Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis

PurposeTo assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images.MethodsFour different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (ΔQMs) were calculated between RS and other segmentations.ResultsHighest QS and lower ΔQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median ΔQMs values ranging from 10HU and 158HU between different algorithms.ConclusionsNone of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses.     

Evaluation of Diagnostic Value of Mediastinum for Differentiation of Drug Sensitive,Multi and Extensively Drug Resistant Tuberculosis Using Chest X-Rays

Background and ObjectiveThe rise of Drug Resistant Tuberculosis (DR TB), particularly Multi DR (MDR), and Extensively DR (XDR) has reduced the rate of control of the disease. Computer aided diagnosis using Chest X-rays (CXRs) can help in mass screening and timely diagnosis of DR TB, which is essential to administer proper treatment regimens. In CXRs, lungs and mediastinum are two significant regions which contain the information about the likelihood of DR TB. The objective of this work is to analyze the shape characteristics of lungs and mediastinum to improve the diagnostics accuracy for differentiation of Drug Sensitive (DS), MDR and XDR TB using computer aided diagnostics system.MethodsThe CXR images of DS and DR TB patients are obtained from a public database. The lung fields are segmented from the CXRs using Reaction Diffusion Level Set Evolution. Mediastinum is segmented from the delineated lung masks using Chan Vese model. The shape features from each lung and mediastinum masks are extracted and analysed. The discriminative power of individual and combination of both lung and mediastinum features are evaluated using machine learning techniques for classification of DS vs MDR, MDR vs XDR and DS vs XDR TB images. The performances of classifiers are compared using standard metrics.ResultsThe proposed segmentation methods are able to delineate lungs and mediastinum from the CXR images. The extracted lung and mediastinum features are found to be statistically significant (p < 0.05) for differentiation of DS and DR TB conditions. Using the combination of both lung and mediastinum features, Multi-Layer Perceptron classifier achieves maximum F-measure of 82.4%, 81.0% and 87.0% for differentiation of DS vs MDR, MDR vs XDR and DS vs XDR, respectively.ConclusionAnalysis of mediastinum along with the lungs in chest X-rays could improve the diagnostic performance for differentiation of drug sensitive and resistant TB conditions. The proposed methodology is able to differentiate DS, MDR and XDR TB, and found to be clinically relevant. Hence, this work is useful for computer-based early detection of DS and DR TB conditions.     

Combined PET/CT for IMRT treatment planning of NSCLC: Contrast-enhanced CT images for Monte Carlo dose calculation

PurposeCombined PET/CT imaging has been proposed as an integral part of radiotherapy treatment planning (TP). Contrast-enhanced CT (ceCT) images are frequently acquired as part of the PET/CT examination to support target delineation. The aim of this dosimetric planning study was to investigate the error introduced by using a ceCT for intensity modulated radiotherapy (IMRT) TP with Monte Carlo dose calculation for non-small cell lung cancer (NSCLC).Material and methodsNine patients with NSCLC prior to chemo-RT were included in this retrospective study. For each patient non-enhanced, low-dose CT (neCT), ceCT and [¹⁸F]-FDG-PET emission data were acquired within a single examination. Manual contouring and TP were performed on the ceCT. An additional set of independent target volumes was auto-segmented in PET images. Dose distributions were recalculated on the neCT. Differences in dosimetric parameters were evaluated.ResultsDose differences in PTV and lungs were small for all patients. The maximum difference in all PTVs when using ceCT images for dose calculation was ?2.1%, whereas the mean difference was less than ?1.7%. Maximum differences in the lungs ranged from ?1.8% to 2.1% (mean: ?0.1%). In four patients an underestimation of the maximum spinal cord dose between 2% and 3.2% was observed, but treatment plans remained clinically acceptable.ConclusionsMonte Carlo based IMRT planning for NSCLC patients using ceCT allows for correct dose calculation. A direct comparison to neCT-based treatment plans revealed only small dose differences. Therefore, ceCT-based TP is clinically safe as long as the maximum acceptable dose to organs at risk is not approached.     

Enhancement of Hounsfield unit distribution in cone-beam CT images for adaptive radiation therapy: Evaluation of a hybrid correction approach

PurposeThis study aims to evaluate the accuracy of a hybrid approach combining the histogram matching (HM) and the multilevel threshold (MLT) to correct the Hounsfield Unit (HU) distribution in cone-beam CT (CBCT) images.Methods and MaterialsCBCT images acquired for ten prostate cancer patients were processed by matching their histograms to those of deformed planning CT (pCT) images obtained after applying a deformable registration (DR) process. Then, HU values corresponding to five tissue types in the pCT were assigned to the obtained CBCT images (CBCT_HM-MLT). Finally, the CBCT_HM-MLT images were compared to the deformed pCT visually and using different statistical metrics.ResultsThe visual assessment and the profiles comparison showed that the high discrepancies in the CBCT images were significantly reduced when using the proposed approach. Furthermore, the correlation values indicated that the CBCT_HM-MLT were in good agreement with the deformed pCT with correlation values ranging from 0.9893 to 0.9962. In addition, the root mean squared error (RMSE) over the entire volume was reduced from 64.15 ± 9.50 to 51.20 ± 6.76 HU. Similarly, the mean absolute error in specific tissue classes was significantly reduced especially in the soft tissue-air interfaces. These results confirmed that applying MLT after HM worked better than using only HM for which the correlation values were ranging from 0.9878 to 0.9955 and the RMSE was 55.95 ± 10.43 HU.ConclusionEvaluation of the proposed approach showed that the HM + MLT correction can improve the HU distribution in the CBCT images and generate corrected images in good agreement with the pCT.     

Normal Lung Quantification in Usual Interstitial Pneumonia Pattern: The Impact of Threshold-based Volumetric CT Analysis for the Staging of Idiopathic Pulmonary Fibrosis

BackgroundAlthough several computer-aided computed tomography (CT) analysis methods have been reported to objectively assess the disease severity and progression of idiopathic pulmonary fibrosis (IPF), it is unclear which method is most practical. A universal severity classification system has not yet been adopted for IPF.ObjectiveThe purpose of this study was to test the correlation between quantitative-CT indices and lung physiology variables and to determine the ability of such indices to predict disease severity in IPF.MethodsA total of 27 IPF patients showing radiological UIP pattern on high-resolution (HR) CT were retrospectively enrolled. Staging of IPF was performed according to two classification systems: the Japanese and GAP (gender, age, and physiology) staging systems. CT images were assessed using a commercially available CT imaging analysis workstation, and the whole-lung mean CT value (MCT), the normally attenuated lung volume as defined from −950 HU to −701 Hounsfield unit (NL), the volume of the whole lung (WL), and the percentage of NL to WL (NL%), were calculated.ResultsCT indices (MCT, WL, and NL) closely correlated with lung physiology variables. Among them, NL strongly correlated with forced vital capacity (FVC) (r = 0.92, P <0.0001). NL% showed a large area under the receiver operating characteristic curve for detecting patients in the moderate or advanced stages of IPF. Multivariable logistic regression analyses showed that NL% is significantly more useful than the percentages of predicted FVC and predicted diffusing capacity of the lungs for carbon monoxide (Japanese stage II/III/IV [odds ratio, 0.73; 95% confidence intervals (CI), 0.48 to 0.92; P < 0.01]; III/IV [odds ratio. 0.80; 95% CI 0.59 to 0.96; P < 0.01]; GAP stage II/III [odds ratio, 0.79; 95% CI, 0.56 to 0.97; P < 0.05]).ConclusionThe measurement of NL% by threshold-based volumetric CT analysis may help improve IPF staging.     

不同放射剂量CT在早期非小细胞肺癌中筛检价值对比

摘要 目的：探讨与对比不同放射剂量计算机断层扫描(Computed Tomography,CT)在早期非小细胞肺癌中筛检价值。方法：2020年1月到2020年12月选择在本院经病理确诊为肺内磨玻璃样结节患者98例作为研究对象,所有患者都给予常规剂量正电子发射计算机断层扫描(Positron emission tomography,PET)/CT检查与低剂量PET/CT检查,记录成像特征、辐射剂量并判定筛检价值。结果：低剂量PET/CT对肺部增厚、边界不规则、钙化、囊变的检出率高于常规剂量PET/CT(P<0.05)。低剂量PET/CT与常规剂量PET/CT的图像质量优良率为98.0 %和96.9 %,对比差异无统计学意义(P>0.05)。低剂量PET/CT的有效放射剂量、剂量长度乘积低于常规剂量PET/CT(P<0.05)。低剂量PET/CT的最大标准摄取值(maximum standardized uptake value,SUVmax)值低于常规剂量PET/CT(P<0.05)。低剂量PET/CT与常规剂量PET/CT分别筛检非小细胞肺癌51例与37例,筛检敏感性分别为98.1 %和69.2 %,特异性分别为100.0 %和97.8 %。结论：低放射剂量PET/CT在肺结节中的应用不会影响图像质量,且能降低辐射剂量,提高对早期非小细胞肺癌患者的筛检效果。     

Investigating centering,scan length,and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues

PurposeOptimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses.MethodsWith respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDI_vol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA).ResultsThe extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11–40% relative to those with arms up) had greater mis-centering and higher CTDI_vol and DLP at 2/4 facilities (p = 0.027–0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDI_vol and DLP across the four facilities (p < 0.001).ConclusionsMis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses.     

高分辨率CT与常规CT检查对肺小结节及早期肺癌诊断价值的对比研究

目的:对比高分辨率电子计算机断层扫描(CT)与常规CT检查对肺小结节及早期肺癌的诊断价值。方法:将2018年6月2020年1月我院收治的肺小结节及早期肺癌患者94例纳入研究。以随机数字表法将其分为观察组及对照组,每组各47例,对照组实施常规CT检查,观察组则实施高分辨率CT检查。比较两组CT肿瘤征象情况(主要包括毛刺征、分叶征、棘突征、钙化征、空泡征、支气管征、胸膜凹陷征、血管集束征),CT扫描图像质量,诊断肺小结节及早期肺癌的效能。结果:观察组各项CT肿瘤征象人数占比均高于对照组(P<0.05)。观察组CT扫描图像质量优良率为97.87%(46/47),高于对照组的72.34%(34/47)(P<0.05)。高分辨率CT诊断早期肺癌的灵敏度及准确度、特异度分别为96.67%(29/30)、95.74%(45/47)、94.12%(16/17),高于常规CT检查的74.19%(23/31)、74.47%(35/47)、75.00%(12/16)。结论:高分辨率CT检查对肺小结节及早期肺癌诊断价值显著高于常规CT检查,可作为临床肺小结节及早期肺癌诊断的有效影像学手段,值得临床应用。     

Computed Tomography Assessment of Response to Therapy: Tumor Volume Change Measurement,Truth Data,and Error

RATIONALE AND OBJECTIVES: This article describes issues and methods that are specific to the measurement of change in tumor volume as measured from computed tomographic (CT) images and how these would relate to the establishment of CT tumor volumetrics as a biomarker of patient response to therapy. The primary focus is on the measurement of lung tumors, but the approach should be generalizable to other anatomic regions. MATERIALS AND METHODS: The first issues addressed are the various sources of bias and variance in the measurement of tumor volumes, which are discussed in the context of measurement variation and its impact on the early detection of response to therapy. RESULTS AND RESOURCES: Research that seeks to identify the magnitude of some of these sources of error is ongoing, and several of these efforts are described herein. In addition, several resources for these investigations are being made available through the National Institutes of Health-funded Reference Image Database to Evaluate Response to therapy in cancer project, and these are described as well. Other measures derived from CT image data that might be predictive of patient response are described briefly, as well as the additional issues that each of these metrics may encounter in real-life applications. CONCLUSIONS: The article concludes with a brief discussion of moving from the assessment of measurement variation to the steps necessary to establish the efficacy of a metric as a biomarker for response.     

PET/CT Assessment of Response to Therapy: Tumor Change Measurement,Truth Data,and Error

We describe methods and issues that are relevant to the measurement of change in tumor uptake of ¹⁸F-fluorodeoxyglucose (FDG) or other radiotracers, as measured from positron emission tomography/computed tomography (PET/CT) images, and how this would relate to the establishment of PET/CT tumor imaging as a biomarker of patient response to therapy. The primary focus is on the uptake of FDG by lung tumors, but the approach can be applied to diseases other than lung cancer and to tracers other than FDG. The first issue addressed is the sources of bias and variance in the measurement of tumor uptake of FDG, and where there are still gaps in our knowledge. These are discussed in the context of measurement variation and how these would relate to the early detection of response to therapy. Some of the research efforts currently underway to identify the magnitude of some of these sources of error are described. In addition, we describe resources for these investigations that are being made available through the Reference Image Database for the Evaluation of Response project. Measures derived from PET image data that might be predictive of patient response as well as the additional issues that each of these metrics may encounter are described briefly. The relationship between individual patient response to therapy and utility for multicenter trials is discussed. We conclude with a discussion of moving from assessing measurement variation to the steps necessary to establish the efficacy of PET/CT imaging as a biomarker for response.     

Deep learning representations to support COVID-19 diagnosis on CT slices

Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist’s expertise, which may result in subjective evaluations.Objective: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples.Materials and methods: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic’s dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers.Results: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively.Conclusion: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.     

Dosimetric and radiation cancer risk evaluation of high resolution thorax CT during COVID-19 outbreak

PurposeThe aim of this work was to evaluate the dosimetric impact of high-resolution thorax CT during COVID-19 outbreak in the University Hospital of Parma. In two months we have performed a huge number of thorax CT scans collecting effective and equivalent organ doses and evaluating also the lifetime attributable risk (LAR) of lung and other major cancers.Materials and MethodFrom February 24th to April 28th, 3224 high-resolution thorax CT were acquired. For all patients we have examined the volumetric computed tomography dose index (CTDIvol), the dose length product (DLP), the size-specific dose estimate (SSDE) and effective dose (E₁₀₃) using a dose tracking software (Radimetrics Bayer HealthCare). From the equivalent dose to organs for each patient, LAR for lung and major cancers were estimated following the method proposed in BEIR VII which considers age and sex differences.ResultsStudy population included 3224 patients, 1843 male and 1381 female, with an average age of 67 years. The average CTDIvol, SSDE and DLP, and E₁₀₃ were 6.8 mGy, 8.7 mGy, 239 mGy·cm and 4.4 mSv respectively. The average LAR of all solid cancers was 2.1 cases per 10,000 patients, while the average LAR of leukemia was 0.2 cases per 10,000 patients. For both male and female the organ with a major cancer risk was lung.ConclusionsDespite the impressive increment in thoracic CT examinations due to COVID-19 outbreak, the high resolution low dose protocol used in our hospital guaranteed low doses and very low risk estimation in terms of LAR.     

Reversibility of Fibrotic Appearance of Lungs with Thyroxine Replacement Therapy in Patients with Severe Hypothyroidism

ObjectiveTo present 2 cases of hypothyroidism with hypoxia associated with computed tomographic (CT) features suggestive of pulmonary fibrosis that resolved with correction of the hypothyroidism.MethodsClinical case histories are described, comparative radiologic pulmonary images before and after treatment are provided, and the pertinent literature regarding possible pathologic mechanisms is reviewed.ResultsOur first patient, a 68-year-old woman, presented with symptomatic severe hypothyroidism associated with respiratory failure. A CT scan of her lungs showed appearances suggestive of pulmonary fibrosis. Replacement therapy with levothyroxine led to correction of hypoxia and radiologic abnormalities. Our second patient, a 26-year-old man, presented with symptoms suggestive of obstructive sleep apnea that persisted despite use of positive pressure ventilation. Biochemical evaluation revealed severe hypothyroidism, and a CT scan disclosed pulmonary appearances consistent with fibrosis. His symptoms and radiologic abnormalities also improved after correction of hypothyroidism with levothyroxine therapy.ConclusionRadiologic pulmonary abnormalities suggestive of fibrotic disease are associated with severe hypothyroidism. Invasive investigations such as lung biopsy should be deferred until the clinical and radiologic responses to thyroxine replacement therapy have been assessed. (Endocr Pract. 2009;15:720-724)     

La perfusion pulmonaire en morphoTEMP dans le diagnostic de l’embolie pulmonaire : comparaison à la scintigraphie pulmonaire planaire de ventilation/perfusion

The aim of this study is to assess a new tool for the diagnosis of acute pulmonary embolism (PE): single-photon emission computed tomography lung perfusion imaging associated with unenhanced computed tomography (SPECT/CT) compared to planar ventilation-perfusion (VQ) lung scintigraphy.MethodsOne hundred and three patients with suspected acute PE underwent VQ scintigraphy (two scans were uninterpretable) followed by perfusion SPECT/CT. The two types of images were analysed separately: (1) according to the modified PIOPED scintigraphic criteria for VQ lung scan and (2) with regard to SPECT/CT mismatches suggestive acute PE (segmental perfusion defects detected on SPECT images not matched with CT abnormalities).ResultsOn average, the number of segmental perfusion defects per patient was higher with SPECT/CT than with planar scintigraphy (4.3 ± 3.6 versus 2.8 ± 2.6; p < 0.001). A mismatch was found with SPECT-CT in 0% (0/18) of normal scintigraphy, and 8% (3/39) for low, 32% (8/25) for intermediate and 74% (14/19) for high probabilities of PE at scintigraphy. The presence of a SPECT/CT mismatch was also associated with higher pretest probability of acute PE (p = 0.001), even for the 25 patients in the intermediate-probability subgroup (p = 0.02). Finally, a SPECT/CT match was found in 29 patients that was not suggestive of acute PE due to the presence, in areas with perfusion defects on SPECT images, of the following CT abnormalities: hypodensity and/or emphysema (71%), condensation or atelectasis (38%), pleural disease (7%), extrapulmonary structure (14%) and/or bronchial obstruction (7%).ConclusionIn patients with suspected acute PE, the results obtained with pulmonary SPECT/CT images are consistent with those obtained with VQ scintigraphy and the pretest probability of PE. Further studies comparing SPECT/CT imaging with angiographic techniques are now required to evaluate more specifically the diagnostic value of this new tool.     

Dosimetric impact of uncorrected systematic yaw rotation in VMAT for peripheral lung SABR

AimThis study aimed to evaluate the dosimetric impact of uncorrected yaw rotational error on both target coverage and OAR dose metrics in this patient population.BackgroundRotational set up errors can be difficult to correct in lung VMAT SABR treatments, and may lead to a change in planned dose distributions.Materials and methodsWe retrospectively applied systematic yaw rotational errors in 1° degree increments up to −5° and +5° degrees in 16 VMAT SABR plans. The impact on PTV and OARs (oesophagus, spinal canal, heart, airway, chest wall, brachial plexus, lung) was evaluated using a variety of dose metrics. Changes were assessed in relation to percentage deviation from approved planned dose at 0 degrees.ResultsTarget coverage was largely unaffected with the largest mean and maximum percentage difference being 1.4% and 6% respectively to PTV D98% at +5 degrees yaw.Impact on OARs was varied. Minimal impact was observed in oesophagus, spinal canal, chest wall or lung dose metrics. Larger variations were observed in the heart, airway and brachial plexus. The largest mean and maximum percentage differences being 20.77% and 311% respectively at −5 degrees yaw to airway D0.1cc, however, the clinical impact was negligible as these variations were observed in metrics with minimal initial doses.ConclusionsNo clinically unacceptable changes to dose metrics were observed in this patient cohort but large percentage deviations from approved dose metrics in OARs were noted. OARs with associated PRV structures appear more robust to uncorrected rotational error.     

Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy

PurposeImage-guided radiation therapy could benefit from implementing adaptive radiation therapy (ART) techniques. A cycle-generative adversarial network (cycle-GAN)-based cone-beam computed tomography (CBCT)-to-synthetic CT (sCT) conversion algorithm was evaluated regarding image quality, image segmentation and dosimetric accuracy for head and neck (H&N), thoracic and pelvic body regions.MethodsUsing a cycle-GAN, three body site-specific models were priorly trained with independent paired CT and CBCT datasets of a kV imaging system (XVI, Elekta). sCT were generated based on first-fraction CBCT for 15 patients of each body region. Mean errors (ME) and mean absolute errors (MAE) were analyzed for the sCT. On the sCT, manually delineated structures were compared to deformed structures from the planning CT (pCT) and evaluated with standard segmentation metrics. Treatment plans were recalculated on sCT. A comparison of clinically relevant dose-volume parameters (D₉₈, D₅₀ and D₂ of the target volume) and 3D-gamma (3%/3mm) analysis were performed.ResultsThe mean ME and MAE were 1.4, 29.6, 5.4 Hounsfield units (HU) and 77.2, 94.2, 41.8 HU for H&N, thoracic and pelvic region, respectively. Dice similarity coefficients varied between 66.7 ± 8.3% (seminal vesicles) and 94.9 ± 2.0% (lungs). Maximum mean surface distances were 6.3 mm (heart), followed by 3.5 mm (brainstem). The mean dosimetric differences of the target volumes did not exceed 1.7%. Mean 3D gamma pass rates greater than 97.8% were achieved in all cases.ConclusionsThe presented method generates sCT images with a quality close to pCT and yielded clinically acceptable dosimetric deviations. Thus, an important prerequisite towards clinical implementation of CBCT-based ART is fulfilled.     

Utility of fast non-local means (FNLM) filter for detection of pulmonary nodules in chest CT for pediatric patient

PurposeThis study was aimed to evaluate the utility based on imaging quality of the fast non-local means (FNLM) filter in diagnosing lung nodules in pediatric chest computed tomography (CT).MethodsWe retrospectively reviewed the chest CT reconstructed with both filtered back projection (FBP) and iterative reconstruction (IR) in pediatric patients with metastatic lung nodules. After applying FNLM filter with six h values (0.0001, 0.001, 0.01, 0.1, 1, and 10) to the FBP images, eight sets of images including FBP, IR, and FNLM were analyzed. The image quality of the lung nodules was evaluated objectively for coefficient of variation (COV), contrast to noise ratio (CNR), and point spread function (PSF), and subjectively for noise, sharpness, artifacts, and diagnostic acceptability.ResultsThe COV was lowest in IR images and decreased according to increasing h values and highest with FBP images (P < 0.001). The CNR was highest with IR images, increased according to increasing h values and lowest with FBP images (P < 0.001). The PSF was lower only in FNLM filter with h value of 0.0001 or 0.001 than in IR images (P < 0.001). In subjective analysis, only images of FNLM filter with h value of 0.0001 or 0.001 rarely showed unacceptable quality and had comparable results with IR images. There were less artifacts in FNLM images with h value of 0.0001 compared with IR images (p < 0.001).ConclusionFNLM filter with h values of 0.0001 allows comparable image quality with less artifacts compared with IR in diagnosing metastatic lung nodules in pediatric chest CT.     

Elimination of biological and physical artifacts in abdomen and brain computed tomography procedures using filtering techniques

IntroductionMedical images are usually affected by biological and physical artifacts or noise, which reduces image quality and hence poses difficulties in visual analysis, interpretation and thus requires higher doses and increased radiographs repetition rate.ObjectivesThis study aims at assessing image quality during CT abdomen and brain examinations using filtering techniques as well as estimating the radiogenic risk associated with CT abdomen and brain examinations.Materials and MethodsThe data were collected from the Radiology Department at Royal Care International (RCI) Hospital, Khartoum, Sudan. The study included 100 abdominal CT images and 100 brain CT images selected from adult patients. Filters applied are namely: Mean filter, Gaussian filter, Median filter and Minimum filter. In this study, image quality after denoising is measured based on the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Metric (SSIM).ResultsThe results show that the images quality parameters become higher after applications of filters. Median filter showed improved image quality as interpreted by the measured parameters: PSNR and SSIM, and it is thus considered as a better filter for removing the noise from all other applied filters.DiscussionThe noise removed by the different filters applied to the CT images resulted in enhancing high quality images thereby effectively revealing the important details of the images without increasing the patients’ risks from higher doses.ConclusionsFiltering and image reconstruction techniques not only reduce the dose and thus the radiation risks, but also enhances high quality imaging which allows better diagnosis.