Interrater agreement and variability in visual reading of [18F] flutemetamol PET images.

OBJECTIVE: The purpose of this study was to validate the concordance of visual ratings of [18F] flutemetamol amyloid positron emission tomography (PET) images and to investigate the correlation between the agreement of each rater and the Centiloid (CL) scale. METHODS: A total of 192 participants, clinically classified as cognitively normal (CN) (n = 59), mild cognitive impairment (MCI) (n = 65), Alzheimer's disease (AD) (n = 55), or non-AD dementia (n = 13), participated in this study. Three experts conducted visual ratings of the amyloid PET images for all 192 patients, assigning a confidence level to each rating on a three-point scale (certain, probable, or neither). The positive or negative determination of amyloid PET results was made by majority vote. The CL value was calculated using the CapAIBL pipeline. RESULTS: Overall, 101 images were determined to be positive, and 91 images were negative. Of the 101 positive images, the three raters were in complete agreement for 92 images and in disagreement for 9 images. Of the 91 negative images, the three raters were in complete agreement for 75 images and in disagreement for 16 images. Interrater reliability among the three experts was particularly high, with both Fleiss' kappa and Conger's kappa measuring 0.83 (0.76-0.89). The CL values of the unanimous positive group were significantly greater than those of the other groups, whereas the CL values of the unanimous negative group were significantly lower than those of the other groups. Images with rater disagreement had intermediate CLs. In cases with a high confidence level, the positive or negative visual ratings were in almost complete agreement. However, as confidence levels decreased, experts' visual ratings became more variable. The lower the confidence level was, the greater the number of cases with disagreement in the visual ratings. CONCLUSION: Three experts independently rated 192 amyloid PET images, achieving a high level of interrater agreement. However, in patients with intermediate amyloid accumulation, visual ratings varied. Therefore, determining positive and negative decisions in these patients should be performed with caution.

Voxel-Based Morphometry of Progressive Supranuclear Palsy Using a 3D Fast Low-angle Shot Localizer Image: A Comparison with Magnetization-Prepared Rapid Gradient Echo.

PURPOSE: Voxel-based morphometry (VBM) is widely used to investigate white matter (WM) atrophy in patients with progressive supranuclear palsy (PSP). In contrast to high-resolution 3D T1-weighted imaging such as magnetization-prepared rapid acquisition with gradient echo (MPRAGE) sequences, the utility of other 3D sequences has not been sufficiently evaluated. This study aimed to assess the feasibility of using a 3D fast low-angle shot sequence captured as a localizer image (L3DFLASH) for VBM analysis of WM atrophy patterns in patients with PSP. METHODS: This retrospective study included 12 patients with pathologically or clinically confirmed PSP, and 18 age- and sex-matched healthy controls scanned with both L3DFLASH and MPRAGE sequences. Image processing was conducted with the Computational Anatomy Toolbox 12 in statistical parametric mapping 12. In addition to the atrophic WM pattern of PSP on VBM, we assessed the WM volume agreement between the two sequences using simple linear regression and Bland-Altman plots. RESULTS: Despite the slightly larger clusters on MPRAGE, VBM using both sequences showed similar characteristics of PSP-related WM atrophy, including in the midbrain, pons, thalamus, and precentral gyrus. In contrast, VBM showed gray matter (GM) atrophy of the precuneus and right superior parietal lobule exclusively on L3DFLASH. Unlike the measured values of total intracranial volume, GM, and cerebrospinal fluid on MPRAGE, the value of WM was larger on L3DFLASH. In contrast to the total intracranial volume, brainstem, and frontal and occipital lobes, the correlation with WM volume in other regions was relatively low. However, the Bland-Altman plots demonstrated strong agreement, with over 90% of the values falling within the agreement limits. CONCLUSION: Both MPRAGE and L3DFLASH are useful for detecting PSP-related WM atrophy using VBM.

Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors.

BACKGROUND: Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning reconstruction (DLR) could be useful for image quality improvements. The purpose of this investigation using in vitro and in vivo studies was to determine the influence of sequence difference and of DLR for DWI on image quality, apparent diffusion coefficient (ADC) evaluation, and differentiation of malignant from benign head and neck tumors. METHODS: For the in vitro study, a DWI phantom was scanned by FASE and EPI sequences and reconstructed with and without DLR. Each ADC within the phantom for each DWI was then assessed and correlated for each measured ADC and standard value by Spearman's rank correlation analysis. For the in vivo study, DWIs obtained by EPI and FASE sequences were also obtained for head and neck tumor patients. Signal-to-noise ratio (SNR) and ADC were then determined based on ROI measurements, while SNR of tumors and ADC were compared between all DWI data sets by means of Tukey's Honest Significant Difference test. RESULTS: For the in vitro study, all correlations between measured ADC and standard reference were significant and excellent (0.92 ≤ ρ ≤ 0.99, p < 0.0001). For the in vivo study, the SNR of FASE with DLR was significantly higher than that of FASE without DLR (p = 0.02), while ADC values for benign and malignant tumors showed significant differences between each sequence with and without DLR (p < 0.05). CONCLUSION: In comparison with EPI sequence, FASE sequence and DLR can improve image quality and distortion of DWIs without significantly influencing ADC measurements or differentiation capability of malignant from benign head and neck tumors.

Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods.

OBJECTIVE: The Centiloid (CL) scale is a standardized measure for quantifying amyloid deposition in amyloid positron emission tomography (PET) imaging. We aimed to assess the agreement among 3 CL calculation methods: CapAIBL, VIZCalc, and Amyquant. METHODS: This study included 192 participants (mean age: 71.5 years, range: 50-87 years), comprising 55 with Alzheimer's disease, 65 with mild cognitive impairment, 13 with non-Alzheimer's dementia, and 59 cognitively normal participants. All the participants were assessed using the three CL calculation methods. Spearman's rank correlation, linear regression, Friedman tests, Wilcoxon signed-rank tests, and Bland-Altman analysis were employed to assess data correlations, linear associations, method differences, and systematic bias, respectively. RESULTS: Strong correlations (rho = 0.99, p < .001) were observed among the CL values calculated using the three methods. Scatter plots and regression lines visually confirmed these strong correlations and met the validation criteria. Despite the robust correlations, a significant difference in CL value between CapAIBL and Amyquant was observed (36.1 ± 39.7 vs. 34.9 ± 39.4; p < .001). In contrast, no significant differences were found between CapAIBL and VIZCalc or between VIZCalc and Amyquant. The Bland-Altman analysis showed no observable systematic bias between the methods. CONCLUSIONS: The study demonstrated strong agreement among the three methods for calculating CL values. Despite minor variations in the absolute values of the Centiloid scores obtained using these methods, the overall agreement suggests that they are interchangeable.

Liver function and image evaluation after radiotherapy for liver metastases after resection of sigmoid colon cancer a case report.

INTRODUCTION: Radiotherapy is a treatment option in the management of patients with metastatic liver disease. The aim in this case was to evaluate radiation-induced dysfunctional liver lesions using 99mTc-GSA-SPECT, Gd-EOB-DTPA-enhanced MRI, and radiation dose distribution in a patient after radiation therapy. PRESENTATION OF CASE: After sigmoid colon resection, three liver metastases were treated with radiotherapy at the same time. Liver function after radiotherapy was determined to be A according to the Child-Turcott-Pugh classification. 99mTc-GSA-SPECT showed a wider reduction in uptake than Gd-EOB-DTPA MRI at all three sites. HH15 showed decreased liver function. DISCUSSION: In the 99mTc-GSA-SPECT and Gd-EOB-DTPA MRI hepatocyte phases, residual signals of normal hepatocytes were observed despite irradiation at three sites. Additional treatment could be considered for the two recurrent lesions because there was no deterioration of liver function in post-irradiation imaging findings and blood sampling. CONCLUSION: 99mTc-GSA-SPECT and EOB-MRI showed characteristic findings for evaluation of liver function after radiotherapy for multiple liver metastases, suggesting the need for both imaging evaluations. It is now possible to choose whether to perform local additional treatment (additional radiation, RFA) or other chemotherapy for liver metastases after recurrence.

Compressed sensing with deep learning reconstruction: Improving capability of gadolinium-EOB-enhanced 3D T1WI.

PURPOSE: The purpose of this study was to determine the utility of compressed sensing (CS) with deep learning reconstruction (DLR) for improving spatial resolution, image quality and focal liver lesion detection on high-resolution contrast-enhanced T1-weighted imaging (HR-CE-T1WI) obtained by CS with DLR as compared with conventional CE-T1WI with parallel imaging (PI). METHODS: Seventy-seven participants with focal liver lesions underwent conventional CE-T1WI with PI and HR-CE-T1WI, surgical resection, transarterial chemoembolization, and radiofrequency ablation, followed by histopathological or >2-year follow-up examinations in our hospital. Signal-to-noise ratios (SNRs) of liver, spleen and kidney were calculated for each patient, after which each SNR was compared by means of paired t-test. To compare focal lesion detection capabilities of the two methods, a 5-point visual scoring system was adopted for a per lesion basis analysis. Jackknife free-response receiver operating characteristic (JAFROC) analysis was then performed, while sensitivity and false positive rates (/data set) for consensus assessment of the two methods were also compared by using McNemar's test or the signed rank test. RESULTS: Each SNR of HR-CE-T1WI was significantly higher than that of conventional CE-T1WI with PI (p < 0.05). Sensitivities for consensus assessment showed that HR-CE-MRI had significantly higher sensitivity than conventional CE-T1WI with PI (p = 0.004). Moreover, there were significantly fewer FP/cases for HR-CE-T1WI than for conventional CE-T1WI with PI (p = 0.04). CONCLUSION: CS with DLR are useful for improving spatial resolution, image quality and focal liver lesion detection capability of Gd-EOB-DTPA enhanced 3D T1WI without any need for longer breath-holding time.

Diffusion weighted imaging with reverse encoding distortion correction: Improvement of image quality and distortion for accurate ADC evaluation in in vitro and in vivo studies.

PURPOSE: The purpose of this in vivo study was to determine the effect of reverse encoding direction (RDC) on apparent diffusion coefficient (ADC) measurements and its efficacy for improving image quality and diagnostic performance for differentiating malignant from benign tumors on head and neck diffusion-weighted imaging (DWI). METHODS: Forty-eight patients with head and neck tumors underwent DWI with and without RDC and pathological examinations. Their tumors were then divided into two groups: malignant (n = 21) and benign (n = 27). To determine the utility of RDC for DWI, the difference in the deformation ratio (DR) between DWI and T2-weighted images of each tumor was determined for each tumor area. To compare ADC measurement accuracy of DWIs with and without RDC for each patient, ADC values for tumors and spinal cord were determined by using ROI measurements. To compare DR and ADC between two methods, Student's t-tests were performed. Then, ADC values were compared between malignant and benign tumors by Student's t-test on each DWI. Finally, sensitivity, specificity and accuracy were compared by means of McNemar's test. RESULTS: DR of DWI with RDC was significantly smaller than that without RDC (p < 0.0001). There were significant differences in ADC between malignant and benign lesions on each DWI (p < 0.05). However, there were no significant difference of diagnostic accuracy between the two DWIs (p > 0.05). CONCLUSION: RDC can improve image quality and distortion of DWI and may have potential for more accurate ADC evaluation and differentiation of malignant from benign head and neck tumors.

Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study.

OBJECTIVES: Evaluation of in-stent restenosis (ISR), especially for small stents, remains challenging during computed tomography (CT) angiography. We used deep learning reconstruction to quantify stent strut thickness and lumen vessel diameter at the stent and compared it with values obtained using conventional reconstruction strategies. METHODS: We examined 166 stents in 85 consecutive patients who underwent CT and invasive coronary angiography (ICA) within 3 months of each other from 2019-2021 after percutaneous coronary intervention with coronary stent placement. The presence of ISR was defined as percent diameter stenosis ≥ 50% on ICA. We compared a super-resolution deep learning reconstruction, Precise IQ Engine (PIQE), and a model-based iterative reconstruction, Forward projected model-based Iterative Reconstruction SoluTion (FIRST). All images were reconstructed using PIQE and FIRST and assessed by two blinded cardiovascular radiographers. RESULTS: PIQE had a larger full width at half maximum of the lumen and smaller strut than FIRST. The image quality score in PIQE was higher than that in FIRST (4.2 ± 1.1 versus 2.7 ± 1.2, p < 0.05). In addition, the specificity and accuracy of ISR detection were better in PIQE than in FIRST (p < 0.05 for both), with particularly pronounced differences for stent diameters < 3.0 mm. CONCLUSION: PIQE provides superior image quality and diagnostic accuracy for ISR, even with stents measuring < 3.0 mm in diameter. CLINICAL RELEVANCE STATEMENT: With improvements in the diagnostic accuracy of in-stent stenosis, CT angiography could become a gatekeeper for ICA in post-stenting cases, obviating ICA in many patients after recent stenting with infrequent ISR and allowing non-invasive ISR detection in the late phase. KEY POINTS: • Despite CT technology advancements, evaluating in-stent stenosis severity, especially in small-diameter stents, remains challenging. • Compared with conventional methods, the Precise IQ Engine uses deep learning to improve spatial resolution. • Improved diagnostic accuracy of CT angiography helps avoid invasive coronary angiography after coronary artery stenting.

Pulmonary MRI with ultra-short TE using single- and dual-echo methods: comparison of capability for quantitative differentiation of non- or minimally invasive adenocarcinomas from other lung cancers with that of standard-dose thin-section CT.

OBJECTIVE: The purpose of this study was thus to compare capabilities for quantitative differentiation of non- and minimally invasive adenocarcinomas from other of pulmonary MRIs with ultra-short TE (UTE) obtained with single- and dual-echo techniques (UTE-MRISingle and UTE-MRIDual) and thin-section CT for stage IA lung cancer patients. METHODS: Ninety pathologically diagnosed stage IA lung cancer patients who underwent thin-section standard-dose CT, UTE-MRISingle, and UTE-MRIDual, surgical treatment and pathological examinations were included in this retrospective study. The largest dimension (Dlong), solid portion (solid Dlong), and consolidation/tumor (C/T) ratio of each nodule were assessed. Two-tailed Student's t-tests were performed to compare all indexes obtained with each method between non- and minimally invasive adenocarcinomas and other lung cancers. Receiver operating characteristic (ROC)-based positive tests were performed to determine all feasible threshold values for distinguishing non- or minimally invasive adenocarcinoma (MIA) from other lung cancers. Sensitivity, specificity, and accuracy were then compared by means of McNemar's test. RESULTS: Each index showed significant differences between the two groups (p < 0.0001). Specificities and accuracies of solid Dlong for UTE-MRIDual2nd echo and CTMediastinal were significantly higher than those of solid Dlong for UTE-MRISingle and UTE-MRIDual1st echo and all C/T ratios except CTMediastinal (p < 0.05). Moreover, the specificities and accuracies of solid Dlong and C/T ratio were significantly higher than those of Dlong for each method (p < 0.05). CONCLUSION: Pulmonary MRI with UTE is considered at least as valuable as thin-section CT for quantitative differentiation of non- and minimally invasive adenocarcinomas from other stage IA lung cancers. CLINICAL RELEVANCE STATEMENT: Pulmonary MRI with UTE's capability for quantitative differentiation of non- and minimally invasive adenocarcinomas from other lung cancers in stage IA lung cancer patients is equal or superior to that of thin-section CT. KEY POINTS: • Correlations were excellent for pathologically examined nodules with the largest dimensions (Dlong) and a solid component (solid Dlong) for all indexes (0.95 ≤ r ≤ 0.99, p < 0.0001). • Pathologically examined Dlong and solid Dlong obtained with all methods showed significant differences between non- and minimally invasive adenocarcinomas and other lung cancers (p < 0.0001). • Solid tumor components are most accurately measured by UTE-MRIDual2nd echo and CTMediastinal, whereas the ground-glass component is imaged by UTE-MRIDual1st echo and CTlung with high accuracy. UTE-MRIDual predicts tumor invasiveness with 100% sensitivity and 87.5% specificity at a C/T threshold of 0.5.

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in different body fields. However, there have been no reports regarding the utility of DLR for image quality and T-factor assessment improvements on T2-weighted imaging (T2WI), short inversion time (TI) inversion recovery (STIR) imaging, and unenhanced- and contrast-enhanced (CE) 3D fast spoiled gradient echo (GRE) imaging with and without CS in comparison with thin-section multidetector-row CT (MDCT) for non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the utility of DLR for improving image quality and the appropriate sequence for T-category assessment for NSCLC patients. METHODS: As subjects for this study, 213 pathologically diagnosed NSCLC patients who underwent thin-section MDCT and MR imaging as well as T-factor diagnosis were retrospectively enrolled. SNR of each tumor was calculated and compared by paired t-test for each sequence with and without DLR. T-factor for each patient was assessed with thin-section MDCT and all MR sequences, and the accuracy for T-factor diagnosis was compared among all sequences and thin-section CT by means of McNemar's test. RESULTS: SNRs of T2WI, STIR imaging, unenhanced thin-section Quick 3D imaging, and CE-thin-section Quick 3D imaging with DLR were significantly higher than SNRs of those without DLR (P < 0.05). Diagnostic accuracy of STIR imaging and CE-thick- or thin-section Quick 3D imaging was significantly higher than that of thin-section CT, T2WI, and unenhanced thick- or thin-section Quick 3D imaging (P < 0.05). CONCLUSION: DLR is thus considered useful for image quality improvement on MR imaging. STIR imaging and CE-Quick 3D imaging with or without CS were validated as appropriate MR sequences for T-factor evaluation in NSCLC patients.

Area-Detector Computed Tomography for Pulmonary Functional Imaging.

An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.

Histopathological findings affect quantitative values of single photon emission computed tomography in patients with antiresorptive agent-related osteonecrosis of the jaws.

Objectives: This study investigated the relationships between quantitative values calculated from bone single photon emission computed tomography/computed tomography (SPECT/CT) images and histopathological findings observed in surgical specimens from patients with antiresorptive agent-related osteonecrosis of the jaw (ARONJ); it sought to clarify histopathological factors that cause accumulation in bone SPECT/CT images of patients with ARONJ. Methods: This study included 81 pathological specimens of 21 lesions obtained from 18 patients with ARONJ who underwent SPECT/CT and jaw resection. The maximum standardized uptake value (SUVmax) of each volume of interest of the specimens was calculated using RAVAT® software. The ratio of the SUVmax to the mean value of SUVmax in temporal bone was termed rSUVmax. The rSUVmax and pathological findings (sequestration, degree of fibrosis, degree of trabecular bone destruction, degree of inflammatory cell infiltration, and vascularity) were compared using the Mann-Whitney U test and the Kruskal-Wallis test. Results: In univariate analysis with rSUVmax as the dependent variable, the pathological findings of sequestration (P=0.058), degree of fibrosis (P=0.810), degree of trabecular bone destruction (P=0.237), degree of inflammatory cell infiltration (P=0.120), and vascularity (P=0.111) showed no significant difference among the groups for each variable. Conclusions: We found no association between quantitative values in bone SPECT/CT and histological changes in ARONJ, probably because bone SPECT/CT has limited spatial resolution. Limitations of this study may include the imaging findings of a decrease in tracer accumulation because of an involucrum of necrosed bone, various histopathological findings in ARONJ, and failure to consider the effect of preoperative anti-inflammatory treatment.

Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies

PURPOSE: Deep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies. The purpose of this study was to determine the effect of DLR for magnetic resonance imaging (MRI) in terms of image quality improvement, apparent diffusion coefficient (ADC) assessment, and IVIM index evaluation on DWI through in vitro and in vivo studies. METHODS: For the in vitro study, a phantom recommended by the Quantitative Imaging Biomarkers Alliance was scanned and reconstructed with and without DLR, and 15 patients with brain tumors with normal-appearing gray and white matter examined using IVIM and reconstructed with and without DLR were included in the in vivo study. The ADCs of all phantoms for DWI with and without DLR, as well as the coefficient of variation percentage (CV%), and ADCs and IVIM indexes for each participant, were evaluated based on DWI with and without DLR by means of region-of-interest measurements. For the in vitro study, using the mean ADCs for all phantoms, a t-test was adopted to compare DWI with and without DLR. For the in vivo study, a Wilcoxon signed-rank test was used to compare the CV% between the two types of DWI. In addition, the Wilcoxon signed-rank test was used to compare the ADC, true diffusion coefficient (D), pseudodiffusion coefficient (D*), and percentage of water molecules in micro perfusion within 1 voxel (f) with and without DLR; the limits of agreement of each parameter were determined through a Bland-Altman analysis. RESULTS: The in vitro study identified no significant differences between the ADC values for DWI with and without DLR (P > 0.05), and the CV% was significantly different for DWI with and without DLR (P < 0.05) when b values ≥250 s/mm2 were used. The in vivo study revealed that D* and f with and without DLR were significantly different (P < 0.001). The limits of agreement of the ADC, D, and D* values for DWI with and without DLR were determined as 0.00 ± 0.51 × 10-3, 0.00 ± 0.06 × 10-3, and 1.13 ± 4.04 × 10-3 mm2/s, respectively. The limits of agreement of the f values for DWI with and without DLR were determined as -0.01 ± 0.07. CONCLUSION: Deep learning reconstruction for MRI has the potential to significantly improve DWI quality at higher b values. It has some effect on D* and f values in the IVIM index evaluation, but ADC and D values are less affected by DLR.

Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images.

PURPOSE: Deep learning reconstruction (DLR) has been introduced by major vendors, tested for CT examinations of a variety of organs, and compared with other reconstruction methods. The purpose of this study was to compare the capabilities of DLR for image quality improvement and lung texture evaluation with those of hybrid-type iterative reconstruction (IR) for standard-, reduced- and ultra-low-dose CTs (SDCT, RDCT and ULDCT) obtained with high-definition CT (HDCT) and reconstructed at 0.25-mm, 0.5-mm and 1-mm section thicknesses with 512 × 512 or 1024 × 1024 matrixes for patients with various pulmonary diseases. MATERIALS AND METHODS: Forty age-, gender- and body mass index-matched patients with various pulmonary diseases underwent SDCT (CT dose index volume : mean ± standard deviation, 9.0 ± 1.8 mGy), RDCT (CTDIvol: 1.7 ± 0.2 mGy) and ULDCT (CTDIvol: 0.8 ± 0.1 mGy) at a HDCT. All CT data set were then reconstructed with 512 × 512 or 1024 × 1024 matrixes by means of hybrid-type IR and DLR. SNR of lung parenchyma and probabilities of all lung textures were assessed for each CT data set. SNR and detection performance of each lung texture reconstructed with DLR and hybrid-type IR were then compared by means of paired t tests and ROC analyses for all CT data at each section thickness. RESULTS: Data for each radiation dose showed DLR attained significantly higher SNR than hybrid-type IR for each of the CT data (p < 0.0001). On assessments of all findings except consolidation and nodules or masses, areas under the curve (AUCs) for ULDCT with hybrid-type IR for each section thickness (0.91 ≤ AUC ≤ 0.97) were significantly smaller than those with DLR (0.97 ≤ AUC ≤ 1, p < 0.05) and the standard protocol (0.98 ≤ AUC ≤ 1, p < 0.05). CONCLUSION: DLR is potentially more effective for image quality improvement and lung texture evaluation than hybrid-type IR on all radiation dose CTs obtained at HDCT and reconstructed with each section thickness with both matrixes for patients with a variety of pulmonary diseases.

Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods.

PURPOSE: To compare the capability of CTs obtained with a silver or copper x-ray beam spectral modulation filter (Ag filter and Cu filter) and reconstructed with FBP, hybrid-type IR and deep learning reconstruction (DLR) for radiation dose reduction for lung nodule detection using a chest phantom study. MATERIALS AND METHODS: A chest CT phantom was scanned with a 320-detector row CT with Ag filter at 0.6, 1.6 and 2.5 mGy and Cu filters at 0.6, 1.6, 2.5 and 9.6 mGy, and reconstructed with the aforementioned methods. To compare image quality of all the CT data, SNRs and CNRs for any nodule were calculated for all protocols. To compare nodule detection capability among all protocols, the probability of detection of any nodule was assessed with a 5-point visual scoring system. Then, ROC analyses were performed to compare nodule detection capability of Ag and Cu filters for each radiation dose data with the same method and of the three methods for any radiation dose data and obtained with either filter. RESULTS: At any of the doses, SNR, CNR and area under the curve for the Ag filter were significantly higher or larger than those for the Cu filter (p < 0.05). Moreover, with DLR, those values were significantly higher or larger than all the others for CTs obtained with any of the radiation doses and either filter (p < 0.05). CONCLUSION: The Ag filter and DLR can significantly improve image quality and nodule detection capability compared with the Cu filter and other reconstruction methods at each of radiation doses used.

Advances for Pulmonary Functional Imaging: Dual-Energy Computed Tomography for Pulmonary Functional Imaging.

Dual-energy computed tomography (DECT) can improve the differentiation of material by using two different X-ray energy spectra, and may provide new imaging techniques to diagnostic radiology to overcome the limitations of conventional CT in characterizing tissue. Some techniques have used dual-energy imaging, which mainly includes dual-sourced, rapid kVp switching, dual-layer detectors, and split-filter imaging. In iodine images, images of the lung's perfused blood volume (PBV) based on DECT have been applied in patients with pulmonary embolism to obtain both images of the PE occluding the pulmonary artery and the consequent perfusion defects in the lung's parenchyma. PBV images of the lung also have the potential to indicate the severity of PE, including chronic thromboembolic pulmonary hypertension. Virtual monochromatic imaging can improve the accuracy of diagnosing pulmonary vascular diseases by optimizing kiloelectronvolt settings for various purposes. Iodine images also could provide a new approach in the area of thoracic oncology, for example, for the characterization of pulmonary nodules and mediastinal lymph nodes. DECT-based lung ventilation imaging is also available with noble gases with high atomic numbers, such as xenon, which is similar to iodine. A ventilation map of the lung can be used to image various pulmonary diseases such as chronic obstructive pulmonary disease.

Three-Dimensional Gradient-Echo-Based Amide Proton Transfer-Weighted Imaging of Brain Tumors: Comparison With Two-Dimensional Spin-Echo-Based Amide Proton Transfer-Weighted Imaging.

OBJECTIVE: Although amide proton transfer-weighted (APTw) imaging is reported by 2-dimensional (2D) spin-echo-based sequencing, 3-dimensional (3D) APTw imaging can be obtained by gradient-echo-based sequencing. The purpose of this study was to compare the efficacy of APTw imaging between 2D and 3D imaging in patients with various brain tumors. METHODS: A total of 49 patients who had undergone 53 examinations [5 low-grade gliomas (LGG), 16 high-grade gliomas (HGG), 6 malignant lymphomas, 4 metastases, and 22 meningiomas] underwent APTw imaging using 2D and 3D sequences. The magnetization transfer ratio asymmetry (MTR asym ) was assessed by means of region of interest measurements. Pearson correlation was performed to determine the relationship between MTR asym for the 2 methods, and Student's t test to compare MTR asym for LGG and HGG. The diagnostic accuracy to differentiate HGG from LGG of the 2 methods was compared by means of the McNemar test. RESULTS: Three-dimensional APTw imaging showed a significant correlation with 2D APTw imaging ( r = 0.79, P < 0.0001). The limits of agreement between the 2 methods were -0.021 ± 1.42%. The MTR asym of HGG (2D: 1.97 ± 0.96, 3D: 2.11 ± 0.95) was significantly higher than those of LGG (2D: 0.46 ± 0.89%, P < 0.01; 3D: 0.15 ± 1.09%, P < 0.001). The diagnostic performance of the 2 methods to differentiate HGG from LGG was not significantly different ( P = 1). CONCLUSIONS: The potential capability of 3D APTw imaging is equal to or greater than that of 2D APTw imaging and is considered at least as valuable in patients with brain tumors.

Examination of postmortem changes in the lungs, trachea, and bronchi in a rat model imaged with small-animal computed tomography.

Objectives: As less autopsies are performed, the need for postmortem computed tomography (PMCT) as an alternative is increasing. It is important to know how postmortem changes over time are reflected on CT, in order to improve the diagnostic capability of PMCT and replace forensic pathology evaluations such as time of death estimation. Methods: In this study, we examined temporal changes on postmortem chest CT images of a rat model. After acquiring antemortem images under isoflurane inhalation anesthesia, the rats were euthanized with a rapid intravenous injection of anesthetics. From immediately after death to 48 hours postmortem, chest images were acquired using small-animal CT. The 3D images were then evaluated on a workstation to measure the antemortem and postmortem air content in the lungs, trachea, and bronchi over time. Results: The air content in the lungs decreased, but the air content of the trachea and bronchi temporarily increased 1-12 hours postmortem, then decreased at 48 hours postmortem. Therefore, the measurement of trachea and bronchi volumes on PMCT could be an objective way to estimate the time of death. Conclusions: While the air content of the lungs decreased, the volume of the trachea and bronchi temporarily increased after death, indicating the potential to use such measurements to estimate time of death.

Reverse encoding distortion correction for diffusion-weighted MRI: Efficacy for improving image quality and ADC evaluation for differentiating malignant from benign areas in suspected prostatic cancer patients.

PURPOSE: The purpose of this study was to determine the influenceof reverse encoding distortion correction (RDC) on ADC measurement and its efficacy for improving image quality and diagnostic performance for differentiating malignant from benign prostatic areas on prostatic DWI. METHODS: Forty suspected prostatic cancer patients underwent DWI with or without RDC (i.e. RDC DWI or DWI) using a 3 T MR system as well as pathological examinations. The pathological examination results indicated 86 areas were malignant while 86 out of 394 areas were computationally selected as benign. SNR for benign areas and muscle and ADCs for malignant and benign areas were determined by ROI measurements on each DWI. Moreover, overall image quality was assessed with a 5-point visual scoring system on each DWI. Paired t-test or Wilcoxon's signed rank test was performed to compare SNR and overall image quality for DWIs. ROC analysis was then used to compare the diagnostic performance, and sensitivity (SE), specificity (SP) and accuracy (AC) of ADC were compared between two DWI by means of McNemar's test. RESULTS: SNR and overall image quality of RDC DWI showed significant improvements when compared with those of DWI (p < 0.05). Areas under the curve (AUC), SP and AC of DWI RDC DWI (AUC: 0.85, SP: 72.1%, AC: 79.1%) were significantly better than those of DWI (AUC: 0.79, p = 0.008; SP: 64%, p = 0.02; AC: 74.4%, p = 0.008). CONCLUSION: RDC technique has the potential to improve image quality and ability to differentiate malignant from benign prostatic areas on DWIs of suspected prostatic cancer patients.

Volume-based 18 F-FDG PET analysis of cardiac sarcoidosis using the descending aorta as a reference tissue.

OBJECTIVE: 18 F-FDG PET can be used to calculate the threshold value of myocardial volume based on the mean standardised uptake value (SUV mean ) of the aorta to detect highly integrated regions of cardiac sarcoidosis. The present study investigated the myocardial volume when the position and number of volumes of interest (VOIs) were changed in the aorta. METHODS: The present study examined PET/computed tomography images of 47 consecutive cardiac sarcoidosis cases. VOIs were set at three locations in the myocardium and aorta (descending thoracic aorta, superior hepatic margin and near the pre-branch of the common iliac artery). The volume was calculated for each threshold using 1.1-1.5 times the SUV mean (median of three cross-sections) of the aorta as the threshold to detect high myocardial 18 F-FDG accumulation. The detected volume, correlation coefficient with the visually manually measured volume and the relative error were also calculated. RESULTS: The optimum threshold value for detecting high 18 F-FDG accumulation was 1.4 times that of the single cross-section of the aorta and showed the smallest relative errors of 33.84% and 25.14% and correlation coefficients of 0.974 and 0.987 for single and three cross-sections, respectively. CONCLUSION: The SUV mean of the descending aorta may be detected in good agreement with the visual high accumulation by multiplying the same threshold constant for both single and multiple cross-sections.