大野 良治

OBJECTIVE: To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary computed tomography angiography (CCTA) in both in vitro and in vivo studies. MATERIALS AND METHODS: For the in vitro study, a total of three-vessel tube phantoms with diameters of 3 mm, 4 mm, and 5 mm and with simulated non-calcified stepped stenosis plaques with degrees of 0%, 25%, 50%, and 75% stenosis were scanned with area-detector CT (ADCT) and ultra-high-resolution CT (UHR-CT). Then, ADCT data were reconstructed using all methods, although UHR-CT data were reconstructed with hybrid-type IR, MBIR, and DLR. For the in vivo study, patients who had undergone CCTA at ADCT were retrospectively selected, and each CCTA data set was reconstructed with all methods. To compare the image noise and measurement accuracy at each of the stenosis levels, image noise, and inner diameter were evaluated and statistically compared. To determine the effect of HR-DLR on CAD-RADS evaluation accuracy, the accuracy of CAD-RADS categorization of all CCTAs was compared by using McNemar's test. RESULTS: The image noise of HR-DLR was significantly lower than that of others on ADCT and UHR-CT (p < 0.0001). At a 50% and 75% stenosis level for each phantom, hybrid-type IR showed a significantly larger mean difference on ADCT than did others (p < 0.05). At in vivo study, 31 patients were included. Accuracy on HR-DLR was significantly higher than that on hybrid-type IR, MBIR, or DLR (p < 0.0001). CONCLUSION: HR-DLR is potentially superior for coronary arterial stenosis evaluations to hybrid-type IR, MBIR, or DLR shown on CCTA. KEY POINTS: Question How do coronary arterial stenosis evaluations by hybrid-type IR, MBIR, DLR, and HR-DLR compare to coronary CT angiography? Findings HR-DLR showed significantly lower image noise and more accurate coronary artery disease reporting and data system (CAD-RADS) evaluation than others. Clinical relevance HR-DLR is potentially superior to other reconstruction methods for coronary arterial stenosis evaluations, as demonstrated by coronary CT angiography results on ADCT and as shown in both in vitro and in vivo studies.

Hereby inviting young rising stars in chest radiology in Japan for contributing what they are working currently, we would like to show the potentials and directions of the near future research trends in the research field. I will provide a reflection on my own research topics. At the end, we also would like to discuss on how to choose the themes and topics of research: What to do or not to do? We strongly believe it will stimulate and help investigators in the field.

OBJECTIVE: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CADv) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR), mode-based iterative reconstruction (MBIR), and deep learning reconstruction (DLR) at a phantom study. METHODS: A commercially available anthropomorphic abdominal phantom was scanned five times with a 320-detector row CT at 600 mA, 400 mA, 200 mA, and 100 mA and reconstructed by four methods. Signal-to-noise ratios (SNRs) of all lesions within the arterial and portal-venous phase inserts were calculated, and SNR of the lesion phantom was compared with that of all reconstruction methods by means of Tukey's honestly significant difference (HSD) test. Then, tumor volume (V) of each nodule was automatically measured using commercially available CADv software. To compare dose reduction capability for each reconstruction method at both phases, mean differences between measured V and standard references were compared by Tukey's honestly significant difference test among the four different reconstruction methods on CT obtained at each of the four tube currents. RESULTS: With each of the tube currents, SNRs for MBIR and DLR were significantly higher than those for FBP and hybrid-type IR (p < 0.05). At the arterial phase, the mean difference in V for the CT protocol obtained at 600 or 100 mA and reconstructed with DLR was significantly smaller than that for others (p < 0.05). At the portal-venous phase, the mean differences in V for the CT protocol obtained at 100 mA and reconstructed with hybrid-type IR, MBIR, and DLR were significantly smaller than that for FBP (p < 0.05). CONCLUSIONS: Findings of our phantom study show that reconstruction method had influence on CADv merits for abdominal CT with not only standard but also reduced dose examinations and that DLR can potentially yield better image quality and CADv measurements than FBP, hybrid-type IR, or MBIR in this setting.

PURPOSE: The purpose of this study was to determine the capability of proton density with fat fraction (PD-FFQ) imaging to help assess hematopoietic ability and diagnose aplastic anemia in adults. METHODS: Between January 2021 and March 2023, patients diagnosed with aplastic anemia (AA: n = 14) or myelodysplastic syndrome (MDS: n = 14) were examined by whole-body PD-FFQ imaging, and 14 of 126 age and gender matched patients who had undergone the same PD-FFQ imaging were selected as control group. All proton density fat fraction (PDFF) index evaluations were then performed by using regions of interest (ROIs). Pearson's correlation was used to determine the relationship between blood test results and each quantitative index, and ROC-based positive test and discrimination analyses to compare capability to differentiate the AA from the non-AA group. Finally, sensitivity, specificity and accuracy of all quantitative indexes were compared by means of McNemar's test. RESULTS: Mean PDFF, standard deviation (SD) and percentage of coefficient of variation (%CV) for vertebrae showed significant correlation with blood test results (-0.52 ≤ r ≤ -0.34, p < 0.05). Specificity (SP) and accuracy (AC) of %CV of PDFF in vertebrae were significantly higher than those of mean PDFF in vertebrae and the posterior superior iliac spine (SP: p = 0.0002, AC: p = 0.0001) and SD of PDFF in vertebrae (SP: p = 0.008, AC: p = 0.008). Moreover, AC of SD of PDFF in vertebrae was significantly higher than that of mean PDFF in vertebrae and the posterior superior iliac spine (p = 0.03). CONCLUSION: Whole-body PD-FFQ imaging is useful for hematopoietic ability assessment and diagnosis of aplastic anemia in adults.

BACKGROUND: We aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T magnetic resonance imaging (MRI). METHODS: Fifty-two consecutive female patients with various pelvic diseases underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. Signal-to-noise ratio (SNR) of muscle and contrast-to-noise ratio (CNR) between fat tissue and iliac muscle on T1-weighted images (T1WI) and between myometrium and straight muscle on T2-weighted images (T2WI) were determined through region-of-interest measurements. Overall image quality (OIQ) and diagnostic confidence level (DCL) were evaluated on 5-point scales. SNRs and CNRs were compared using Tukey's test, and qualitative indexes using the Wilcoxon signed-rank test. RESULTS: SNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.010). CNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.003). OIQ of T1WI and T2WI obtained using CS with DLR were higher than that using CS without DLR or conventional PI (p < 0.001). DCL of T2WI obtained using CS with DLR was higher than that using conventional PI or CS without DLR (p < 0.001). CONCLUSION: CS with DLR provided better image quality and shorter examination time than those obtainable with PI for female pelvic 1.5-T MRI. RELEVANCE STATEMENT: CS with DLR can be considered effective for attaining better image quality and shorter examination time for female pelvic MRI at 1.5 T compared with those obtainable with PI. KEY POINTS: Patients underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. CS with DLR allowed for examination times significantly shorter than those of PI and provided significantly higher signal- and CNRs, as well as OIQ.

Chronic obstructive pulmonary disease (COPD) and asthma are common lung diseases with heterogeneous clinical presentations. Lung imaging allows evaluations of underlying pathophysiological changes and provides additional personalized approaches for disease management. This narrative review provides an overview of recent advances in chest imaging analysis using various modalities, such as computed tomography (CT), dynamic chest radiography, and magnetic resonance imaging (MRI). Visual CT assessment localizes emphysema subtypes and mucus plugging in the airways. Dedicated software quantifies the severity and spatial distribution of emphysema and the airway tree structure, including the central airway wall thickness, branch count and fractal dimension of the tree, and airway-to-lung size ratio. Nonrigid registration of inspiratory and expiratory CT scans quantifies small airway dysfunction, local volume changes and shape deformations in specific regions. Lung ventilation and diaphragm movement are also evaluated on dynamic chest radiography. Functional MRI detects regional oxygen transfer across the alveolus using inhaled oxygen and ventilation defects and gas diffusion into the alveolar-capillary barrier tissue and red blood cells using inhaled hyperpolarized 129Xe gas. These methods have the potential to determine local functional properties in the lungs that cannot be detected by lung function tests in patients with COPD and asthma. Further studies are needed to apply these technologies in clinical practice, particularly for early disease detection and tailor-made interventions, such as the efficient selection of patients likely to respond to biologics. Moreover, research should focus on the extension of healthy life expectancy in patients at higher risk and with established diseases.

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.

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.

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.

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.

Since lung magnetic resonance imaging (MRI) became clinically available, limited clinical utility has been suggested for applying MRI to lung diseases. Moreover, clinical applications of MRI for patients with lung diseases or thoracic oncology may vary from country to country due to clinical indications, type of health insurance, or number of MR units available. Because of this situation, members of the Fleischner Society and of the Japanese Society for Magnetic Resonance in Medicine have published new reports to provide appropriate clinical indications for lung MRI. This review article presents a brief history of lung MRI in terms of its technical aspects and major clinical indications, such as (1) what is currently available, (2) what is promising but requires further validation or evaluation, and (3) which developments warrant research-based evaluations in preclinical or patient studies. We hope this article will provide Investigative Radiology readers with further knowledge of the current status of lung MRI and will assist them with the application of appropriate protocols in routine clinical practice.

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.

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.

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.

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.

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 <CTDIvol>: 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.

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.

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.

BACKGROUND: Lung cancer frequently occurs in lungs with background idiopathic interstitial pneumonias (IIPs). Limited resection is often selected to treat lung cancer in patients with IIPs in whom respiratory function is already compromised. However, accurate surgical margins are essential for curative resection; underestimating these margins is a risk for residual lung cancer after surgery. We aimed to investigate the findings of lung fields adjacent to cancer segments affect the estimation of tumor size on computed tomography compared with the pathological specimen. METHODS: This analytical observational study retrospectively investigated 896 patients with lung cancer operated on at Fujita Health University from January 2015 to June 2020. The definition of underestimation was a ≥10 mm difference between the radiological and pathological maximum sizes of the tumor. RESULTS: The lung tumors were in 15 honeycomb, 30 reticulated, 207 emphysematous, and 628 normal lungs. The ratio of underestimation in honeycomb lungs was 33.3% compared to 7.4% without honeycombing (P=0.004). Multivariate analysis showed that honeycombing was a significant risk factor for tumor size underestimation. A Bland-Altman plot represented wide 95% limits of agreement, -40.8 to 70.2 mm, between the pathological and radiological maximum tumor sizes in honeycomb lungs.

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 (MTRasym) was assessed by means of region of interest measurements. Pearson correlation was performed to determine the relationship between MTRasym for the 2 methods, and Student's t test to compare MTRasym 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 MTRasym 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.

Since the Radiology Diagnostic Oncology Group (RDOG) report had been published in 1991, magnetic resonance (MR) imaging had limited clinical availability for thoracic malignancy, as well as pulmonary diseases. However, technical advancements in MR systems, such as sequence and reconstruction methods, and adjustments in the clinical protocol for gadolinium contrast media administration have provided fruitful results and validated the utility of MR imaging (MRI) for lung cancer evaluations. These techniques include: (1) contrast-enhanced MR angiography for T-factor evaluation, (2) short-time inversion recovery turbo spin-echo sequences as well as diffusion-weighted imaging (DWI) for N-factor assessment, and (3) whole-body MRI with and without DWI and with positron emission tomography fused with MRI for M-factor or TNM stage evaluation as well as for postoperative recurrence assessment of lung cancer or other thoracic tumors using 1.5 tesla (T) or 3T systems. According to these fruitful results, the Fleischner Society has changed its position to approve of MRI for lung or thoracic diseases. The purpose of this review is to analyze recent advances in lung MRI with a particular focus on lung cancer evaluation, clinical staging, and recurrence assessment evaluation.

Syphilis can cause a wide range of systemic manifestations, such as papular rash, malaise, weight loss, muscle aches, generalized lymphadenopathy, and meningitis. However, pulmonary involvement in patients with secondary syphilis is thought to be relatively rare. Moreover, bone involvement in patients with secondary syphilis is also considered rare, and only a few cases of involvement of lung and bone in such patients have been reported. In this paper, we report a case of secondary syphilis with pulmonary involvement in the form of multiple nodules with low attenuation areas, lymphadenopathy and multiple bone lesions detected on computed tomography and 18F fluorodeoxyglucose-positron emission tomography/computed tomography.

PURPOSE: To compare capabilities of compressed sensing (CS) with and without deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) with and without DLR for improving examination time and image quality of shoulder MRI for patients with various shoulder diseases. METHODS AND MATERIALS: Thirty consecutive patients with suspected shoulder diseases underwent MRI at a 3 T MR system using PI and CS. All MR data was reconstructed with and without DLR. For quantitative image quality evaluation, ROI measurements were used to determine signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). For qualitative image quality assessment, two radiologists evaluated overall image quality, artifacts and diagnostic confidence level using a 5-point scoring system, and consensus of the two readers determined each final value. Tukey's HSD test was used to compare examination times to establish the capability of the two techniques for reducing examination time. All indexes for all methods were then compared by means of Tukey's HSD test or Wilcoxon's signed rank test. RESULTS: CS with and without DLR showed significantly shorter examination times than PI with and without DLR (p < 0.05). SNR and CNR of CS or PI with DLR were significantly higher than of those without DLR (p < 0.05). Use of DLR significantly improved overall image quality and artifact incidence of CS and PI (p < 0.05). CONCLUSION: Examination time with CS is shorter than with PI without deterioration of image quality of shoulder MRI. Moreover, DLR is useful for both CS and PI for improvement of image quality on shoulder MRI.

PURPOSE: Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease onset to CT (i.e., time until CT) in this setting. MATERIALS AND METHODS: From March to May 2020, 32 COVID-19 patients underwent initial chest CT before enrollment were evaluated in this study. Eighteen patients were randomized to start favipiravir on day 1 (early treatment group), and 14 patients on day 6 of study participation (late treatment group). In this study, percentages of ground-glass opacity (GGO), reticulation, consolidation, emphysema, honeycomb, and nodular lesion volumes were calculated as quantitative indexes by means of the software, while CT-determined disease severity was also visually scored. Next, univariate and stepwise regression analyses were performed to determine relationships between quantitative indexes and time until CT. Moreover, patient outcomes determined as viral clearance in the first 6 days and duration of fever were compared for those who started therapy within 4, 5, or 6 days as time until CT and those who started later by means of the Kaplan-Meier method followed by Wilcoxon's signed-rank test. RESULTS: % GGO and % consolidation showed significant correlations with time until CT (p < 0.05), and stepwise regression analyses identified both indexes as significant descriptors for time until CT (p < 0.05). When divided all patients between time until CT of 4 days and that of more than 4 days, accuracy of the combined quantitative method (87.5%) was significantly higher than that of the CT disease severity score (62.5%, p = 0.008). CONCLUSION: ML-based CT texture analysis is equally or more useful for predicting time until CT for favipiravir treatment on COVID-19 patients than CT disease severity score.

OBJECTIVE: Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA. MATERIALS AND METHODS: Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey's HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness. RESULTS: For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05). CONCLUSION: Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT. KEY POINTS: • Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05). • Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05). • Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05).

BACKGROUND: Computed diffusion-weighted imaging (cDWI) is a mathematical computation technique that generates DWIs for any b-value by using actual DWI (aDWI) data with at least two different b-values and may improve differentiation of metastatic from nonmetastatic lymph nodes. PURPOSE: To determine the appropriate b-value for cDWI to achieve a better diagnostic capability for lymph node staging (N-staging) in non-small cell lung cancer (NSCLC) patients compared to aDWI, short inversion time (TI) inversion recovery (STIR) imaging, or positron emission tomography with 2-[fluorine-18] fluoro-2-deoxy-d-glucose combined with computed tomography (FDG-PET/CT). STUDY TYPE: Prospective. SUBJECTS: A total of 245 (127 males and 118 females; mean age 72 years) consecutive histopathologically confirmed NSCLC patients. FIELD STRENGTH/SEQUENCE: A 3 T, half-Fourier single-shot turbo spin-echo sequence, electrocardiogram (ECG)-triggered STIR fast advanced spin-echo (FASE) sequence with black blood and STIR acquisition and DWI obtained by FASE with b-values of 0 and 1000 sec/mm2 . ASSESSMENT: From aDWIs with b-values of 0 and 1000 (aDWI1000 ) sec/mm2 , cDWI using 400 (cDWI400 ), 600 (cDWI600 ), 800 (cDWI800 ), and 2000 (cDWI2000 ) sec/mm2 were generated. Then, 114 metastatic and 114 nonmetastatic nodes (mediastinal and hilar lymph nodes) were selected and evaluated with a contrast ratio (CR) for each cDWI and aDWI, apparent diffusion coefficient (ADC), lymph node-to-muscle ratio (LMR) on STIR, and maximum standard uptake value (SUVmax ). STATISTICAL TESTS: Receiver operating characteristic curve (ROC) analysis, Youden index, and McNemar's test. RESULTS: Area under the curve (AUC) of CR600 was significantly larger than the CR400 , CR800 , CR2000 , aCR1000 , and SUVmax . Comparison of N-staging accuracy showed that CR600 was significantly higher than CR400 , CR2000 , ADC, aCR1000 , and SUVmax , although there were no significant differences with CR800 (P = 0.99) and LMR (P = 0.99). DATA CONCLUSION: cDWI with b-value at 600 sec/mm2 may have potential to improve N-staging accuracy as compared with aDWI, STIR, and PET/CT. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

OBJECTIVE: To compare the utility of deep learning reconstruction (DLR) for improving acquisition time, image quality, and intraductal papillary mucinous neoplasm (IPMN) evaluation for 3D MRCP obtained with parallel imaging (PI), multiple k-space data acquisition for each repetition time (TR) technique (Fast 3D mode multiple: Fast 3Dm) and compressed sensing (CS) with PI. MATERIALS AND METHODS: A total of 32 IPMN patients who had undergone 3D MRCPs obtained with PI, Fast 3Dm, and CS with PI and reconstructed with and without DLR were retrospectively included in this study. Acquisition time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) obtained with all protocols were compared using Tukey's HSD test. Results of endoscopic ultrasound, ERCP, surgery, or pathological examination were determined as standard reference, and distribution classifications were compared among all 3D MRCP protocols by McNemar's test. RESULTS: Acquisition times of Fast 3Dm and CS with PI with and without DLR were significantly shorter than those of PI with and without DLR (p < 0.05). Each MRCP sequence with DLR showed significantly higher SNRs and CNRs than those without DLR (p < 0.05). IPMN distribution accuracy of PI with and without DLR and Fast 3Dm with DLR was significantly higher than that of Fast 3Dm without DLR and CS with PI without DLR (p < 0.05). CONCLUSION: DLR is useful for improving image quality and IPMN evaluation capability on 3D MRCP obtained with PI, Fast 3Dm, or CS with PI. Moreover, Fast 3Dm and CS with PI may play as substitution to PI for MRCP in patients with IPMN. KEY POINTS: • Mean examination times of multiple k-space data acquisitions for each TR and compressed sensing with parallel imaging were significantly shorter than that of parallel imaging (p < 0.0001). • When comparing image quality of 3D MRCPs with and without deep learning reconstruction, deep learning reconstruction significantly improved signal-to-noise ratio and contrast-to-noise ratio (p < 0.05). • IPMN distribution accuracies of parallel imaging with and without deep learning reconstruction (with vs. without: 88.0% vs. 88.0%) and multiple k-space data acquisitions for each TR with deep learning reconstruction (86.0%) were significantly higher than those of others (p < 0.05).