Does PET-CT Have a Role in the Evaluation of Tuberculosis Treatment in Phase 2 Clinical Trials?

Positron emission tomography-computed tomography (PET-CT) has the potential to revolutionize research in infectious diseases, as it has done with cancer. There is growing interest in it as a biomarker in the setting of early-phase tuberculosis clinical trials, particularly given the limitations of current biomarkers as adequate predictors of sterilizing cure for tuberculosis. PET-CT is a real-time tool that provides a 3-dimensional view of the spatial distribution of tuberculosis within the lung parenchyma and the nature of lesions with uptake (ie, whether nodular, consolidative, or cavitary). Its ability to provide functional data on changes in metabolism, drug penetration, and immune control of tuberculous lesions has the potential to facilitate drug development and regimen selection for advancement to phase 3 trials in tuberculosis. In this narrative review, we discuss the role that PET-CT may have in evaluating responses to drug therapy in active tuberculosis treatment and the challenges in taking PET-CT forward as predictive biomarker of relapse-free cure in the setting of phase 2 clinical trials.

Enhancing diagnostic precision for acute chest syndrome in sickle cell disease: insights from dual-energy CT lung perfusion mapping.

PURPOSE: Acute chest syndrome (ACS) is secondary to occlusion of the pulmonary vasculature and a potentially life-threatening complication of sickle cell disease (SCD). Dual-energy CT (DECT) iodine perfusion map reconstructions can provide a method to visualize and quantify the extent of pulmonary microthrombi. METHODS: A total of 102 patients with sickle cell disease who underwent DECT CTPA with perfusion were retrospectively identified. The presence or absence of airspace opacities, segmental perfusion defects, and acute or chronic pulmonary emboli was noted. The number of segmental perfusion defects between patients with and without acute chest syndrome was compared. Sub-analyses were performed to investigate robustness. RESULTS: Of the 102 patients, 68 were clinically determined to not have ACS and 34 were determined to have ACS by clinical criteria. Of the patients with ACS, 82.4% were found to have perfusion defects with a median of 2 perfusion defects per patient. The presence of any or new perfusion defects was significantly associated with the diagnosis of ACS (P = 0.005 and < 0.001, respectively). Excluding patients with pulmonary embolism, 79% of patients with ACS had old or new perfusion defects, and the specificity for new perfusion defects was 87%, higher than consolidation/ground glass opacities (80%). CONCLUSION: DECT iodine map has the capability to depict microthrombi as perfusion defects. The presence of segmental perfusion defects on dual-energy CT maps was found to be associated with ACS with potential for improved specificity and reclassification.

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.

Myocardial Characterization with Extracellular Volume Mapping with a First-Generation Photon-counting Detector CT with MRI Reference.

Background Photon-counting detector (PCD) CT provides comprehensive spectral data with every acquisition, but studies evaluating myocardial extracellular volume (ECV) quantification with use of PCD CT compared with an MRI reference remain lacking. Purpose To compare ECV quantification for myocardial tissue characterization between a first-generation PCD CT system and cardiac MRI. Materials and Methods In this single-center prospective study, adults without contraindication to iodine-based contrast media underwent same-day cardiac PCD CT and MRI with native and postcontrast T1 mapping and late gadolinium enhancement for various clinical indications for cardiac MRI (the reference standard) between July 2021 and January 2022. Global and midventricular ECV were assessed with use of three methods: single-energy PCD CT, dual-energy PCD CT, and MRI T1 mapping. Quantitative comparisons among all techniques were performed. Correlation and reliability between different methods of ECV quantification were assessed with use of the Pearson correlation coefficient (r) and the intraclass correlation coefficient. Results The final sample included 29 study participants (mean age ± SD, 54 years ± 17; 15 men). There was a strong correlation of ECV between dual- and single-energy PCD CT (r = 0.91, P < .001). Radiation dose was 40% lower with dual-energy versus single-energy PCD CT (volume CT dose index, 10.1 mGy vs 16.8 mGy, respectively; P < .001). In comparison with MRI, dual-energy PCD CT showed strong correlation (r = 0.82 and 0.91, both P < .001) and good to excellent reliability (intraclass correlation coefficients, 0.81 and 0.90) for midventricular and global ECV quantification, but it overestimated ECV by approximately 2%. Single-energy PCD CT showed similar relationship with MRI but underestimated ECV by 3%. Conclusion Myocardial tissue characterization with photon-counting detector CT-based quantitative extracellular volume analysis showed a strong correlation to MRI. © RSNA, 2023 Supplemental material is available for this article.

Stent imaging on a clinical dual-source photon-counting detector CT system-impact of luminal attenuation and sharp kernels on lumen visibility.

OBJECTIVES: To assess the impact of scan modes and reconstruction kernels using a novel dual-source photon-counting detector CT (PCD-CT) on lumen visibility and sharpness of different stent sizes. METHODS: A phantom containing six balloon-expandable stents (2.5 to 9 mm diameter) in silicone tubing was scanned on a PCD-CT with standard (0.6 mm and 0.4 mm thicknesses) and ultra-high-resolution (0.2 mm thickness) modes. With the use of increasing contrast medium concentrations, densities of 0, 200, 400, and 600 HU were achieved. Standard-resolution scans were reconstructed using increasing sharpness kernels, using both polyenergetic quantitative soft tissue "conventional" ((Qr40c(0.6 mm), Qr40c(0.4 mm), Qr72c(0.2 mm)) and vascular (Bv) virtual monoenergetic reconstructions (Bv44m(0.4 mm), Bv60m(0.4 mm)) at 70 keV. In-stent lumen visibility, sharpness (max. ΔHU of the stent measured in profile plots), and in-stent noise (standard deviation of HU) were measured. RESULTS: In-stent lumen visibility was highest for Qr72c(0.2 mm) (86.5 ± 2.8% to 88.3 ± 2.6%) and in Bv60m(0.4 mm) reconstructions (77.3 ± 2.9 to 82.7 ± 2.5%). Lumen visibility was lowest in the smallest stent (2.5 mm) ranging from 54.1% in Qr40c(0.6 mm) to 74.1% in Qr72c(0.2 mm) and highest in the largest stent (9 mm) ranging from 93.8% in Qr40c(0.6 mm) to 99.1% in the Qr72c(0.2 mm) series. Lumen visibility decreased by 2.1% for every 200-HU increase in lumen attenuation. Max. ΔHU between stents and stent lumen was highest in Qr72c(0.2 mm) (ΔHU 892 ± 504 to 1526 ± 517) and Bv60m(0.4 mm) series (ΔHU 480 ± 357 to 1030 ± 344). Improvement of lumen visibility and sharpness in UHR and Bv60m(0.4 mm) series was strongest in smaller stent sizes. CONCLUSION: UHR acquisition mode and sharp reconstruction kernels on a novel PCD-CT system significantly improve in-stent lumen visibility and sharpness-especially for smaller stent sizes. KEY POINTS: • In-stent lumen visibility and sharpness of stents significantly improve using sharp reconstruction kernels (Bv60) and ultra-high-resolution mode in photon-counting detector computed tomography. • The observed improvement of stent-lumen visibility was highest in smaller stent sizes.

Improved amyloid burden quantification with nonspecific estimates using deep learning.

PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-ß burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we propose a novel amyloid-PET quantification approach that harnesses the intermodal image translation capability of convolutional networks to remove this undesirable source of variability. METHODS: Paired MR and PET images exhibiting very low specific uptake were selected from a Singaporean amyloid-PET study involving 172 participants with different severities of CeVD. Two convolutional neural networks (CNN), ScaleNet and HighRes3DNet, and one conditional generative adversarial network (cGAN) were trained to map structural MR to NS PET images. NS estimates generated for all subjects using the most promising network were then subtracted from SUVr images to determine specific amyloid load only (SAßL). Associations of SAßL with various cognitive and functional test scores were then computed and compared to results using conventional SUVr. RESULTS: Multimodal ScaleNet outperformed other networks in predicting the NS content in cortical gray matter with a mean relative error below 2%. Compared to SUVr, SAßL showed increased association with cognitive and functional test scores by up to 67%. CONCLUSION: Removing the undesirable NS uptake from the amyloid load measurement is possible using deep learning and substantially improves its accuracy. This novel analysis approach opens a new window of opportunity for improved data modeling in Alzheimer's disease and for other neurodegenerative diseases that utilize PET imaging.

Development of a 99mTc-Labeled Single-Domain Antibody for SPECT/CT Assessment of HER2 Expression in Breast Cancer.

Accurate determination of human epidermal growth factor receptor 2 (HER2) expression is essential for HER2-targeted therapy in patients with cancer. HER2 expression in a complex environment, such as in a heterogeneous tumor, makes the precise assessment of the HER2 status difficult using current methods. In this study, we developed a novel 99mTc-labeled anti-HER2 single-domain antibody (99mTc-NM-02) as a molecular imaging tracer for the noninvasive detection of HER2 expression and investigated its safety, radiation dosimetry, biodistribution, and tumor-targeting potential in 10 patients with breast cancer. Our data showed that no drug-related adverse reactions occurred. The tracer mainly accumulated in the kidneys and liver with mild uptake in the spleen, intestines, and thyroid; however, only background tracer levels were observed in other organs where primary tumors and metastases typically occurred. The mean effective dose was 6.56 × 10-3 mSv/MBq, and tracer uptake was visually observed in the primary tumors and metastases. A maximal standard uptake value of 1.5 was determined as a reasonable cutoff for identifying HER2 positivity using SPECT/CT imaging. Our 99mTc-NM-02 tracer is safe for use in breast cancer imaging, with reasonable radiation doses, favorable biodistribution, and imaging characteristics. 99mTc-NM-02 SPECT imaging may be an accurate and noninvasive method to detect the HER2 status in patients with breast cancer.

Effect of Bayesian-penalized likelihood reconstruction on [13N]-NH3 rest perfusion quantification.

ABSTACT: OBJECTIVES: Myocardial blood flow (MBF) imaging is used in patients with suspected cardiac sarcoidosis, and also in stress/rest studies. The accuracy of MBF is dependent on imaging parameters such as new reconstruction methodologies. In this work, we aim to assess the impact of a novel PET reconstruction algorithm (Bayesian-penalized likelihood-BPL) on the values determined from the calculation of [13N]-NH3 MBF values. METHODS: Data from 21 patients undergoing rest MBF evaluation [13N]-NH3 as part of sarcoidosis imaging were retrospectively analyzed. Each scan was reconstructed with a range of BPL coefficients (1-500), and standard clinical FBP and OSEM reconstructions. MBF values were calculated via an automated software routine for all datasets. RESULTS: Reconstruction of [13N]-NH3 dynamic data using the BPL, OSEM, or FBP reconstruction showed no quantitative differences for the calculation of territorial or global MBF (P = .97). Image noise was lower using OSEM or BPL reconstructions than FBP and noise from BPL reached levels seen in OSEM images between B = 300 and B = 400. Intrasubject differences between all reconstructions over all patients in respect of all cardiac territories showed a maximum coefficient of variation of 9.74%. CONCLUSION: Quantitation of MBF via kinetic modeling of cardiac rest MBF by [13N]-NH3 is minimally affected by the use of a BPL reconstruction technique, with BPL images presenting with less noise.

Assessment of an intermediate reprojection technique transitioning from planar to SPECT radionuclide ventriculography.

BACKGROUND: The technique of SPECT-RNV (radionuclide ventriculography) offers a greater amount of clinically usable data than its planar counterpart (P-RNV). In transitioning from planar to SPECT-only acquisition methodologies, reprojection of the SPECT data can provide a planar dataset which can be used as an interim technique. The aim of this study was to test if reprojected planar images could be used as a surrogate for true planar images in SPECT-only setting. METHODS: We performed SPECT-RNV and P-RNV on 47 patients on traditional sodium iodide (NaI) cameras, determining left ventricular ejection fractions (LVEF) for planar (EFP) and SPECT (EFS) techniques. We reprojected the SPECT-RNV data along the best septal separation angle determined from planar scanning. This creates a further planar dataset denoted 'reprojected P-RNV' (rP-RNV) giving a reprojected ejection fraction (EFR) which can be used as a validation variable in transitioning to SPECT-only acquisition. RESULTS: Performing t tests showed no statistical difference between EFP and EFR (P > .017) but bias was observed in EFS results compared to EFP and EFS compared to EFR results. An unblinded, comparison of parametric data between the three datasets for a subset of ten patients showed good clinical concordance. False negative and false positive rates were low for rP-RNV compared to P-RNV. CONCLUSIONS: The reprojected planar LVEF correlates well to P-RNV EF values. The rP-RNV dataset can aid clinicians in transitioning from planar RNV to SPECT-only acquisition.

Technical note: Can photon-counting CT improve PET/CT's quantitative accuracy?

BACKGROUND: Linear attenuation coefficients (LACs) in positron emission tomography combined with computed tomography (PET/CT) are derived from CT scans that utilize energy-integrating detectors (EID-CT). These LACs are inaccurate when iodine contrast has been injected. Photon counting detector CT (PCD-CT) may be able to improve the accuracy. PURPOSE: To investigate whether PCD-CT can improve PET/CT quantitative accuracy. METHODS: Two experiments were performed: one with CT only and one that combined PET and CT. The first experiment used an electron density phantom whose inserts were imaged with EID-CT and PCD-CT. The inserts simulated normal human tissues, including bone and iodinated blood. In the case of PCD-CT, virtual-monoenergetic images at 190 keV were created. LACs were derived in each case and compared against known values. For inserts with iodine, more accurate LACs were expected with PCD-CT. The second experiment involved a custom PET phantom with various materials simulating human tissues (blood, iodinated blood, and bone) and 18F radioactivity. Data were first acquired with an EID-CT-based PET/CT system and then separately in a PCD-CT system without PET. PET images were reconstructed using LAC from EID-CT and PCD-CT. PET image values were compared against known activity values using recovery coefficients (RC). RESULTS: In the first experiment, LAC based on EID-CT were in error by as much as 18%, whereas the corresponding PCD-CT based measurements were within 3%. In the second experiment, minimum, maximum, and mean RC were (96.1%, 115.4%, and 103.8%) for the EID-CT method, and (95.8%, 105.5%, and 101.0%) for the PCD-CT method. The consistency of PET images in body and head orientations was improved. CONCLUSIONS: PCD-CT can acquire the information needed for accurate LAC for PET reconstruction in a single spiral acquisition.

Image quality of photon-counting detector CT virtual monoenergetic and polyenergetic reconstructions for head and neck CT angiography.

We compared image quality of head and neck CT angiography (CTA) obtained with a photon-counting detector CT (PCD-CT), including virtual monoenergetic images and polyenergetic reconstructions, and conventional energy-integrating detectors CT (EID-CT) in three patients. PCD-CT monoenergetic reconstructions at 70 keV and lower provided excellent image quality, with improved signal-to-noise and contrast-to-noise compared to EID-CT and PCD-CT polyenergetic reconstructions. PCD-CT may enable radiation dose and iodinated contrast dose reduction for cerebrovascular imaging.

Time-Activity data fitting in molecular Radiotherapy: Methodology and pitfalls.

Absorbed radiation doses are essential in assessing the effects, e.g. safety and efficacy, of radiopharmaceutical therapy (RPT). Patient-specific absorbed dose calculations in the target or the organ at risk require multiple inputs. These include the number of disintegrations in the organ, i.e. the time-integrated activities (TIAs) of the organs, as well as other parameters describing the process of radiation energy deposition in the target tissue (i.e. mean energy per disintegration, radiation dose constants, etc). TIAs are then estimated by incorporating the area under the radiopharmaceutical's time-activity curve (TAC), which can be obtained by quantitative measurements of the biokinetics in the patient (typically based on imaging data such as planar scintigraphy, SPECT/CT, PET/CT, or blood and urine samples). The process of TAC determination/calculation for RPT generally depends on the user, e.g., the chosen number and schedule of measured time points, the selection of the fit function, the error model for the data and the fit algorithm. These decisions can strongly affect the final TIA values and thus the accuracy of calculated absorbed doses. Despite the high clinical importance of the TIA values, there is currently no consensus on processing time-activity data or even a clear understanding of the influence of uncertainties and variations in personalised RPT dosimetry related to user-dependent TAC calculation. As a first step towards minimising site-dependent variability in RPT dosimetry, this work provides an overview of quality assurance and uncertainty management considerations of the TIA estimation.

Neurovascular Imaging with Ultra-High-Resolution Photon-Counting CT: Preliminary Findings on Image-Quality Evaluation.

BACKGROUND AND PURPOSE: The first-generation photon-counting detector CT was recently introduced into clinical practice and represents a promising innovation in high-resolution CT imaging. The purpose of this study was to assess the image quality of ultra-high-resolution photon-counting detector CT compared with energy-integrating detector CT and to explore different reconstruction kernel sharpness levels for the evaluation of intracranial aneurysms. MATERIALS AND METHODS: Ten patients with intracranial saccular aneurysms who had previously undergone conventional energy-integrating detector CT were prospectively enrolled. CT angiograms were acquired on a clinical dual-source photon-counting detector CT in ultra-high-resolution mode and reconstructed with 4 vascular kernels (Bv36, Bv40, Bv44, Bv48). Quantitative and qualitative image-quality parameters of the intracranial arteries were evaluated. For the quantitative analysis (image noise, SNR, contrast-to-noise ratio), ROIs were manually placed at standard anatomic intracranial and extracranial locations by 1 author. In addition, vessel border sharpness was evaluated quantitatively. For the qualitative analysis, 3 blinded neuroradiologists rated photon-counting detector CT and energy-integrating detector CT image quality for the evaluation of the intracranial vessels (ie, the aneurysms and 9 standard vascular branching locations) on a 5-point Likert-type scale. Additionally, readers independently selected their preferred kernel among the 4 kernels evaluated on photon-counting detector CT. RESULTS: In terms of quantitative image quality, Bv48, the sharpest kernel, yielded increased image noise and decreased SNR and contrast-to-noise ratio parameters compared with Bv36, the smoothest kernel. Compared with energy-integrating detector CT, the Bv48 kernel offered better quantitative image quality for the evaluation of small intracranial vessels (P < .001). Image-quality ratings of the Bv48 were superior to those of the energy-integrating detector CT and not significantly different from ratings of the B44 reconstruction kernel. When comparing side by side all 4 photon-counting detector reconstruction kernels, readers selected the B48 kernel as the best to visualize the aneurysms in 80% of cases. CONCLUSIONS: Ultra-high-resolution photon-counting detector CT provides improved image quality for neurovascular imaging. Although the less sharp kernels provided superior SNR and contrast-to-noise ratio, the sharpest kernels delivered the best subjective image quality on photon-counting detector CT for the evaluation of intracranial aneurysms.

Polyenergetic reconstruction mitigates streak artifacts by dual source imaging in chest photon counting detector computed tomography.

OBJECTIVE: This study aims to assess the efficacy of polyenergetic reconstruction methods in reducing streak artifacts caused by dual source imaging in Photon Counting Detector Computed Tomography (PCD-CT) imaging, thereby improving image quality and diagnostic accuracy. METHODS: A retrospective cohort study was conducted, involving 50 patients who underwent chest Computed Tomography Angiography with PCD-CT, focusing on those with streak artifacts. Quantitative and qualitative analyses were performed on images reconstructed using monoenergetic and polyenergetic techniques. Quantitative evaluations measured the attenuation of tracheal air density in regions affected by streak artifacts, while qualitative assessments employed a modified Likert scale to rate image quality. Statistical analyses included Wilcoxon's signed-rank tests and Spearman's correlation, alongside assessments of inter-rater reliability. RESULTS: There was significantly lower attenuation of tracheal air density on the polyenergetic reconstructions (Median - 1010 ± 62 HU vs -930 ± 110 HU; P < 0.001), and significantly decreased variation on the polyenergetic reconstructions (Median 65.2 ± 79.5 HU vs 38.8 ± 33.9 HU; P < 0.001). The median modified-Likert scale were significantly better for the polyenergetic reconstructions (median modified-Likert 4 ± 0.5 vs 2.5 ± 1; P < 0.001). The inter-rater agreement was substantial and not significantly different between reconstructions (Gwet's ACPolyenergetic = 0.78 vs Gwet's ACVMI = 0.775). CONCLUSION: Polyenergetic reconstruction significantly mitigates streak artifacts in PCD-CT imaging, enhancing quantitative and qualitative image quality. This advancement addresses a known limitation of current PCD-CT reconstruction techniques, offering a promising approach to improving diagnostic reliability and accuracy in clinical practice. We demonstrate that future software implementations can resolve this artifact.

Intraindividual reproducibility of myocardial radiomic features between energy-integrating detector and photon-counting detector CT angiography.

BACKGROUND: Radiomics is not yet used in clinical practice due to concerns regarding its susceptibility to technical factors. We aimed to assess the stability and interscan and interreader reproducibility of myocardial radiomic features between energy-integrating detector computed tomography (EID-CT) and photon-counting detector CT (PCD-CT) in patients undergoing coronary CT angiography (CCTA) on both systems. METHODS: Consecutive patients undergoing clinically indicated CCTA on an EID-CT were prospectively enrolled for a PCD-CT CCTA within 30 days. Virtual monoenergetic images (VMI) at various keV levels and polychromatic images (T3D) were generated for PCD-CT, with image reconstruction parameters standardized between scans. Two readers performed myocardial segmentation and 110 radiomic features were compared intraindividually between EID-CT and PDC-CT series. The agreement of parameters was assessed using the intraclass correlation coefficient and paired t-test for the stability of the parameters. RESULTS: Eighteen patients (15 males) aged 67.6 ± 9.7 years (mean ± standard deviation) were included. Besides polychromatic PCD-CT reconstructions, 60- and 70-keV VMIs showed the highest feature stability compared to EID-CT (96%, 90%, and 92%, respectively). The interscan reproducibility of features was moderate even in the most favorable comparisons (median ICC 0.50 [interquartile range 0.20-0.60] for T3D; 0.56 [0.33-0.74] for 60 keV; 0.50 [0.36-0.62] for 70 keV). Interreader reproducibility was excellent for the PCD-CT series and good for EID-CT segmentations. CONCLUSION: Most myocardial radiomic features remain stable between EID-CT and PCD-CT. While features demonstrated moderate reproducibility between scanners, technological advances associated with PCD-CT may lead to greater reproducibility, potentially expediting future standardization efforts. RELEVANCE STATEMENT: While the use of PCD-CT may facilitate reduced interreader variability in radiomics analysis, the observed interscanner variations in comparison to EID-CT should be taken into account in future research, with efforts being made to minimize their impact in future radiomics studies. KEY POINTS: Most myocardial radiomic features resulted in being stable between EID-CT and PCD-CT on certain VMIs. The reproducibility of parameters between detector technologies was limited. PCD-CT improved interreader reproducibility of myocardial radiomic features.

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.

PURPOSE: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA). PATIENTS AND METHODS: A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers. RESULTS: A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%. CONCLUSIONS: The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

Improved Detection of Small and Low-Density Plaques in Virtual Noncontrast Imaging-based Calcium Scoring at Photon-Counting Detector CT.

Purpose To investigate the impact of plaque size and density on virtual noncontrast (VNC)-based coronary artery calcium scoring (CACS) using photon-counting detector CT and to provide safety net reconstructions for improved detection of subtle plaques in patients whose VNC-based CACS would otherwise be erroneously zero when compared with true noncontrast (TNC)-based CACS. Materials and Methods In this prospective study, CACS was evaluated in a phantom containing calcifications with different diameters (5, 3, and 1 mm) and densities (800, 400, and 200 mg/cm3) and in participants who underwent TNC and contrast-enhanced cardiac photon-counting detector CT (July 2021-March 2022). VNC images were reconstructed at different virtual monoenergetic imaging (55-80 keV) and quantum iterative reconstruction (QIR) levels (QIR,1-4). TNC scans at 70 keV with QIR off served as the reference standard. In vitro CACS was analyzed using standard settings (3.0-mm sections, kernel Qr36, 130-HU threshold). Calcification detectability and CACS of small and low-density plaques were also evaluated using 1.0-mm sections, kernel Qr44, and 120- or 110-HU thresholds. Safety net reconstructions were defined based on background Agatston scores and evaluated in vivo in TNC plaques initially nondetectable using standard VNC reconstructions. Results The in vivo cohort included 63 participants (57.8 years ± 15.5 [SD]; 37 [59%] male, 26 [41%] female). Correlation and agreement between standard CACSVNC and CACSTNC were higher in large- and medium-sized and high- and medium-density than in low-density plaques (in vitro: intraclass correlation coefficient [ICC] ≥ 0.90; r > 0.9 vs ICC = 0.20-0.48; r = 0.5-0.6). Small plaques were not detectable using standard VNC reconstructions. Calcification detectability was highest using 1.0-mm sections, kernel Qr44, 120- and 110-HU thresholds, and QIR level of 2 or less VNC reconstructions. Compared with standard VNC, using safety net reconstructions (55 keV, QIR 2, 110-HU threshold) for in vivo subtle plaque detection led to higher detection (increased by 89% [50 of 56]) and improved correlation and agreement of CACSVNC with CACSTNC (in vivo: ICC = 0.51-0.61; r = 0.6). Conclusion Compared with TNC-based calcium scoring, VNC-based calcium scoring was limited for small and low-density plaques but improved using safety net reconstructions, which may be particularly useful in patients with low calcium scores who would otherwise be treated based on potentially false-negative results. Keywords: Coronary Artery Calcium CT, Photon-Counting Detector CT, Virtual Noncontrast, Plaque Size, Plaque Density Supplemental material is available for this article. © RSNA, 2024.

Ultra-high resolution coronary CT angiography on photon-counting detector CT: bi-centre study on the impact of quantum iterative reconstruction on image quality and accuracy of stenosis measurements.

PURPOSE: To assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT. METHOD: A dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1-4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans. RESULTS: Increasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15). CONCLUSIONS: The QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.

Radiomic study of antenatal prediction of severe placenta accreta spectrum from MR imaging.

OBJECTIVE: We previously demonstrated the potential of radiomics for prediction of severe histological Placenta Accreta Spectrum (PAS) subtypes using T2-weighted MRI. We aim is to validate our model using an additional dataset. Secondly, we explore whether performance is improved using a new approach to develop a new multivariate radiomics model. METHODS: Multi-centre retrospective analysis conducted between 2018-2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomised feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including Area Under the Curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity and specificity. RESULTS: We present 100 women (mean age 34.6 (±3.9) with PAS, 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found a Support Vector Machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922) and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990) and specificity of 0.447 (95% CI 0.167; 0.667). CONCLUSION: We validated our previous model and present a new multivariate radiomic model for prediction of severe placenta increta from a well-defined, cohort of PAS cases. ADVANCES IN KNOWLEDGE: Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.

Intra-individual comparison of coronary CT angiography-based FFR between energy-integrating and photon-counting detector CT systems.

BACKGROUND: Coronary computed tomography angiography (CCTA)-based fractional flow reserve (CT-FFR) allows for noninvasive determination of the functional severity of anatomic lesions in patients with coronary artery disease. The aim of this study was to intra-individually compare CT-FFR between photon-counting detector (PCD) and conventional energy-integrating detector (EID) CT systems. METHODS: In this single-center prospective study, subjects who underwent clinically indicated CCTA on an EID-CT system were recruited for a research CCTA on PCD-CT within 30 days. Image reconstruction settings were matched as closely as possible between EID-CT (Bv36 kernel, iterative reconstruction strength level 3, slice thickness 0.5 mm) and PCD-CT (Bv36 kernel, quantum iterative reconstruction level 3, virtual monoenergetic level 55 keV, slice thickness 0.6 mm). CT-FFR was measured semi-automatically using a prototype on-site machine learning algorithm by two readers. CT-FFR analysis was performed per-patient and per-vessel, and a CT-FFR ≤ 0.75 was considered hemodynamically significant. RESULTS: A total of 22 patients (63.3 ± 9.2 years; 7 women) were included. Median time between EID-CT and PCD-CT was 5.5 days. Comparison of CT-FFR values showed no significant difference and strong agreement between EID-CT and PCD-CT in the per-vessel analysis (0.88 [0.74-0.94] vs. 0.87 [0.76-0.93], P = 0.096, mean bias 0.02, limits of agreement [LoA] -0.14/0.19, r = 0.83, ICC = 0.92), and in the per-patient analysis (0.81 [0.60-0.86] vs. 0.76 [0.64-0.86], P = 0.768, mean bias 0.02, LoA -0.15/0.19, r = 0.90, ICC = 0.93). All included patients were classified into the same category (CT-FFR > 0.75 vs ≤0.75) with both CT systems. CONCLUSIONS: CT-FFR evaluation is feasible with PCD-CT and it shows a strong agreement with EID-CT-based evaluation when images are similarly reconstructed.