Citations

Abstract This article provides an overview of deep-learning–based techniques in CT image reconstruction and processing (referred to as “DLR”), covering technical implementations, performance evaluation, radiation dose reduction, and future perspectives. DLR methods can be categorized into projection-space, projection-to-image-space, image-space, and various hybrid techniques, with applications such as noise reduction, artefact correction, and spatial resolution enhancement. Performance evaluations include phantom-based studies, patient image-based studies, and virtual imaging trials. These evaluation studies demonstrated that DLR can effectively reduce image noise while preserving an image texture like in traditional filtered-backprojection (FBP) images, although the extent of radiation dose reduction varies widely depending on the study and the specific diagnostic task. Challenges remain in low-contrast lesion detection and characterization, where dose reduction may still be less than 50% compared to traditional reconstruction methods. Additionally, the potential for DLR methods to generate false structures or “hallucinations,” especially at low radiation doses, emphasizes the need for effective monitoring and mitigation strategies from both technical and clinical perspectives. Quantitative, accurate, and efficient evaluation techniques, such as virtual image trial-based methods, can be explored to help optimize these algorithms for reducing radiation dose and enhancing diagnostic performance.

Abstract Background and Aims Intestinal ultrasound (IUS) is increasingly used to monitor treatment efficacy in inflammatory bowel disease (IBD) trials. However, standardized definitions for response, remission, and optimal assessment timing remain undefined. An international expert consensus meeting was held to establish IUS endpoints for clinical trials. Methods A panel of 35 international gastroenterologists and radiologists participated in a modified Delphi process, reviewing the literature and developing consensus statements. Agreement was defined as at least 75% consensus. Results Consensus was reached on 150 statements across four domains: general IBD (30 statements), luminal Crohn’s disease (CD) (43), perianal CD (51), and ulcerative colitis (UC) (26). For luminal CD and UC, ultrasound response was defined by: (1) a ≥25% reduction in bowel wall thickness (BWT) from baseline, or (2) multifactorial improvement, combining BWT reduction with ≥1 grade decrease in color Doppler signal (CDS) or another IUS parameter. Assessments were set at weeks 4-8 for the colon and week 12 for the terminal ileum. Ultrasound remission in luminal CD was defined as: (1) BWT normalization (≤3 mm) or (2) normalization of multiple parameters, including BWT, CDS, and all other IUS parameters. Similar remission criteria were proposed for UC, but the sigmoid BWT normal range (3-4 mm) remained uncertain. The bowel ultrasound score (BUSS) for CD and the Milan ultrasound criteria (MUC) for UC were supported as standardized scoring systems for trials. Conclusion This consensus provides standardized IUS definitions to enhance consistency in IBD trials, supporting the integration of IUS in future research.

This consensus by the Society of Abdominal Radiology Inflammatory Bowel Disease Disease Focused Panel provides an updated imaging-based definition of small bowel Crohn disease stricture for CT and MR enterography.

Radiologists are integral members of the interdisciplinary team for patients with mucinous appendiceal neoplasms and can directly affect care by suggesting the diagnosis of pseudomyxoma peritonei, optimizing imaging protocols for evaluating peritoneal implants, and reporting the primary tumor and spilled tumor cells thoroughly.

Abstract Quantitative computed tomography (CT) enables the measurement of biophysical processes characterized by morphology, composition, flow, and/or motion to aid in clinical diagnosis and intervention. Since its initial application for determining bone mineral density for skeletal fragility assessment, quantitative CT has continued to evolve alongside CT’s technological advancements. A key challenge facing quantitative CT is the lack of standardization pertinent to image acquisition, reconstruction, and image analysis. With the introduction of spectral CT involving dual-energy approaches and photon-counting detectors (PCD), we are now able to obtain detailed information regarding mass densities of endogenous and exogeneous materials. Furthermore, energy-resolved CT yields spectral image types (eg, virtual monoenergetic image) that are, in principle, independent of tube potential and patient size. This paves the way for workflow standardization and helps improve the consistency and reproducibility of CT-derived measurements. In this article, we review clinical applications of quantitative CT, discuss key challenges associated with quantitative CT and its adoption into routine practice, and outline the unique benefits ushered by new CT technologies such as PCD-CT towards improving quantitative imaging.

Abstract Background Accurate low‐dose CT simulation is required to efficiently assess reconstruction and dose reduction techniques. Projection domain noise insertion requires proprietary information from manufacturers. Analytic image domain noise insertion methods are successful for linear reconstruction algorithms, however extending them to non‐linear algorithms remains challenging. Emerging, deep‐learning‐based image domain noise insertion methods have potential, but few approaches have explicitly incorporated physics information and a texture‐synthesis model to guide the generation of locally and globally correlated noise texture. Purpose We proposed a p hysics‐informed model‐based gener a tive neura l network for simulating scann e r‐ and algori t hm‐specific low‐dose C T e xams (PALETTE). It is expected to provide an alternative to projection domain noise insertion methods in the absence of manufacturers’ proprietary information and tools. Methods PALETTE integrated a physics‐based noise prior generation process, a Noise2Noisier sub‐network, and a noise texture synthesis sub‐network. The Noise2Noisier sub‐network provided a bias prior, which, combined with the noise prior, served as the inputs to noise texture synthesis sub‐network. Explicit regularizations in spatial and frequency domains were developed to account for noise spatial correlation and frequency characteristics. For proof‐of‐concept, PALETTE was trained and validated for a commercial iterative reconstruction algorithm (SAFIRE, Siemens Healthineers), using the paired routine and 25% dose images from CT phantoms (lateral size 30–40 cm; three training and four testing phantoms) and open‐access patient cases (10 training and 20 testing cases). In phantom validation, noise power spectra (NPS) were compared in water background and tissue‐mimicking inserts, using peak frequency and mean‐absolute‐error (MAE). In patient case evaluation, visual inspection and quantitative assessment were conducted on axial, coronal, and sagittal planes. Local and global noise texture were visually inspected in low‐dose CT images and the difference images between routine and low dose. Noise levels in liver and fat were measured. Local and global 2D Fourier magnitude spectra of the difference images and the corresponding radial mean profiles were used to assess similarity in noise frequency components within tissues and entire field‐of‐view, using spectral correlation mapper (SCM) and spectral angle mapper (SAM). Several baseline neural network models (e.g., GAN) were included in the evaluation. Statistical significance was tested using a t ‐test for related samples. Results PALETTE‐derived NPS showed accurate noise peak frequency (PALETTE/reference: water 1.40/1.40 lp/cm; inserts 1.7/1.7lp/cm) and small MAE (≤0.65 HU 2 cm 2 ). PALETTE created anatomy‐dependent noise texture, showing realistic local and global granularity and streaks. No statistically significant difference was observed in noise levels ( p > 0.05). Noise range was comparable across 3D image volume (PALETTE / reference):liver – [18.0, 53.4] / [19.3, 50.0] HU; fat – [11.7, 42.4] / [12.1, 41.3] HU. Percent absolute difference of local noise was small (mean ± standard deviation): liver 4.1%±3.1%, fat 4.6%±3.1%. Noise frequency distribution was close to the reference (mean per case): SCM ≥ 0.92, SAM ≤ 0.22. Additionally, PALETTE outperformed all baseline models in visual inspection and quantitative comparison. Conclusion PALETTE can provide high‐quality image domain noise insertion for simulating accurate low‐dose CT images created with a commercial non‐linear reconstruction algorithm.

Abstract Background We previously identified circulating and MRI biomarkers associated with the surgical management of Crohn’s disease (CD). Here we tested associations between these biomarkers and ileal resection inflammation and collagen content. Methods Fifty CD patients undergoing ileal resection were prospectively enrolled at 4 centers. Circulating CD64, extracellular matrix protein 1 (ECM1), GM-CSF autoantibodies (GM-CSF Ab), and fecal calprotectin were measured by ELISA. Ileal 3-dimensional magnetization transfer ratio (3D MTR), modified Look-Locker inversion recovery (MOLLI) T1 relaxation, diffusion-weighted intravoxel incoherent motion (IVIM), and the simplified magnetic resonance index of activity (sMaRIA) were measured by MRI. Ileal resection specimen acute inflammation was graded, and collagen content was measured quantitatively using second harmonic imaging microscopy. Associations between biomarkers and ileal collagen content were tested. Results Median (interquartile range [IQR]) age was 19.5 (16-33) years. We observed an inverse relationship between ileal acute inflammation and collagen content (r = −0.39 [95% confidence interval {CI}: −0.61, −0.10], P = .008). Most patients (33 [66%]) received biologics, with no variation in collagen content with treatment exposures. In the univariate analysis, CD64, GM-CSF Ab, fecal calprotectin, and sMaRIA were positively associated with acute inflammation and negatively associated with collagen content (P < .1). The multivariable model for ileal collagen content (R2 = 0.31 [95% CI: 0.11, 0.52]) included log CD64 (β = −.27; P = .19), log ECM1 (β = .47; P = .06), log GM-CSF Ab (β = −.15; P = .01), IVIM f (β = .29, P = .10), and IVIM D* (β = 1.69, P = .13). Conclusions Clinically available and exploratory circulating and MRI biomarkers are associated with the degree of inflammation versus fibrosis in CD ileal resections. With further validation, these biomarkers may be used to guide medical and surgical decision-making for refractory CD.

Abstract Background Progress in clinical trials and drug development for paediatric perianal fistulising CD (pfCD) depends upon accurate, reproducible imaging to assess disease activity and measure treatment response. We evaluated the inter- and intra-rater reliability of existing radiologic disease activity indices and anatomical classification systems previously identified as appropriate for assessment of paediatric pfCD.1 Methods A retrospective cohort (N=50) of magnetic resonance imaging (MRI) exams representing a full spectrum of paediatric (<18 years) perianal CD severity was selected by a single expert paediatric radiologist. Methods described by Zou2 were used to estimate sample size. Three separate radiologists (2 paediatric) blinded to clinical information assessed pfCD in the exams using the Magnetic Resonance Novel Index for Fistula Imaging in CD (MAGNIFI-CD), Van Assche Index (VAI), modified VAI (mVAI), Paediatric MRI based Perianal Crohn’s disease (PEMPAC) index, a visual analogue scale (VAS) of overall pfCD disease severity (range, 0-100 mm), as well as with the Park’s and St. James University Hospital anatomic classification systems. Two radiologists read all exams twice, separated by 2 weeks, and 1 radiologist read the exams once (250 total reads). Intra- and inter-rater reliability was evaluated using intraclass correlation coefficients (ICC; equivalent to weighted kappa). Precision of the ICC estimates was quantified with 95% confidence intervals (CIs) obtained using clustered bootstrapping. Degree of reliability was interpreted with benchmarks proposed by Landis and Koch3. Results Median age of children was 13.5 years (IQR 11.4-16.1), and 64% were male. Median Paediatric Crohn’s Disease Activity Index score at time of imaging was 17.5 (IQR 10-32.5). Substantial (ICC>0.61) inter-rater reliability was observed for MAGNIFI-CD, VAI, and mVAI (Table 1), with the highest ICCs observed for the mVAI (0.72 [0.6 to 0.79]). Moderate (ICC>0.41) inter-rater reliability was observed for PEMPAC, St. James University Hospital classification, and the VAS, and fair (ICC>0.21) inter-rater reliability was observed for Parks classification. Intra-rater reliability was nearly perfect (ICC>0.81) for the VAS, and substantial for all indices except for Parks classification, which had moderate (0.60 [0.44 to 0.75]) intra-rater reliability (Table 1). Conclusion Most existing multi-item MRI-based disease activity indices and the St. James Hospital classification were reliable for the assessment of paediatric pfCD in our cohort. Our preliminary results suggest that the disease activity indices may have utility to measure treatment effect in paediatric pfCD clinical trials. References 1.Crowley E, Ma C, Guizzetti L, Zou G, Lewindon PJ, Gee MS, et al. Recommendations for Standardizing MRI-based Evaluation of Perianal Fistulizing Disease Activity in Pediatric Crohn's Disease Clinical Trials. Inflamm Bowel Dis. 2024;30(3):357-69. 2.Zou GY. Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Statistics in Medicine. 2012;31(29):3972-398 3.Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-74.