Prospective multicenter study on personalized and optimized MDCT contrast protocols: results on liver enhancement

Validation of a multi-parameter algorithm for personalized contrast injection protocol in liver CT

BACKGROUND

In liver computed tomography (CT), tailoring the contrast injection to the patient's specific characteristics is relevant for optimal imaging and patient safety. We evaluated a novel algorithm engineered for personalized contrast injection to achieve reproducible liver enhancement centered on 50 HU.

METHODS

From September 2020 to August 31, 2022, CT data from consecutive adult patients were prospectively collected at our multicenter premises. Inclusion criteria consisted of an abdominal CT referral for cancer staging or follow-up. For all examinations, a web interface incorporating data from the radiology information system (patient details and examination information) and radiographer-inputted data (patient fat-free mass, imaging center, kVp, contrast agent details, and imaging phase) were used. Calculated contrast volume and injection rate were manually entered into the CT console controlling the injector. Iopamidol 370 mgI/mL or Iohexol 350 mgI/mL were used, and kVp varied (80, 100, or 120) based on patient habitus.

RESULTS

We enrolled 384 patients (mean age 61.2 years, range 21.1-94.5). The amount of administered iodine dose (gI) was not significantly different across contrast agents (p = 0.700), while a significant increase in iodine dose was observed with increasing kVp (p < 0.001) and in males versus females (p < 0.001), as expected. Despite the differences in administered iodine load, image quality was reproducible across patients with 72.1% of the examinations falling within the desirable range of 40-60 HU.

CONCLUSION

This study validated a novel algorithm for personalized contrast injection in adult abdominal CT, achieving consistent liver enhancement centered at 50 HU.

RELEVANCE STATEMENT

In healthcare's ongoing shift towards personalized medicine, the algorithm offers excellent potential to improve diagnostic accuracy and patient management, particularly for the detection and follow-up of liver malignancies.

KEY POINTS

The algorithm achieves reproducible liver enhancement, promising improved diagnostic accuracy and patient management in diverse clinical settings. The real-world study demonstrates this algorithm's adaptability to different variables ensuring high-quality liver imaging. A personalized algorithm optimizes liver CT, improving the visibility, conspicuity, and follow-up of liver lesions.

Reduced contrast dose for CT head studies during COVID-19-related contrast shortage: Lesson from a crisis

OBJECTIVE

Intravenous contrast injection protocol for certain CT studies at our institution was revised in June 2022 in response to the global shortage of iohexol. This included CT head studies performed for neuro-navigation (contrast dose from 90 mL to 70 mL). The quality of these studies was assessed.

METHODS

Consecutive CT scans before (n = 32) and after (n = 32) contrast dose reduction were reviewed. Demographic data was obtained from the chart. Subjective observations made by two radiologists in consensus included overall study quality (Likert scale of 1 to 5) and lesion location, margins and internal characteristics that were compared with MRI findings (reference standard) using Fisher's exact test. Superior sagittal sinus attenuation, used as an objective measurement of enhancement, and lesion size were compared using Student's t-test. The institutional database was searched for any study requiring repetition or deemed non-diagnostic.

RESULTS/DISCUSSION

The average age (61.1 ± 12.7 years and 61.6 ± 14.9 years) and body surface area (BSA) (1.9 ± 0.3 m2 and 1.9 ± 0.02 m2) was not significantly different (p > 0.05) between groups. There was no significant difference (p > 0.05) in objective or subjective enhancement between the two groups. There was no significant difference between CT and MRI for lesion size, location, number, margins and internal enhancement characteristics in the two groups. No study required repetition or was reported as non-diagnostic. There was no adverse comment about study quality in operative notes.

CONCLUSION

Reduced contrast dose neuro-navigation CT head studies are not different in quality compared to the conventional studies.

Multivariable Technique for the Evaluation of the Trans-stenotic Pressure Gradient

PURPOSE

This study establishes a reliable image-based multivariable technique for measuring the trans-stenotic pressure gradient.

METHODS

A self-made in vitro steady flow model based on adjustable velocities and stenotic properties were used as the experimental subject. The pre-stenotic flow velocity, severity, and length of the stenosis were used as the input variables. Based on equations used to fit the plots of the physically measured pressure gradient values versus each input variable, a multivariable formula for the pressure gradient measurement could then be derived. The flow model was scanned using velocity-encoded phase-contrast magnetic resonance imaging (PC-MRI) to validate the derived formula while simultaneously measuring the trans-stenotic pressure gradient. The correlation between the physically-measured pressure gradient values and the pressure gradient values calculated using the new formula were subsequently analyzed.

RESULTS

The results of linear regression analysis using the physically measured pressure gradient values for the new method were compared to values obtained using the simplified Bernoulli equation (R², 0.991, and 0.975, respectively). In a paired t-test, no statistically significant difference was found between the new method and the physical measurements.

CONCLUSIONS

The derived multivariable technique was found to reliably measure the trans-stenotic pressure gradient, with better performance than a traditional procedure based on the simplified Bernoulli equation.

Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography

PURPOSE

The purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, Abdomen and Pelvis.

MATERIALS AND METHODS

Training, testing and validation data were acquired from a dataset of 82,690 chest and abdomen CT examinations performed at 17 different institutions. Free text in DICOM metadata was utilized as weak labels for semi-supervised classification training. Contrast phase identification was approached as a classification task, using a 12-layer CNN and ResNet18 with four contrast-phase output. The model was reformulated to fit a regression task aimed to predict actual seconds from time of IV contrast administration to series image acquisition. Finally, transfer learning was used to optimize the model to predict contrast presence on CT Chest.

RESULTS

By training based on labels inferred from noisy, free text DICOM information, contrast phase was predicted with 93.3% test accuracy (95% CI: 89.3%, 96.6%) . Regression analysis resulted in delineation of early vs late arterial phases and a nephrogenic phase in between the portal venous and delayed excretory phase. Transfer learning applied to Chest CT achieved an AUROC of 0.776 (95% CI: 0.721, 0.832) directly using the model trained for abdomen CT and 0.999 (95% CI: 0.998, 1.000) by fine-tuning.

CONCLUSIONS

The presence and phase of contrast on CT examinations of the Abdomen-pelvis accurately and automatically be ascertained by a machine learning algorithm. Transfer learning applied to CT Chest achieves high precision with as little as 100 labeled samples.

Lean body weight-adjusted intravenous iodinated contrast dose for abdominal CT in dogs reduces interpatient enhancement variability while providing diagnostic quality organ enhancement

Contrast-enhanced computed tomography (CECT) is increasingly used to screen for abdominal pathology in dogs, and the contrast dose used is commonly calculated as a linear function of total body weight (TBW). Body fat is not metabolically active and contributes little to dispersing or diluting contrast medium (CM) in the blood. This prospective, analytic, cross-section design pilot study aimed to establish the feasibility of intravenous CM dosed according to lean body weight (LBW) for abdominal CECT in dogs compared to TBW. We hypothesized that when dosing intravenous CM according to LBW, studies will remain at diagnostic quality, there will be a reduced interindividual contrast enhancement (CE) variability, and there will be less change to heart rate and blood pressure in dogs compared to when administering CM calculated on TBW. Twelve dogs had two CECT studies with contrast doses according to TBW and LBW at least 8 weeks apart. Interindividual organ and vessel CE variability, diagnostic quality of the studies, and changes in physiological status were compared between protocols. The LBW-based protocol provided less variability in the CE of most organs and vessels (except the aorta). When dosed according to LBW, liver enhancement was positively associated with grams of iodine per kg TBW during the portal venous phase (P = 0.046). There was no significant difference in physiological parameters after CM administration between dosing protocols. Our conclusion is that a CM dose based on LBW for abdominal CECT lowers interindividual CE variability and is effective at maintaining studies of diagnostic quality.

MRI biomarkers in neuro-oncology

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.