Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.

OBJECTIVE: To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients. MATERIALS AND METHODS: From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m2 were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups. RESULTS: The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001). CONCLUSION: DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients. CLINICAL RELEVANCE STATEMENT: Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality. KEY POINTS: Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.

Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm.

OBJECTIVES: To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm. MATERIALS AND METHODS: Oncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale. RESULTS: Fifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4-5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%. CONCLUSIONS: DLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm. CLINICAL RELEVANCE STATEMENT: Deep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm. KEY POINTS: • Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.

Iterative Reconstruction: State-of-the-Art and Future Perspectives.

ABSTRACT: Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.

Radiomics applications in cardiac imaging: a comprehensive review.

Radiomics is a new emerging field that includes extraction of metrics and quantification of so-called radiomic features from medical images. The growing importance of radiomics applied to oncology in improving diagnosis, cancer staging and grading, and improved personalized treatment, has been well established; yet, this new analysis technique has still few applications in cardiovascular imaging. Several studies have shown promising results describing how radiomics principles could improve the diagnostic accuracy of coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) in diagnosis, risk stratification, and follow-up of patients with coronary heart disease (CAD), ischemic heart disease (IHD), hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD), and many other cardiovascular diseases. Such quantitative approach could be useful to overcome the main limitations of CCTA and MRI in the evaluation of cardiovascular diseases, such as readers' subjectiveness and lack of repeatability. Moreover, this new discipline could potentially overcome some technical problems, namely the need of contrast administration or invasive examinations. Despite such advantages, radiomics is still not applied in clinical routine, due to lack of standardized parameters acquisition, inconsistent radiomic methods, lack of external validation, and different knowledge and experience among the readers. The purpose of this manuscript is to provide a recent update on the status of radiomics clinical applications in cardiovascular imaging.

Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.

PURPOSE: To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V). MATERIAL AND METHODS: Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient. RESULTS: DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001). CONCLUSION: DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.

Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation.

PURPOSE: To compare liver MRI with AIR Recon Deep Learning™(ARDL) algorithm applied and turned-off (NON-DL) with conventional high-resolution acquisition (NAÏVE) sequences, in terms of quantitative and qualitative image analysis and scanning time. MATERIAL AND METHODS: This prospective study included fifty consecutive volunteers (31 female, mean age 55.5 ± 20 years) from September to November 2021. 1.5 T MRI was performed and included three sets of images: axial single-shot fast spin-echo (SSFSE) T2 images, diffusion-weighted images(DWI) and apparent diffusion coefficient(ADC) maps acquired with both ARDL and NAÏVE protocol; the NON-DL images, were also assessed. Two radiologists in consensus drew fixed regions of interest in liver parenchyma to calculate signal-to-noise-ratio (SNR) and contrast to-noise-ratio (CNR). Subjective image quality was assessed by two other radiologists independently with a five-point Likert scale. Acquisition time was recorded. RESULTS: SSFSE T2 objective analysis showed higher SNR and CNR for ARDL vs NAÏVE, ARDL vs NON-DL(all P < 0.013). Regarding DWI, no differences were found for SNR with ARDL vs NAÏVE and, ARDL vs NON-DL (all P > 0.2517).CNR was higher for ARDL vs NON-DL(P = 0.0170), whereas no differences were found between ARDL and NAÏVE(P = 1). No differences were observed for all three comparisons, in terms of SNR and CNR, for ADC maps (all P > 0.32). Qualitative analysis for all sequences showed better overall image quality for ARDL with lower truncation artifacts, higher sharpness and contrast (all P < 0.0070) with excellent inter-rater agreement (k ≥ 0.8143). Acquisition time was lower in ARDL sequences compared to NAÏVE (SSFSE T2 = 19.08 ± 2.5 s vs. 24.1 ± 2 s and DWI = 207.3 ± 54 s vs. 513.6 ± 98.6 s, all P < 0.0001). CONCLUSION: ARDL applied on upper abdomen showed overall better image quality and reduced scanning time compared with NAÏVE protocol.

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia.

PURPOSE: To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. MATERIALS AND METHODS: One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann-Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. RESULTS: Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). CONCLUSIONS: Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.

Modified calcium subtraction in dual-energy CT angiography of the lower extremity runoff: impact on diagnostic accuracy for stenosis detection.

OBJECTIVES: To investigate the diagnostic accuracy of a modified three-material decomposition calcium subtraction (CS) algorithm for the detection of arterial stenosis in dual-energy CT angiography (DE-CTA) of the lower extremity runoff compared to standard image reconstruction, using digital subtraction angiography (DSA) as the reference standard. METHODS: Eighty-eight patients (53 males; mean age, 65.9 ± 11 years) with suspected peripheral arterial disease (PAD) who had undergone a DE-CTA examination of the lower extremity runoff between May 2014 and May 2015 were included in this IRB-approved, HIPAA-compliant retrospective study. Standard linearly blended and CS images were reconstructed and vascular contrast-to-noise ratios (CNR) were calculated. Two independent observers assessed subjective image quality using a 5-point Likert scale. Diagnostic accuracy for ≥ 50% stenosis detection was analyzed in a subgroup of 45 patients who had undergone additional DSA. Diagnostic accuracy parameters were estimated with a random-effects logistic regression analysis and compared using generalized estimating equations. RESULTS: CS datasets showed higher CNR (15.3 ± 7.3) compared to standard reconstructions (13.5 ± 6.5, p < 0.001). Both reconstructions showed comparable qualitative image quality scores (CS, 4.64; standard, 4.57; p = 0.220). Diagnostic accuracy (sensitivity, specificity, positive and negative predictive values) for CS reconstructions was 96.5% (97.5%, 95.6%, 90.9%, 98.1) and 93.1% (98.8%, 90.4%, 82.3%, 99.1%) for standard images. CONCLUSIONS: A modified three-material decomposition CS algorithm provides increased vascular CNR, equivalent qualitative image quality, and greater diagnostic accuracy for the detection of significant arterial stenosis of the lower extremity runoff on DE-CTA compared with standard image reconstruction. KEY POINTS: • Calcified plaques may lead to overestimation of stenosis severity and false positive results, requiring additional invasive digital subtraction angiography (DSA). • A modified three-material decomposition algorithm for calcium subtraction provides greater diagnostic accuracy for the detection of significant arterial stenosis of the lower extremity runoff compared with standard image reconstruction. • The application of this algorithm in patients with heavily calcified vessels may be helpful to potentially reduce inconclusive CT angiography examinations and the need for subsequent invasive DSA.

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.

OBJECTIVES: We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard. METHODS: Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard. RESULTS: One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p = 0.037), non-calcified plaque volume (OR 1.02, p = 0.007), napkin-ring sign (OR 5.97, p = 0.014), and CT-FFR (OR 0.81, p < 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93). CONCLUSION: Coronary CTA-derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power. KEY POINTS: • Coronary CT angiography (cCTA)-derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia. • A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.

Dynamic contrast-enhanced magnetic resonance imaging in locally advanced rectal cancer: role of perfusion parameters in the assessment of response to treatment.

PURPOSE: To correlate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters to tumor grading and to assess their reliability in predicting pathological complete response (pCR) before neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Forty patients (24 male; mean age, 67.3 ± 8.1 years) with histologically proven LARC who had undergone 3-Tesla DCE-MRI before (MRI_1) and after CRT (MRI_2) between August 2015 and February 2016 were included in this retrospective study. DCE-MRI parameters at MRI_1 and MRI_2 were extracted by two board certified radiologists in consensus reading with Olea Sphere 2.3 software using the extended Tofts model. Based on DCE-MRI results, patients were divided in complete responders (CR) and non-complete responders (nCR) and the perfusion parameters were correlated to tumor grading and pCR. RESULTS: Wash-out and Kep at MRI_1 showed significant correlation with LARC grading (P = 0.004 and 0.01, respectively). Ve showed a significant increase between MRI_1 (0.47 ± 0.27) and MRI_2 (0.63 ± 0.23; P = 0.007). Ktrans measured at MRI_1 was significantly higher in CR (0.66 ± 0.48) compared to nCR (0.53 ± 0.34, P = 0.02). CONCLUSION: Wash-out and Kep measured before CRT correlate with LARC grading. Ve changes during CRT, while Ktrans measured before CRT may predict the response to therapy. Therefore, DCE-MRI parameters can predict tumor aggressiveness and CRT efficacy, playing a role as imaging biomarkers in patients with LARC.

Bowel preparation in CT colonography: Is diet restriction necessary? A randomised trial (DIETSAN).

OBJECTIVES: To investigate whether diet restriction affects quality of colon cleansing and patient tolerance during reduced bowel preparation for CT colonography (CTC). METHODS: Asymptomatic and symptomatic patients were enrolled in this pragmatic, single-centre, randomised trial. All patients were randomly assigned (1:1 ratio, blocks of ten) to receive a reduced bowel preparation and faecal tagging with (Diet-Restriction-Group [DR]) or without (No-Diet-Restriction-Group [NDR]) dietary restriction. Five readers performed a blinded subjective image analysis, by means of 4-point Likert-scales from 0 (highest score) to 3 (worst score). Endpoints were the quality of large bowel cleansing and tolerance to the assigned bowel preparation regimen. The trial is registered at ClinicalTrial.gov (URomLSDBAL1). RESULTS: Ninety-five patients were randomly allocated to treatments (48 in NDR-group, 47 in DR-group). Both groups resulted in optimal colon cleansing. The mean residual stool (0.22, 95%CI 0.00-0.44) and fluid burden (0.39, 95%CI 0.25-0.53) scores for patients in DR-group were similar to those in patients in NDR-group (0.25, 95%CI 0.03-0.47 [p = 0.82] and 0.49, 95%CI 0.30-0.67 [p = 0.38], respectively). Tolerance was significantly better in NDR-group. CONCLUSION: A reduced bowel preparation in association with faecal tagging and without any dietary restriction demonstrated optimal colon cleansing effectiveness for CTC, providing better patient compliance compared with dietary restriction. KEY POINTS: • Dietary restriction in reduced bowel preparation regimen can be avoided. • The quality of colon cleansing is not affected by dietary restriction. • The quality of faecal tagging is not affected by dietary restriction. • Avoidance of dietary restriction improves patients' tolerance for CTC.

Contrast media injection protocol optimization for dual-energy coronary CT angiography: results from a circulation phantom.

OBJECTIVES: To investigate the minimum iodine delivery rate (IDR) required to achieve diagnostic coronary attenuation (300 HU) with dual-energy coronary CTA. METHODS: Acquisitions were performed on a circulation phantom with a third- generation dual-source CT scanner. Contrast media was injected for a fixed time whilst IDRs varied from 1.0 to 0.3 gI/s in 0.1-gI/s intervals. Noise-optimized virtual monoenergetic imaging (VMI+) reconstructions from 40 to 90 keV in 5 keV increments were generated. Contrast-to-noise ratio (CNR) and coronary HU were measured for each injection. RESULTS: VMI+ from 40-70 keV reached diagnostic attenuation with at least one IDR. The minimum IDR achieving a diagnostic attenuation ranged from 0.4 gI/s at 40 keV (312.8 HU) to 1.0 gI/s at 70 keV (334.1 HU). Attenuation values reached with IDR of 1.0 gI/s were significantly higher at each keV level (p<0.001). CNR showed a near perfect correlation with the IDR (ρ≥0.962; p<0.001), the IDR of 1.0 gI/s provided the highest CNR at each keV level, achieving the highest overall value at 40 keV (54.0±3.1). CONCLUSIONS: IDRs from 0.4-1.0 gI/s associated with VMI+ from 40-70 keV provide diagnostic coronary attenuation with dual-energy coronary CTA. KEY POINTS: • Iodine delivery rate (IDR) is a major determinant of contrast enhancement. • Low-keV noise-optimized monoenergetic images (VMI+) maximize iodine attenuation. • Low-keV VMI+ allows for lower IDRs while maintaining adequate coronary attenuation. • Lowest IDR to reach 300 HU was 0.4 gI/s, 40 keV VMI+.

A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions.

OBJECTIVES: To assess the image quality and diagnostic accuracy of a noise-optimized virtual monoenergetic imaging (VMI+) algorithm compared with standard virtual monoenergetic imaging (VMI) and linearly-blended (M_0.6) reconstructions for the detection of hypervascular liver lesions in dual-energy CT (DECT). METHODS: Thirty patients who underwent clinical liver MRI were prospectively enrolled. Within 60 days of MRI, arterial phase DECT images were acquired on a third-generation dual-source CT and reconstructed with M_0.6, VMI and VMI+ algorithms from 40 to 100 keV in 5-keV intervals. Liver parenchyma and lesion contrast-to-noise-ratios (CNR) were calculated. Two radiologists assessed image quality. Lesion sensitivity, specificity and area under the receiver operating characteristic curves (AUCs) were calculated for the three algorithms with MRI as the reference standard. RESULTS: VMI+ datasets from 40 to 60 keV provided the highest liver parenchyma and lesion CNR (p ≤0.021); 50 keV VMI+ provided the highest subjective image quality (4.40±0.54), significantly higher compared to VMI and M_0.6 (all p <0.001), and the best diagnostic accuracy in < 1-cm diameter lesions (AUC=0.833 vs. 0.777 and 0.749, respectively; p ≤0.003). CONCLUSIONS: 50-keV VMI+ provides superior image quality and diagnostic accuracy for the detection of hypervascular liver lesions with a diameter < 1cm compared to VMI or M_0.6 reconstructions. KEY POINTS: • Low-keV VMI+ are characterized by higher contrast resulting from maximum iodine attenuation. • VMI+ provides superior image quality compared with VMI or M_0.6. • 50-keV_VMI+ provides higher accuracy for the detection of hypervascular liver lesions < 1cm.

High-pitch low-voltage CT coronary artery calcium scoring with tin filtration: accuracy and radiation dose reduction.

OBJECTIVES: To investigate diagnostic accuracy and radiation dose of high-pitch CT coronary artery calcium scoring (CACS) with tin filtration (Sn100kVp) versus standard 120kVp high-pitch acquisition. METHODS: 78 patients (58% male, 61.5±9.1 years) were prospectively enrolled. Subjects underwent clinical 120kVp high-pitch CACS using third-generation dual-source CT followed by additional high-pitch Sn100kVp acquisition. Agatston scores, calcium volume scores, Agatston score categories, percentile-based risk categorization and radiation metrics were compared. RESULTS: 61/78 patients showed coronary calcifications. Median Agatston scores were 34.9 [0.7-197.1] and 41.7 [0.7-207.2] and calcium volume scores were 34.1 [0.7-218.0] for Sn100kVp and 35.7 [1.1-221.0] for 120kVp acquisitions, respectively (both p<0.0001). Bland-Altman analysis revealed underestimated Agatston scores and calcium volume scores with Sn100kVp versus 120kVp acquisitions (mean difference: 16.4 and 11.5). However, Agatston score categories and percentile-based risk categories showed excellent agreement (ĸ=0.98 and ĸ=0.99). Image noise was 25.8±4.4HU and 16.6±2.9HU in Sn100kVp and 120kVp scans, respectively (p<0.0001). Dose-length-product was 9.9±4.8mGy*cm and 40.9±14.4mGy*cm with Sn100kVp and 120kVp scans, respectively (p<0.0001). This resulted in significant effective radiation dose reduction (0.13±0.07mSv vs. 0.57±0.2mSv, p<0.0001) for Sn100kVp acquisitions. CONCLUSION: CACS using high-pitch low-voltage tin-filtered acquisitions demonstrates excellent agreement in Agatston score and percentile-based cardiac risk categorization with standard 120kVp high-pitch acquisitions. Furthermore, radiation dose was significantly reduced by 78% while maintaining accurate risk prediction. KEY POINTS: • Coronary artery calcium scoring with tin filtration reduces radiation dose by 78%. • There is excellent correlation between high-pitch Sn100kVp and standard 120kVp acquisitions. • Excellent agreement regarding Agatston score categories and percentile-based risk categorization was achieved. • No cardiac risk reclassifications were observed using Sn100kVp coronary artery calcium scoring.

Diagnostic accuracy of low and high tube voltage coronary CT angiography using an X-ray tube potential-tailored contrast medium injection protocol.

OBJECTIVES: To compare the diagnostic accuracy between low-kilovolt peak (kVp) (≤ 100) and high-kVp (> 100) third-generation dual-source coronary CT angiography (CCTA) using a kVp-tailored contrast media injection protocol. METHODS: One hundred twenty patients (mean age = 62.6 years, BMI = 29.0 kg/m2) who underwent catheter angiography and CCTA with automated kVp selection were separated into two cohorts (each n = 60, mean kVp = 84 and 117). Contrast media dose was tailored to the kVp level: 70 = 40 ml, 80 = 50 ml, 90 = 60 ml, 100 = 70 ml, 110 = 80 ml, and 120 = 90 ml. Contrast-to-noise ratio (CNR) was measured. Two observers evaluated image quality and the presence of significant coronary stenosis (> 50% luminal narrowing). RESULTS: Diagnostic accuracy (sensitivity/specificity) with ≤ 100 vs. > 100 kVp CCTA was comparable: per patient = 93.9/92.6% vs. 90.9/92.6%, per vessel = 91.5/97.8% vs. 94.0/96.8%, and per segment = 90.0/96.7% vs. 90.7/95.2% (all P > 0.64). CNR was similar (P > 0.18) in the low-kVp vs. high-kVp group (12.0 vs. 11.1), as ws subjective image quality (P = 0.38). Contrast media requirements were reduced by 38.1% in the low- vs. high-kVp cohort (53.6 vs. 86.6 ml, P < 0.001) and radiation dose by 59.6% (4.3 vs. 10.6 mSv, P < 0.001). CONCLUSIONS: Automated tube voltage selection with a tailored contrast media injection protocol allows CCTA to be performed at ≤ 100 kVp with substantial dose reductions and equivalent diagnostic accuracy for coronary stenosis detection compared to acquisitions at > 100 kVp. KEY POINTS: • Low-kVp coronary CT angiography (CCTA) enables reduced contrast and radiation dose. • Diagnostic accuracy is comparable between ≤ 100 and > 100 kVp CCTA. • Image quality is similar for low- and high-kVp CCTA. • Low-kVp image acquisition is facilitated by automated tube voltage selection. • Tailoring contrast injection protocols to the automatically selected kVp-level is feasible.

Haralick's texture features for the prediction of response to therapy in colorectal cancer: a preliminary study.

PURPOSE: Haralick features Texture analysis is a recent oncologic imaging biomarker used to assess quantitatively the heterogeneity within a tumor. The aim of this study is to evaluate which Haralick's features are the most feasible in predicting tumor response to neoadjuvant chemoradiotherapy (CRT) in colorectal cancer. MATERIALS AND METHODS: After MRI and histological assessment, eight patients were enrolled and divided into two groups based on response to neoadjuvant CRT in complete responders (CR) and non-responders (NR). Oblique Axial T2-weighted MRI sequences before CRT were analyzed by two radiologists in consensus drawing a ROI around the tumor. 14 over 192 Haralick's features were extrapolated from normalized gray-level co-occurrence matrix in four different directions. A dedicated statistical analysis was performed to evaluate distribution of the extracted Haralick's features computing mean and standard deviation. RESULTS: Pretreatment MRI examination showed significant value (p < 0.05) of 5 over 14 computed Haralick texture. In particular, the significant features are the following: concerning energy, contrast, correlation, entropy and inverse difference moment. CONCLUSIONS: Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectal patients.

Cinematic Rendering in CT: A Novel, Lifelike 3D Visualization Technique.

OBJECTIVE: The purpose of this article is to present an overview of cinematic rendering, illustrating its potential advantages and applications. CONCLUSION: Volume-rendered reconstruction, obtaining 3D visualization from original CT datasets, is increasingly used by physicians and medical educators in various clinical and educational scenarios. Cinematic rendering is a novel 3D rendering algorithm that simulates the propagation and interaction of light rays as they pass through the volumetric data, showing a more photorealistic representation of 3D images than achieved with standard volume rendering.

Optimizing Contrast Media Injection Protocols in Computed Tomography Angiography at Different Tube Voltages: Evaluation in a Circulation Phantom.

OBJECTIVES: The aim of this study was to investigate the minimum iodine delivery rate (IDR) and contrast media (CM) volume required for diagnostic contrast enhancement of 350 HU (Hounsfield units) in the ascending aorta at different kV settings. METHODS: Dynamic computed tomography acquisitions from 70 to 150 kV were performed in a circulation phantom. First, injections with IDR ranging from 0.1 to 2.0 g I/s were tested for each kV. In the second part, the IDR was held constant, whereas the CM volume was reduced from 50 to 10 mL. Diagnostic aortic peak enhancement for each kV was compared using the Kruskal-Wallis test. P < 0.05 was considered statistically significant. RESULTS: The mean aortic peak enhancement for all diagnostic IDRs was 368.7 ± 11.1 HU. Diagnostic IDRs returned similar aortic peak enhancement values for all protocols (all P ≥ 0.18). For the second part of the study, a diagnostic enhancement was yielded by using a minimum of 30 mL of CM for 110 kV, 25 mL for 100 and 90 kV, and 15 mL for 80 and 70 kV. CONCLUSION: Our study suggests that a differentiated approach reducing the CM volume for tube voltages of less than 120 kV and increasing the IDR for higher kV settings seems to be the most effective approach.

Liver Magnetic Resonance Elastography: Focus on Methodology, Technique, and Feasibility.

Magnetic resonance elastography (MRE) is an imaging technique that combines low-frequency mechanical vibrations with magnetic resonance imaging to create visual maps and quantify liver parenchyma stiffness. As in recent years, diffuse liver diseases have become highly prevalent worldwide and could lead to a chronic condition with different stages of fibrosis. There is a strong necessity for a non-invasive, highly accurate, and standardised quantitative assessment to evaluate and manage patients with different stages of fibrosis from diagnosis to follow-up, as the actual reference standard for the diagnosis and staging of liver fibrosis is biopsy, an invasive method with possible peri-procedural complications and sampling errors. MRE could quantitatively evaluate liver stiffness, as it is a rapid and repeatable method with high specificity and sensitivity. MRE is based on the propagation of mechanical shear waves through the liver tissue that are directly proportional to the organ's stiffness, expressed in kilopascals (kPa). To obtain a valid assessment of the real hepatic stiffness values, it is mandatory to obtain a high-quality examination. To understand the pearls and pitfalls of MRE, in this review, we describe our experience after one year of performing MRE from indications and patient preparation to acquisition, quality control, and image analysis.

Is CT Radiomics Superior to Morphological Evaluation for pN0 Characterization? A Pilot Study in Colon Cancer.

The aim of this study was to compare CT radiomics and morphological features when assessing benign lymph nodes (LNs) in colon cancer (CC). This retrospective study included 100 CC patients (test cohort) who underwent a preoperative CT examination and were diagnosed as pN0 after surgery. Regional LNs were scored with a morphological Likert scale (NODE-SCORE) and divided into two groups: low likelihood (LLM: 0-2 points) and high likelihood (HLM: 3-7 points) of malignancy. The T-test and the Mann-Whitney test were used to compare 107 radiomic features extracted from the two groups. Radiomic features were also extracted from primary lesions (PLs), and the receiver operating characteristic (ROC) was used to test a LN/PL ratio when assessing the LN's status identified with radiomics and with the NODE-SCORE. An amount of 337 LNs were divided into 167 with LLM and 170 with HLM. Radiomics showed 15/107 features, with a significant difference (p < 0.02) between the two groups. The comparison of selected features between 81 PLs and the corresponding LNs showed all significant differences (p < 0.0001). According to the LN/PL ratio, the selected features recognized a higher number of LNs than the NODE-SCORE (p < 0.001). On validation of the cohort of 20 patients (10 pN0, 10 pN2), significant ROC curves were obtained for LN/PL busyness (AUC = 0.91; 0.69-0.99; 95% C.I.; and p < 0.001) and for LN/PL dependence entropy (AUC = 0.76; 0.52-0.92; 95% C.I.; and p = 0.03). The radiomics ratio between CC and LNs is more accurate for noninvasively discriminating benign LNs compared to CT morphological features.