculosis admitted to hospital from June 2017 toJanuary 2021 were selected as research subjects, including 71males with an average age of 45:6 ± 7:1 years and 67 femaleswith an average age of 46:4 ± 8:5 years. A random numbertable method divided them into the observation group andcontrol group, with 69 cases in each group. The controlgroup was given conventional treatment, and the observa-tion group was given dietary intervention on the basis ofdrug treatment. There were 35 males and 34 females in thecontrol group, with an average age of 45:47 ± 5:18 years;there were 36 males and 33 females in the observation group,

with an average age of 46:02 ± 6:23 years. The study hasbeen approved by the ethics committee of the hospital. Allpatients and their families were aware of the study and hadsigned an informed consent form.

Inclusion criteria were as follows: (1) patients aged ≥18years old and no limitation to the gender and (2) patientsdiagnosed with diabetes and tuberculosis.

Exclusion criteria were as follows: (1) patients with AIDSand other diseases that affect the immune system, (2)patients with a history of insulin treatment, (3) patients withvital organ dysfunction, and (4) patients with endocrine dis-eases and severe gastrointestinal dysfunction.

2.2. CT Imaging. X-ray is a way for CT to obtain informationon the internal structure of the human body. The mostimportant way is to use a detector to capture the attenuatedX-rays passing through the human body. The acquisition ofCT images is mainly to use algorithm reconstruction toreconstruct the projection data acquired by the detector atdifferent angles. In the medical field, the basic unit of CTreconstruction is generally voxels. Voxels divide the tissueinto small blocks with uniform density, and the resultinguniform blocks are called voxels. The finer the division ofthe tissue, the smaller the voxel, which means that the inter-val between the receivers on the detector is also smaller. Forthe same object, the detector can obtain more projectiondata, and the quality of the reconstructed image becomesbetter. The data flow chart of CT system imaging is shownin Figure 1. First, the X-ray tomographic plane is concealed.The X-ray on the X-ray receiver is the projection data to col-lect, and the collected information reconstructs the imageinformation (Figure 1).

2.3. Optimization of the FBP Algorithm. The projection pro-cess equation of the A × A size two-dimensional image is asfollows: D =QH ð1Þ, where H represents the image vector ofsize AA × 1, Q represents the projection coefficient matrix ofYW × AA (W represents the projection angle and Y repre-sents the maximum number of projections at each projec-tion angle), and D represents the projection vector of sizeYW × 1. The reconstructed image is shown in

B∗ =Q ∗D: ð1Þ

In the equation, B ∗ is the reconstructed image with a sizeof AA × 1 and Q ∗ represents the generalized inverse of theprojection coefficient matrixQ. The calculation ofQ ∗ directlywill be slightly complicated and consume reconstruction time.It can be replaced by other calculations, which can save com-puting time. According to the following principles, the first-order iterative method is used to obtain Q ∗.

If the initial estimate of the generalized inverse Q ∗ ofthe projection coefficient matrix Q of YW × AA is set toN0, set the residual S0 =DRðTÞ −QN0 to meet γS0 < 1, whereγS0 represents the spectral radius of S0 and DRðQÞ is theorthogonal matrix of Q; then, the sequence ½N0,N1 ⋯⋯NK ,NK+1,⋯⋯ � can be expressed by

NK + 1 =Nk +N0 −N0QNK , K = 0, 1⋯⋯: ð2Þ

2 Computational and Mathematical Methods in Medicine

When K ⟶∞, the equation converges to Q ∗; then,the corresponding sequence of the residual value meets therequirements of the following equation.

║SK+1║≤║S0║SK║, K = 0, 1,⋯⋯ : ð3Þ

The norm of any multiplication matrix conforms to SK=DRðTÞ −QNK .

In order to facilitate the approximate value N0 of the ini-tial value of Q ∗, simply consider it equal to

N0 = θQt : ð4Þ

In the equation, Qt is the transposition of Q and θ is anactual value and conforms to the following equation:

0 < θ < 2λ1 QQt� � : ð5Þ

In the equation, λ1 ðQQtÞ is the largest nonzero eigen-value of QQt .

In order to avoid the difficulty of calculating the rela-tively large values of Q and Qq, both sides of the equationin equation (3) are multiplied by the projection value D atthe same time, and we get

NK+1D =NKD +N0D −N0QNKD, K = 0, 1⋯⋯: ð6Þ

NK+1D and NKD represent the K + 1 and Kth recon-structed images Hi

K+1 and HiK , N0D is the initial image

Hi0, and N0QNKD represents the projection of Hi, and

finally, the reconstruction is performed. The FBP algorithmis used to reconstruct the image. θ times to replace N0QNKD. When AA < YW, θ = 1; when NN > YW, θ < 2−AA/YW .Equation (6) can be simplified as follows.

HiK+1 =Hi

K +Hi0 −N0QH

iK : ð7Þ

In order to verify the performance of the algorithm, thefollowing experiments are carried out (Figure 2). In theexperiments, the value of θ is all 1.

2.4. Algorithm Steps. According to the principle of the algo-rithm, the steps of the algorithm as shown in Figure 3 are asfollows: first, the first step is to initialize θ and the termina-tion condition φ.

In the second step, when K = 0, the FBP algorithmreconstructs the image Hifbp to obtain the preprocessedimage Hi

0 = θHiflp.

The third step is to project HiK to obtain the projection

value Dk.The fourth step is to reconstruct the image according to

Dk according to FBP and multiply the result obtained by θ toobtain Hr .

The fifth step is to correct the image, and the equation isHi

K+1 =HiK +Hi

0 −Hir .

In the sixth step, make Δ = ║HiK+1 −Hi

K║≤K = k + 1; inthe last step, if Δ > φ, it will return to step three; otherwise,the loop will end.

2.5. Test Inspection Method

(1) CT scanning method: the patients participating inthe study were all in an empty stomach state beforethe examination, and they were allowed to drinkmoderately before the examination. Using the Phi-lips 16-slice spiral CT scanner, the patient wasplaced in a supine position and the chest wasscanned axially from the lung tip to the top of thediaphragm. The scanning parameters are as follows:select reconstruction layer thickness 8mm, recon-struction layer distance 8mm, tube voltage100~150 kV, and tube current 30~45mA, combinedwith the scanned image results, select a multiangle,and carefully observe in a multidirectional mannerand reconstruct the present image. All patientsreceived the first multislice spiral CT scan within 5days, the data that appeared for the first time werereconstructed by an iterative algorithm, and the sen-sitive parts of the patient were protected during eachscan. After the scan, the two attending radiologistswill read and analyze the pictures

(2) Then, 0.5mL of median cubital venous blood wascollected from each patient under fasting and trans-ferred to a sterile blood collection tube with 50μL ofheparin. After centrifugation, the upper plasma wascollected and tested with an automatic biochemicalanalyzer

2.6. Statistical Methods. The data in this study were sortedout using Excel 2019 and processed by SPSS 22.0. The mea-surement data were expressed as �x ± s, and the count datawas expressed as a percentage (%). If the data betweengroups obeyed the normal distribution, the t-test was used;if not, the rank sum test was used, and α = 0:05 was used

High voltagegenerator

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Figure 1: Schematic diagram of the data flow of CT systemimaging.

3Computational and Mathematical Methods in Medicine

as the test standard. P < 0:05 indicated that the differencewas statistically significant.

3. Results

3.1. The Image of the Algorithm. In the case of using thesame dose in Figure 4, it can be seen that the noise of theFigure 4(b) image is relatively large and the graininess isstrong. After the Figure 4(a) image uses the FBP reconstruc-tion algorithm for noise processing, the image definition ishigher, the image noise level is greatly improved, and thesmoothness of the image is improved.

3.2. Video Image. The iterative reconstruction algorithmreconstructs the image based on the estimation of the statis-tical model of the observation data. Figure 5(a) is the imag-ing result of the FBP reconstruction algorithm. Figure 5(b)shows the marked part in Figure 5(a). The FBP reconstruc-tion algorithm can extract image features well (Figure 5).

3.3. Error Comparison of Different Boundary Pixels. For thesame local area, 4, 6, and 8 pixels were selected to comparethe measured values of FBP reconstruction in the ROI. Atdifferent pixel numbers, different distance measurementvalues will be obtained. As the number of local dataincreases, the smaller the distance measurement value, thesmaller the error obtained, which means that the more localdata are selected, the better the ROI reconstruction obtainedand the better the image effect (Figure 6).

3.4. CT Detection Results of Fasting Blood Glucose Levels inPatients. All patients accepted examinations for the fastingblood glucose level. As per the Guidelines for Preventionand Treatment of Type 2 Diabetes in China, the 138 patientswere divided into the good blood glucose level group(<7mmol/L), normal blood glucose level group (7-10mmol/L), and poor blood glucose group (>10mmol/L), as shown inFigure 7. There was no statistical difference in the age, theaverage duration of tuberculosis, and the average duration ofdiabetes (P > 0:05).

The results are shown in Figure 8. Patient 1 was a 62-year-old male. CT examination suggested multiple lunginfections. The lesions concentrated on the left upper lobeand right lower lobe, accompanied by multiple cavities anda small amount of bilateral pleural effusion (Figures 8(a)–8(c)). Patient 2 was a 60-year-old male. CT examinationshowed exudative lesions in both lungs and multiple cavitiesin the upper right and lower left lungs, suggesting bilaterallung infection (Figures 8(d)–8(f)). Patient 3 was a 54-year-old female, and the CT examination showed pulmonarytuberculosis in the upper left lung, accompanied by cavita-tion and mediastinal lymph node calcification(Figures 8(g)–8(i)).

CT examination of the most frequent sites of lesions inpatients with diabetes and tuberculosis showed that the aver-age number of lung lesions involved in patients with goodfasting blood glucose levels was 5:8 ± 3:9, that in patientswith normal fasting blood glucose levels was 6:0 ± 4:1, andthat in patients with poor fasting blood glucose levels was6:1 ± 3:8. There was no significant difference in the number

Image update

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Compare

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Figure 2: Flow chart of the reconstruction algorithm principle.

Initialization

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FBP image update

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End

θ

YesNo

H0i = Hiflp

Dk

Hr

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Figure 3: Flow chart of algorithm steps.


of lung lesions involved in patients with different fastingblood glucose levels (P > 0:05) (Figure 9).

CT images of patients with diabetes and pulmonarytuberculosis usually showed segmental, large lobular fusionlesions and consolidation, cavitation, pleural effusion, andlymph node enlargement. Then, the relationship betweendifferent fasting blood glucose levels and the imaging mani-

festations was analyzed. The results showed that of the 25patients with good fasting blood glucose levels, 14 hadthree-segment large leaf-like shadows, 18 had proliferativenodular shadows, 11 had bronchial inflation signs, 7 hadwall-less cavities, 7 had thick-walled cavities, 8 had multiplecavities in the lungs, 4 had thin-walled cavities, and 6 hadbronchial tuberculosis. Of 41 patients with normal fasting

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Figure 4: Comparison of low-dose lung original image and reconstruction algorithm: (a) CT image reconstructed by the FBP algorithm; (b)original CT image.

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Figure 5: Image reconstruction algorithm feature label map of the lungs: (a) CT image reconstructed by the FBP algorithm; (b) the markedpart in (a).

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Figure 6: Distance measurement values of different pixel reconstruction results. ∗ represents a statistical difference compared with pixel 8(P < 0:05).


blood glucose levels, 25 cases had three-segment leaf-likelarge shadows, 30 cases developed proliferative nodularshadows, 22 cases had bronchial inflation signs, 15 caseshad wall-less cavities, 14 cases had thick-walled cavities, 17cases had multiple cavities in the lungs, 7 cases had thin-walled cavities, and 14 cases had bronchial tuberculosis. Of72 patients with poor fasting blood glucose levels, 57 caseshad three-segment leaf-like large shadows, 53 cases had pro-liferative nodular shadows, 55 cases had bronchial inflation,36 cases had wall-less cavities, 35 cases had thick-walled cav-ities, 40 cases had multiple cavities in the lungs, 12 cases hadthin-walled cavities, and 35 cases had bronchial tuberculosis.Figure 10 shows the incidence of eight types of imagingmanifestations with different fasting blood glucose levels.The results showed that there was no significant differencein the incidence of proliferative nodules and thin-walled cav-ities in patients with different fasting blood glucose levels(P < 0:05), but segmental leaf-like large-scale shadows, bron-chial inflation sign, wall-less cavities, thick-walled cavities,multiple cavities in lungs, and the incidence of bronchial

tuberculosis showed statistically significant differences(P < 0:05).

3.5. Comparison of the Effects of Dietary Interventionbetween the Two Groups of Patients. After 138 patients wererandomly divided into groups, the effects of dietary inter-vention on the various indicators of the patients were ana-lyzed. The results showed that in the observation group,the glucose level was 11:32 ± 1:48mmol/L 2h after mealand it was 14:55 ± 1:87mmol/L in the control group. Com-pared with the control group, the observation group’s bloodglucose level 2 h after meal improved significantly, and thedifference was statistically significant (P < 0:05). After dietintervention, it was found that in the observation group,the pleural effusion was significantly reduced in 61 patientsand the tuberculosis lesions of 51 patients were significantlyreduced; in the control group, the pleural effusion of 32patients was significantly reduced and the tuberculosislesions of 27 patients were significantly reduced. The differ-ence was statistically significant in terms of the number of

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lesions reduced (P < 0:05). The hemoglobin level in theobservation group after intervention was 7:1 ± 1:26, and thatin the control group was 8:91 ± 2:03; the difference was sta-tistically significant (P < 0:05) (Figure 11).

4. Discussion

Diabetes is a chronic metabolic disease. Due to metabolic disor-der and low immunity, the patients are susceptible to tuberculo-sis. Diabetes complicated with tuberculosis develops rapidly,and the lesions are widely distributed and easy to form cavities,which makes it difficult to treat [23]. The treatment and prog-nosis of diabetes with pulmonary tuberculosis depend to a cer-tain extent on the degree of blood glucose control. A reasonablediet can provide patients with sufficient nutrition and avoidhyperglycemia. Therefore, the patient’s fasting blood glucoselevel can be used as a blood glucose detection indicator [24].To prevent the drug and food intake from interacting withblood sugar levels which will affect the clinical treatment, it isamust that patients undergo regular chest examinations. Diabe-tes and tuberculosis lesions with different levels of fasting bloodglucose may have different CT manifestations.

The choice of a CT contrast agent is related to the detec-tion ability of the lesion and the early clinical diagnosis rateof the disease. Compared with single-slice spiral CT, multi-slice spiral CT has multiple rows of detectors, which not onlyincreases the scanning speed but also shortens the time ofone revolution to 0.5 s. It can also obtain multilayer imagesduring one revolution. Because it is a fast volume scan, it is

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Figure 8: CT examination results of patients with diabetes and pulmonary tuberculosis.

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Figure 9: Comparison of the number of lung lesions involved indifferent fasting blood glucose levels.


possible to conduct uninterrupted data collection on a largearea of the body in a short time, and the informationobtained can also be increased. After computer processing,the imaging of multiple technologies is completed, and theimage quality is higher. The virtual endoscopy not only is

more real but also improves the detection rate of smallerlesions and mucosal lesions. On this basis, the image isreconstructed by the FBP algorithm, which can effectivelyimprove the image quality. The reconstruction algorithmdivides the entire image processing process into many times

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Figure 10: The incidence of CT imaging manifestations in patients with different fasting blood glucose levels.


and gradually improves the image processing. In most low-dose scans, the scanned image processing parameters mustbe optimized to maximize the image quality. Algorithmsand various noise reduction techniques can achieve the effectof smoothing the image [25]. The FBP algorithm is appliedto low-dose CT images for denoising, iterative reconstruc-tion, image postprocessing, and sinusoidal image prepro-cessing; the FBP algorithm can denoise low-dose CTimages, which is consistent with the results of this study.This study also used low-dose scans, and the results showedthat the FBP reconstruction algorithm showed good superi-ority in lung CT imaging scans. After the image is recon-structed by the iterative algorithm, the image quality andimage information are significantly improved. For the samelocal area, compare the measured value of the FBP recon-struction in the ROI. The more the number of local data inthe image, the smaller the distance measurement value andthe smaller the error. That is to say, the more local dataare selected, the better the effect of the ROI reconstructionimage. In this study, the relationship between fasting bloodglucose and CT findings in patients with diabetes and pul-monary tuberculosis was detected. The results showed thatdue to the existence of multiple pathological properties, theCT imaging of patients showed coexistence of multiple mor-phological and dense lesions. Because a large number ofmycobacterium invaded the patient’s lung tissue, it causedchanges in the CT imaging of the patient’s lung. The exit rate

also increases, which is consistent with research results ofBarreda et al. [26].

The bronchial inflation symptom reflects the inflammatorylesions of patients. This study found that as the fasting bloodsugar level of patients increased, the incidence of bronchialinflation syndrome also increased. Cavitation is an importantpathological feature of active tuberculosis. The results of thisstudy showed that the incidence of wall-less cavities, thick-walled cavities, and multiple pulmonary cavities increased withthe increase in the patient’s fasting blood glucose level. Addi-tionally, the incidence of bronchial tuberculosis also increased.This was in line with the results of Xia et al. [27]. The maincause may be the necrosis and liquefaction of the caseous mate-rial in the small bronchus, and the drainage bronchus and sub-stances containing tubercle bacilli adhere to the inner wall of thebronchus and cause local infection in the patient. After dietintervention, it was found that in the observation group, bloodglucose levels, pleural effusion, tuberculosis lesions, and glyco-sylated hemoglobin levels improved significantly 2h aftermeals,indicating that diet nursing intervention positively affectedblood glucose control in patients with diabetes and tuberculosis.

5. Conclusion

In this study, the FBP reconstruction algorithm was appliedto CT images of patients with diabetic tuberculosis, and therelationship between fasting blood glucose levels and CT

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Figure 11: Comparison of various indicators between the two groups of patients after intervention.


imaging findings in patients with diabetes and tuberculosiswas detected. Finally, the effect of diet intervention on theimprovement of clinical symptoms of patients was analyzed.The results show that the CT image definition of the PDFreconstruction algorithm is relatively high, and the imagehas good classification characteristics. The more local dataof the image are selected, the better the ROI reconstructionimage obtained and the better the CT image effect. The fast-ing blood glucose level is closely related to the patient’s CTmanifestations. The change of fasting blood glucose level willcause changes in CT imaging. Through personalized dietnursing intervention, the patient’s blood glucose can be con-trolled to a certain extent and clinical symptoms can beimproved, which has a certain clinical promotion value.

Data Availability

The data used to support the findings of this study are avail-able from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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ct image features of the fbp reconstruction algorithm in

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