Baffour, Francis

Abstract Objectives To describe the feasibility and evaluate the performance of multiphasic photon-counting detector (PCD) CT for detecting breast cancer and nodal metastases with correlative dynamic breast MRI and digital mammography as the reference standard. Methods Adult females with biopsy-proven breast cancer undergoing staging breast MRI were prospectively recruited to undergo a multiphasic PCD-CT using a 3-phase protocol: a non-contrast ultra-high-resolution (UHR) scan and 2 intravenous contrast-enhanced scans with 50 and 180 s delay. Three breast radiologists compared CT characteristics of the index malignancy, regional lymphadenopathy, and extramammary findings to MRI. Results Thirteen patients underwent both an MRI and PCD-CT (mean age: 53 years, range: 36-75 years). Eleven of thirteen cases demonstrated suspicious mass or non-mass enhancement on PCD-CT when compared to MRI. All cases with metastatic lymphadenopathy (3/3 cases) demonstrated early avid enhancement similar to the index malignancy. All cases with multifocal or multicentric disease on MRI were also identified on PCD-CT (3/3 cases), including a 4 mm suspicious satellite lesion. Four of five patients with residual suspicious post-biopsy calcifications on mammograms were detected on the UHR PCD-CT scan. Owing to increased field-of-view at PCD-CT, a 5 mm thoracic vertebral metastasis was identified at PCD-CT and not with the breast MRI. Conclusions A 3-phase PCD-CT scan protocol shows initial promising results in characterizing breast cancer and regional lymphadenopathy similar to MRI and detects microcalcifications in 80% of cases. Advances in knowledge UHR and spectral capabilities of PCD-CT may allow for comprehensive characterization of breast cancer and may represent an alternative to breast MRI in select cases.

Epidural steroid injections are commonly performed using fluoroscopic or CT guidance. With both modalities, the injection of contrast material is necessary before steroid administration to ensure adequate epidural flow and exclude non-epidural flow. While fluoroscopic guidance is conventional, CT is utilized at some centers and can be particularly helpful in the setting of challenging or postoperative anatomy. It is important for proceduralists to be adept at evaluating contrast media flow patterns under both modalities. The goal of this review article is to describe and provide examples of epidural and non-epidural flow patterns on both conventional fluoroscopy and CT. Specific non-epidural patterns discussed include intrathecal flow, intradural/subdural flow, vascular uptake, flow into the retrodural space of Okada, inadvertent facet joint flow, and intradiscal flow.

Abstract Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows “always-on” dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

Study Design. Retrospective cohort study. Objective. Hounsfield units (HUs) are known to correlate with clinical outcomes, but no study has evaluated how they correlate with biomechanical computed tomography (BCT) and dual-energy x-ray absorptiometry (DXA) measurements. Summary of Background. Low bone mineral density (BMD) represents a major risk factor for fracture and poor outcomes following spine surgery. DXA can provide regional BMD measurements but has limitations. Opportunistic HUs provide targeted BMD estimates; however, they are not formally accepted for diagnosing osteoporosis in current guidelines. More recently, BCT analysis has emerged as a new modality endorsed by the International Society for Clinical Densitometry for assessing bone strength. Methods. Consecutive cases from 2017 to 2022 at a single institution were reviewed for patients who underwent BCT in the thoracolumbar spine. BCT-measured vertebral strength, trabecular BMD, and the corresponding American College of Radiology Classification were recorded. DXA studies within three months of the BCT were reviewed. Pearson Correlation Coefficients were calculated, and receiver-operating characteristic curves were constructed to assess the predictive capacity of HUs. Threshold analysis was performed to identify optimal HU values for identifying osteoporosis and low BMD. Results. Correlation analysis of 114 cases revealed a strong relationship between HUs and BCT vertebral strength (r=0.69; P<0.0001; R 2=0.47) and trabecular BMD (r=0.76; P<0.0001; R 2=0.58). However, DXA poorly correlated with opportunistic HUs and BCT measurements. HUs accurately predicted osteoporosis and low BMD (Osteoporosis: C=0.95, 95% CI 0.89–1.00; Low BMD: C=0.87, 95% CI 0.79–0.96). Threshold analysis revealed that 106 and 122 HUs represent optimal thresholds for detecting osteoporosis and low BMD. Conclusion. Opportunistic HUs strongly correlated with BCT-based measures, while neither correlated strongly with DXA-based BMD measures in the thoracolumbar spine. HUs are easy to perform at no additional cost and provide accurate BMD estimates at noninstrumented vertebral levels across all American College of Radiology-designated BMD categories.

BackgroundSarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning–based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diagnosed multiple myeloma (NDMM) were reviewed to determine whether the presence of sarcopenia had any prognostic value.MethodsSarcopenia was detected by accurate segmentation and measurement of the skeletal muscle components present at the level of the L3 vertebrae. These skeletal muscle measurements were further normalized by the height of the patient to obtain the skeletal muscle index for each patient to classify them as sarcopenic or not.ResultsThe study cohort consisted of 322 patients of which 67 (28%) were categorized as having high risk (HR) fluorescence in situ hybridization (FISH) cytogenetics. A total of 171 (53%) patients were sarcopenic based on their peri‐diagnosis standard‐dose CT scan. The median overall survival (OS) and 2‐year mortality rate for sarcopenic patients was 44 months and 40% compared to 90 months and 18% for those not sarcopenic, respectively (p < .0001 for both comparisons). In a multivariable model, the adverse prognostic impact of sarcopenia was independent of International Staging System stage, age, and HR FISH cytogenetics.ConclusionsSarcopenia identified by a machine learning–based convolutional neural network algorithm significantly affects OS in patients with NDMM. Future studies using this machine learning–based methodology of assessing sarcopenia in larger prospective clinical trials are required to validate these findings.

AbstractBackgroundDual‐energy CT with virtual noncalcium (VNCa) images allows the evaluation of focal intramedullary bone marrow involvement in patients with multiple myeloma. However, current commercial VNCa techniques suffer from excessive image noise and artifacts due to material decomposition used in synthesizing VNCa images.ObjectivesIn this work, we aim to improve VNCa image quality for the assessment of focal multiple myeloma, using an Artificial intelligence based Generalizable Algorithm for mulTi‐Energy CT (AGATE) method.Materials and methodsAGATE method used a custom dual‐task convolutional neural network (CNN) that concurrently carries out material classification and quantification. The material classification task provided an auxiliary regularization to the material quantification task. CNN parameters were optimized using custom loss functions that involved cross‐entropy, physics‐informed constraints, structural redundancy in spectral and material images, and texture information in spectral images. For training data, CT phantoms (diameters 30 to 45 cm) with tissue‐mimicking inserts were scanned on a third generation dual‐source CT system. Scans were performed at routine dose and half of the routine dose. Small image patches (i.e., 40 × 40 pixels) of tissue‐mimicking inserts with known basis material densities were extracted for training samples. Numerically simulated insert materials with various shapes increased diversity of training samples. Generalizability of AGATE was evaluated using CT images from phantoms and patients. In phantoms, material decomposition accuracy was estimated using mean‐absolute‐percent‐error (MAPE), using physical inserts that were not used during the training. Noise power spectrum (NPS) and modulation transfer function (MTF) were compared across phantom sizes and radiation dose levels. Five patients with multiple myeloma underwent dual‐energy CT, with VNCa images generated using a commercial method and AGATE. Two fellowship‐trained musculoskeletal radiologists reviewed the VNCa images (commercial and AGATE) side‐by‐side using a dual‐monitor display, blinded to VNCa type, rating the image quality for focal multiple myeloma lesion visualization using a 5‐level Likert comparison scale (−2 = worse visualization and diagnostic confidence, −1 = worse visualization but equivalent diagnostic confidence, 0 = equivalent visualization and diagnostic confidence, 1 = improved visualization but equivalent diagnostic confidence, 2 = improved visualization and diagnostic confidence). A post hoc assignment of comparison ratings was performed to rank AGATE images in comparison to commercial ones.ResultsAGATE demonstrated consistent material quantification accuracy across phantom sizes and radiation dose levels, with MAPE ranging from 0.7% to 4.4% across all testing materials. Compared to commercial VNCa images, the AGATE‐synthesized VNCa images yielded considerably lower image noise (50–77% noise reduction) without compromising noise texture or spatial resolution across different phantom sizes and two radiation doses. AGATE VNCa images had markedly reduced area under NPS curves and maintained NPS peak frequency (0.7 lp/cm to 1.0 lp/cm), with similar MTF curves (50% MTF at 3.0 lp/cm). In patients, AGATE demonstrated reduced image noise and artifacts with improved delineation of focal multiple myeloma lesions (all readers comparison scores indicating improved overall diagnostic image quality [scores 1 or 2]).ConclusionsAGATE demonstrated reduced noise and artifacts in VNCa images and ability to improve visualization of bone marrow lesions for assessing multiple myeloma.