Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.

Optimisation of virtual monoenergetic reconstructions for the diagnosis of pulmonary embolism using photon-counting detector computed tomography angiography

Purpose

Computed tomography (CT) pulmonary angiography is considered the gold standard for pulmonary embolism (PE) diagnosis, relying on the discrimination between contrast and embolus. Photon-counting detector CT (PCD-CT) generates monoenergetic reconstructions through energy-resolved detection. Virtual monoenergetic images (VMI) at low keV can be used to improve pulmonary artery opacification. While studies have assessed VMI for PE diagnosis on dual-energy CT (DECT), there is a lack of literature on optimal settings for PCD-CT-PE reconstructions, warranting further investigation.

Material and methods

Twenty-five sequential patients who underwent PCD-CT pulmonary angiography for suspicion of acute PE were retrospectively included in this study. Quantitative metrics including signal-to-noise ratio (SNR) and contrast-to-noise (CNR) ratio were calculated for 4 VMI values (40, 60, 80, and 100 keV). Qualitative measures of diagnostic quality were obtained for proximal to distal pulmonary artery branches by 2 cardiothoracic radiologists using a 5-point modified Likert scale.

Results

SNR and CNR were highest for the 40 keV VMI (49.3 ± 22.2 and 48.2 ± 22.1, respectively) and were inversely related to monoenergetic keV. Qualitatively, 40 and 60 keV both exhibited excellent diagnostic quality (mean main pulmonary artery: 5.0 ± 0 and 5.0 ± 0; subsegmental pulmonary arteries 4.9 ± 0.1 and 4.9 ± 0.1, respectively) while distal segments at high (80-100) keVs had worse quality.

Conclusions

40 keV was the best individual VMI for the detection of pulmonary embolism by quantitative metrics. Qualitatively, 40-60 keV reconstructions may be used without a significant decrease in subjective quality. VMIs at higher keV lead to reduced opacification of the distal pulmonary arteries, resulting in decreased image quality.

A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation

Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on "the role of the MPEs in clinical trials"-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.

Intra-individual comparison of coronary calcium scoring between photon counting detector- and energy integrating detector-CT: Effects on risk reclassification

Purpose

To compare coronary artery calcium volume and score (CACS) between photon-counting detector (PCD) and conventional energy integrating detector (EID) computed tomography (CT) in a phantom and prospective patient study.

Methods

A commercially available CACS phantom was scanned with a standard CACS protocol (120 kVp, slice thickness/increment 3/1.5 mm, and a quantitative Qr36 kernel), with filtered back projection on the EID-CT, and with monoenergetic reconstruction at 70 keV and quantum iterative reconstruction off on the PCD-CT. The same settings were used to prospectively acquire data in patients (n = 23, 65 ± 12.1 years), who underwent PCD- and EID-CT scans with a median of 5.5 (3.0-12.5) days between the two scans in the period from August 2021 to March 2022. CACS was quantified using a commercially available software solution. A regression formula was obtained from the aforementioned comparison and applied to simulate risk reclassification in a pre-existing cohort of 514 patients who underwent a cardiac EID-CT between January and December 2021.

Results

Based on the phantom experiment, CACS _PCD-CT showed a more accurate measurement of the reference CAC volumes (overestimation of physical volumes: PCD-CT 66.1 ± 1.6% vs. EID-CT: 77.2 ± 0.5%). CACS _EID-CT and CACS _PCD-CT were strongly correlated, however, the latter measured significantly lower values in the phantom (CACS _PCD-CT : 60.5 (30.2-170.3) vs CACS _EID-CT 74.7 (34.6-180.8), p = 0.0015, r = 0.99, mean bias -9.7, Limits of Agreement (LoA) -36.6/17.3) and in patients (non-significant) (CACS _PCD-CT : 174.3 (11.1-872.7) vs CACS _EID-CT 218.2 (18.5-876.4), p = 0.10, r = 0.94, mean bias -41.1, LoA -315.3/232.5). The systematic lower measurements of Agatston score on PCD-CT system led to reclassification of 5.25% of our simulated patient cohort to a lower classification class.

Conclusion

CACS _PCD-CT is feasible and correlates strongly with CACS _EID-CT , however, leads to lower CACS values. PCD-CT may provide results that are more accurate for CACS than EID-CT.

Simultaneous 13N-Ammonia and gadolinium first-pass myocardial perfusion with quantitative hybrid PET-MR imaging: a phantom and clinical feasibility study

BACKGROUND

Positron emission tomography (PET) is the non-invasive reference standard for myocardial blood flow (MBF) quantification. Hybrid PET-MR allows simultaneous PET and cardiac magnetic resonance (CMR) acquisition under identical experimental and physiological conditions. This study aimed to determine feasibility of simultaneous 13N-Ammonia PET and dynamic contrast-enhanced CMR MBF quantification in phantoms and healthy volunteers.

METHODS

Images were acquired using a 3T hybrid PET-MR scanner. Phantom study: MBF was simulated at different physiological perfusion rates and a protocol for simultaneous PET-MR perfusion imaging was developed. Volunteer study: five healthy volunteers underwent adenosine stress. 13N-Ammonia and gadolinium were administered simultaneously. PET list mode data was reconstructed using ordered subset expectation maximisation. CMR MBF was quantified using Fermi function-constrained deconvolution of arterial input function and myocardial signal. PET MBF was obtained using a one-tissue compartment model and image-derived input function.

RESULTS

Phantom study: PET and CMR MBF measurements demonstrated high repeatability with intraclass coefficients 0.98 and 0.99, respectively. There was high correlation between PET and CMR MBF (r = 0.98, p < 0.001) and good agreement (bias - 0.85 mL/g/min; 95% limits of agreement 0.29 to - 1.98). Volunteer study: Mean global stress MBF for CMR and PET were 2.58 ± 0.11 and 2.60 ± 0.47 mL/g/min respectively. On a per territory basis, there was moderate correlation (r = 0.63, p = 0.03) and agreement (bias - 0.34 mL/g/min; 95% limits of agreement 0.49 to - 1.18).

CONCLUSION

Simultaneous MBF quantification using hybrid PET-MR imaging is feasible with high test repeatability and good to moderate agreement between PET and CMR. Future studies in coronary artery disease patients may allow cross-validation of techniques.