A physics-informed deep learning framework for dynamic susceptibility contrast perfusion MRI.

BACKGROUND: Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant characteristics of the perfusion dynamics and suffer from a lack of standardization. PURPOSE: We propose a physics-informed deep learning framework which is capable of analyzing dynamic susceptibility contrast perfusion MRI data and recovering the dynamic tissue response with high accuracy. METHODS: The framework uses physics-informed neural networks (PINNs) to learn the voxel-wise TRF, which represents the dynamic response of the local vascular network to the contrast agent bolus. The network output is stabilized by total variation and elastic net regularization. Parameter maps of normalized cerebral blood flow (nCBF) and volume (nCBV) are then calculated from the predicted residue functions. The results are validated using extensive comparisons to values derived by conventional Tikhonov-regularized singular value decomposition (TiSVD), in silico simulations and an in vivo dataset of perfusion MRI exams of patients with high-grade gliomas. RESULTS: The simulation results demonstrate that PINN-derived residue functions show a high concordance with the true functions and that the calculated values of nCBF and nCBV converge towards the true values for higher contrast-to-noise ratios. In the in vivo dataset, we find high correlations between conventionally derived and PINN-predicted perfusion parameters (Pearson's rho for nCBF: 0.84 ± 0.03 $0.84 \pm 0.03$ and nCBV: 0.92 ± 0.03 $0.92 \pm 0.03$ ) and very high indices of image similarity (structural similarity index for nCBF: 0.91 ± 0.03 $0.91 \pm 0.03$ and for nCBV: 0.98 ± 0.00 $0.98 \pm 0.00$ ). CONCLUSIONS: PINNs can be used to analyze perfusion MRI data and stably recover the response functions of the local vasculature with high accuracy.

Whole-Brain Vascular Architecture Mapping Identifies Region-Specific Microvascular Profiles In Vivo.

BACKGROUND AND PURPOSE: The novel MR imaging technique of vascular architecture mapping allows in vivo characterization of local changes in cerebral microvasculature, but reference ranges for vascular architecture mapping parameters in healthy brain tissue are lacking, limiting its potential applicability as an MR imaging biomarker in clinical practice. We conducted whole-brain vascular architecture mapping in a large cohort to establish vascular architecture mapping parameter references ranges and identify region-specific cortical and subcortical microvascular profiles. MATERIALS AND METHODS: This was a single-center examination of adult patients with unifocal, stable low-grade gliomas with multiband spin- and gradient-echo EPI sequence at 3T using parallel imaging. Voxelwise plotting of resulting values for gradient-echo (R2*) versus spin-echo (R2) relaxation rates during contrast agent bolus administration generates vessel vortex curves that allow the extraction of vascular architecture mapping parameters representative of, eg, vessel type, vessel radius, or CBV in the underlying voxel. Averaged whole-brain parametric maps were calculated for 9 parameters, and VOI analysis was conducted on the basis of a standardized brain atlas and individual cortical GM and WM segmentation. RESULTS: Prevalence of vascular risk factors among subjects (n = 106; mean age, 39.2 [SD, 12.5] years; 56 women) was similar to those in the German population. Compared with WM, we found cortical GM to have larger mean vascular calibers (5.80 [SD, 0.59] versus 4.25 [SD, 0.62] P < .001), increased blood volume fraction (20.40 [SD, 4.49] s-1 versus 11.05 [SD, 2.44] s-1; P < .001), and a dominance of venous vessels. Distinct microvascular profiles emerged for cortical GM, where vascular architecture mapping vessel type indicator differed, eg, between the thalamus and cortical GM (mean, -2.47 [SD, 4.02] s-2 versus -5.41 [SD, 2.84] s-2; P < .001). Intraclass correlation coefficient values indicated overall high test-retest reliability for vascular architecture mapping parameter mean values when comparing multiple scans per subject. CONCLUSIONS: Whole-brain vascular architecture mapping in the adult brain reveals region-specific microvascular profiles. The obtained parameter reference ranges for distinct anatomic and functional brain areas may be used for future vascular architecture mapping studies on cerebrovascular pathologies and might facilitate early discovery of microvascular changes, in, eg, neurodegeneration and neuro-oncology.

Detection of myeloma-associated osteolytic bone lesions with energy-integrating and photon-counting detector CT.

BACKGROUND: A recent innovation in computed tomography (CT) imaging has been the introduction of photon-counting detector CT (PCD-CT) systems, which are able to register the number and the energy level of incoming x­ray photons and have smaller detector elements compared with conventional CT scanners that operate with energy-integrating detectors (EID-CT). OBJECTIVES: The study aimed to evaluate the potential benefits of a novel, non-CE certified PCD-CT in detecting myeloma-associated osteolytic bone lesions (OL) compared with a state-of-the-art EID-CT. MATERIALS AND METHODS: Nine patients with multiple myeloma stage III (according to Durie and Salmon) underwent magnetic resonance imaging (MRI), EID-CT, and PCD-CT of the lower lumbar spine and pelvis. The PCD-CT and EID-CT images of all myeloma lesions that were visible in clinical MRI scans were reviewed by three radiologists for corresponding OL. Additionally, the visualization of destructions to cancellous or cortical bone, and trabecular structures, was compared between PCD-CT and EID-CT. RESULTS: Readers detected 21% more OL in PCD-CT than in EID-CT images (138 vs. 109; p < 0.0001). The sensitivity advantage of PCD-CT in lesion detection increased with decreasing lesion size. The visualization quality of cancellous and cortical destructions as well as of trabecular structures was rated higher by all three readers in PCD-CT images (mean image quality improvements for PCD-CT over EID-CT were +0.45 for cancellous and +0.13 for cortical destructions). CONCLUSIONS: For myeloma-associated OL, PCD-CT demonstrated significantly higher sensitivity, especially with small size. Visualization of bone tissue and lesions was considered significantly better in PCD-CT than in EID-CT. This implies that PCD-CT scanners could potentially be used in the early detection of myeloma-associated bone lesions.

Reproducible Radiomics Features from Multi-MRI-Scanner Test-Retest-Study: Influence on Performance and Generalizability of Models.

BACKGROUND: Radiomics models trained on data from one center typically show a decline of performance when applied to data from external centers, hindering their introduction into large-scale clinical practice. Current expert recommendations suggest to use only reproducible radiomics features isolated by multiscanner test-retest experiments, which might help to overcome the problem of limited generalizability to external data. PURPOSE: To evaluate the influence of using only a subset of robust radiomics features, defined in a prior in vivo multi-MRI-scanner test-retest-study, on the performance and generalizability of radiomics models. STUDY TYPE: Retrospective. POPULATION: Patients with monoclonal plasma cell disorders. Training set (117 MRIs from center 1); internal test set (42 MRIs from center 1); external test set (143 MRIs from center 2-8). FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T; T1-weighted turbo spin echo. ASSESSMENT: The task for the radiomics models was to predict plasma cell infiltration, determined by bone marrow biopsy, noninvasively from MRI. Radiomics machine learning models, including linear regressor, support vector regressor (SVR), and random forest regressor (RFR), were trained on data from center 1, using either all radiomics features, or using only reproducible radiomics features. Models were tested on an internal (center 1) and a multicentric external data set (center 2-8). STATISTICAL TESTS: Pearson correlation coefficient r and mean absolute error (MAE) between predicted and actual plasma cell infiltration. Fisher's z-transformation, Wilcoxon signed-rank test, Wilcoxon rank-sum test; significance level P < 0.05. RESULTS: When using only reproducible features compared with all features, the performance of the SVR on the external test set significantly improved (r = 0.43 vs. r = 0.18 and MAE = 22.6 vs. MAE = 28.2). For the RFR, the performance on the external test set deteriorated when using only reproducible instead of all radiomics features (r = 0.33 vs. r = 0.44, P = 0.29 and MAE = 21.9 vs. MAE = 20.5, P = 0.10). CONCLUSION: Using only reproducible radiomics features improves the external performance of some, but not all machine learning models, and did not automatically lead to an improvement of the external performance of the overall best radiomics model. TECHNICAL EFFICACY: Stage 2.

Potential radiation dose reduction in clinical photon-counting CT by the small pixel effect: ultra-high resolution (UHR) acquisitions reconstructed to standard resolution.

OBJECTIVE: To assess the potential dose reduction achievable with clinical photon-counting CT (PCCT) in ultra-high resolution (UHR) mode compared to acquisitions using the standard resolution detector mode (Std). MATERIALS AND METHODS: With smaller detector pixels, PCCT achieves far higher spatial resolution than energy-integrating (EI) CT systems. The reconstruction of UHR acquisitions to the lower spatial resolution of conventional systems results in an image noise and radiation dose reduction. We quantify this small pixel effect in measurements of semi-anthropomorphic abdominal phantoms of different sizes as well as in a porcine knuckle in the first clinical PCCT system by using the UHR mode (0.2 mm pixel size at isocenter) in comparison to the standard resolution mode (0.4 mm). At different slice thicknesses (0.4 up to 4 mm) and dose levels between 4 and 12 mGy, reconstructions using filtered backprojection were performed to the same target spatial resolution, i.e., same modulation transfer function, using both detector modes. Image noise and the resulting potential dose reduction was quantified as a figure of merit. RESULTS: Images acquired using the UHR mode yield lower noise in comparison to acquisitions using standard pixels at the same resolution and noise level. This holds for sharper convolution kernels at the spatial resolution limit of the standard mode, e.g., up to a factor 3.2 in noise reduction and a resulting potential dose reduction of up to almost 90%. CONCLUSION: Using sharper convolution kernels, UHR acquisitions allow for a significant dose reduction compared to acquisitions using the standard detector mode. CLINICAL RELEVANCE: Acquisitions should always be performed using the ultra-high resolution detector mode, if possible, to benefit from the intrinsic noise and dose reduction. KEY POINTS: • Ionizing radiation used in computed tomography examinations is a concern to public health. • The ultra-high resolution of novel photon-counting systems can be invested towards a noise and dose reduction if only a spatial resolution below the resolution limit of the detector is desired. • Acquisitions should always be performed in ultra-high resolution mode, if possible, to benefit from an intrinsic dose reduction.

Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.

OBJECTIVES: The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions. METHODS: In this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.87, 75.08], 48.4% male) and 47 colorectal cancer patients with 435 lesions (median age [IQR]: 65.79 [56.99, 74.62], 63.8% male) were enrolled. A pretrained nnU-Net performed automated segmentation of 1296 liver lesions. Radiomics features for each lesion were extracted using pyradiomics. The performance of several radiomics-based machine-learning classifiers was investigated for the lesions and compared to an image-based deep-learning approach using a DenseNet-121. The performance was evaluated by AUC/ROC analysis. RESULTS: The radiomics-based K-nearest neighbor classifier showed the best performance on an independent test set with AUC values of 0.87 and an accuracy of 0.67. In comparison, the image-based DenseNet-121-classifier reached an AUC of 0.80 and an accuracy of 0.83. CONCLUSIONS: CT-based radiomics and deep learning can distinguish the etiology of liver metastases from gastrointestinal primary tumors. Compared to deep learning, radiomics based models showed a varying generalizability in distinguishing liver metastases from colorectal cancer and pancreatic adenocarcinoma.

Are T2WI PI-RADS sub-scores of transition zone prostate lesions biased by DWI information? A multi-reader, single-center study.

PURPOSE: According to PI-RADS v2.1, T2-weighted imaging (T2WI) is the dominant sequence for transition zone (TZ) lesions. This study aimed to assess, whether diffusion-weighted imaging (DWI) information influences the assignment of T2WI scores. METHOD: Out of 283 prostate MRI examinations with correlated biopsy results, fourty-four patients were selected retrospectively: first, 22 patients with a TZ lesion with T2WI and DWI scores ≥ 4, to represent lesions with unequivocal suspicion on T2WI and DWI. Second, 22 additional patients with TZ lesions of similar T2WI appearance but with corresponding DWI score ≤ 3 were added as control. Four residents and one board-certified radiologist each performed two assessments of the included patients: First, only T2WI was available (T2-only read); second, both T2WI and DWI sequences were available (biparametric read). Lesion scores were assessed using Wilcoxon signed-rank test, inter-reader agreement using weighted kappa and Kendall's W statistics, and sensitivity/specificity using McNemar test. RESULTS: The T2WI scores were significantly different between the T2-only and biparametric read for 3 out of 4 residents (p ≤ 0.049) but not for the radiologist. The overall PI-RADS scores derived from the two reading sessions differed considerably for 35/220 cases (all readers pooled). Inter-reader agreement was fair for the T2WI and overall PI-RADS scores (mean kappa 0.27-0.30) and moderate for the DWI scores (mean kappa 0.43). CONCLUSIONS: For inexperienced readers, assessment of T2WI is variable and potentially biased by availability of DWI information, which can lead to changes of overall PI-RADS score and consequently clinical management. Assessment of T2WI should be performed before reviewing DWI to ensure non-biased interpretation of TZ lesions in the dominant sequence.

Periaortic adipose radiomics texture features associated with increased coronary calcium score-first results on a photon-counting-CT.

BACKGROUND: Cardiovascular diseases remain the world's primary cause of death. The identification and treatment of patients at risk of cardiovascular events thus are as important as ever. Adipose tissue is a classic risk factor for cardiovascular diseases, has been linked to systemic inflammation, and is suspected to contribute to vascular calcification. To further investigate this issue, the use of texture analysis of adipose tissue using radiomics features could prove a feasible option. METHODS: In this retrospective single-center study, 55 patients (mean age 56, 34 male, 21 female) were scanned on a first-generation photon-counting CT. On axial unenhanced images, periaortic adipose tissue surrounding the thoracic descending aorta was segmented manually. For feature extraction, patients were divided into three groups, depending on coronary artery calcification (Agatston Score 0, Agatston Score 1-99, Agatston Score ≥ 100). 106 features were extracted using pyradiomics. R statistics was used for statistical analysis, calculating mean and standard deviation with Pearson correlation coefficient for feature correlation. Random Forest classification was carried out for feature selection and Boxplots and heatmaps were used for visualization. Additionally, monovariable logistic regression predicting an Agatston Score > 0 was performed, selected features were tested for multicollinearity and a 10-fold cross-validation investigated the stability of the leading feature. RESULTS: Two higher-order radiomics features, namely "glcm_ClusterProminence" and "glcm_ClusterTendency" were found to differ between patients without coronary artery calcification and those with coronary artery calcification (Agatston Score ≥ 100) through Random Forest classification. As the leading differentiating feature "glcm_ClusterProminence" was identified. CONCLUSION: Changes in periaortic adipose tissue texture seem to correlate with coronary artery calcium score, supporting a possible influence of inflammatory or fibrotic activity in perivascular adipose tissue. Radiomics features may potentially aid as corresponding biomarkers in the future.

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively. RESULTS: A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated ( P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets. CONCLUSIONS: The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy.

Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma.

OBJECTIVE: To investigate the reproducibility of size measurements of focal bone marrow lesions (FL) in MRI in patients with monoclonal plasma cell disorders under variation of patient positioning and observer. METHODS: A data set from a prospective test-retest study was used, in which 37 patients with a total of 140 FL had undergone 2 MRI scans with identical parameters after patient repositioning. Two readers measured long and short axis diameter on the initial scan in T1 weighted, T2 weighted short tau inversion recovery and diffusion-weighted imaging sequences. The first reader additionally measured FL on the retest-scan. The Bland-Altman method was used to assess limits of agreement (LoA), and the frequencies of absolute size changes were calculated. RESULTS: In the simple test-retest experiment with one identical reader, a deviation of ≥1 mm / ≥2 mm / ≥3 mm for the long axis diameter in T1 weighted images was observed in 66% / 25% / 8% of cases. When comparing measurements of one reader on the first scan to the measurement of the other reader on the retest scan, a change of ≥1 mm / ≥3 mm / ≥5 mm for the long axis diameter in T1 weighted images was observed in 78% / 21% / 5% of cases. CONCLUSION: Small deviations in FL size are common and probably due to variation in patient positioning or inter-rater variability alone, without any actual biological change of the FL. Knowledge of the uncertainty associated with size measurements of FLs is critical for radiologists and oncologists when interpreting changes in FL size in clinical practice and in clinical trials. ADVANCES IN KNOWLEDGE: According to the MY-RADs criteria, size measurements of focal lesions in MRI are now of relevance for response assessment in patients with monoclonal plasma cell disorders.Size changes of 1 or 2 mm are frequently observed due to uncertainty of the measurement only, while the actual focal lesion has not undergone any biological change.Size changes of at least 6 mm or more in T1 weighted or T2 weighted short tau inversion recovery sequences occur in only 5% or less of cases when the focal lesion has not undergone any biological change.

Influence of heart rate and heart rate variability on the feasibility of ultra-fast, high-pitch coronary photon-counting computed tomography angiography.

Coronary computed tomography angiography has become a mainstay in diagnosing coronary artery disease and is increasingly used in screening symptomatic patients. Recently, photon-counting computed tomography (PCCT) has been introduced into clinical practice, offering higher spatial and temporal resolution. As the applied radiation dose is highly dependent on the choice of scan mode and is lowest using the ultra-fast high-pitch (FLASH) mode, guidelines for their application are needed. From a retrospective study investigating the properties of a novel photon-counting computed tomography, all patients who underwent FLASH-mode PCCT angiography were selected between January and April 2022. This resulted in a study population of 46 men and 27 women. We recorded pre- and intrascan ECG readings and calculated heart rate (maximum heart rate 73 bpm) as well heart rate variability (maximum HRV 37 bpm) as measured by the standard deviation of the heart rate. Diagnostic quality and motion artifacts scores were recorded for each coronary artery segment by consensus between two readers. We found a highly significant association between heart rate variability and image quality (p < 0.001). The heart rate itself was not independently associated with image quality. Both heart rate and heart rate variability were significantly associated with the presence of motion artifacts in a combined model. Scan heart rate variability-but not heart rate itself-is a highly significant predictor of reduced image quality on high-pitch coronary photon-counting computed tomography angiography. This may be due to better scanner architecture and an increased temporal resolution compared to conventional energy-integrating detector computed tomography, which has to be addressed in a comparison study in the future.

Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT.

OBJECTIVES: Radiomics image data analysis offers promising approaches in research but has not been implemented in clinical practice yet, partly due to the instability of many parameters. The aim of this study is to evaluate the stability of radiomics analysis on phantom scans with photon-counting detector CT (PCCT). METHODS: Photon-counting CT scans of organic phantoms consisting of 4 apples, kiwis, limes, and onions each were performed at 10 mAs, 50 mAs, and 100 mAs with 120-kV tube current. The phantoms were segmented semi-automatically and original radiomics parameters were extracted. This was followed by statistical analysis including concordance correlation coefficients (CCC), intraclass correlation coefficients (ICC), as well as random forest (RF) analysis, and cluster analysis to determine the stable and important parameters. RESULTS: Seventy-three of the 104 (70%) extracted features showed excellent stability with a CCC value > 0.9 when compared in a test and retest analysis, and 68 features (65.4%) were stable compared to the original in a rescan after repositioning. Between the test scans with different mAs values, 78 (75%) features were rated with excellent stability. Eight radiomics features were identified that had an ICC value greater than 0.75 in at least 3 of 4 groups when comparing the different phantoms in a phantom group. In addition, the RF analysis identified many features that are important for distinguishing the phantom groups. CONCLUSION: Radiomics analysis using PCCT data provides high feature stability on organic phantoms, which may facilitate the implementation of radiomics analysis likewise in clinical routine. KEY POINTS: • Radiomics analysis using photon-counting computed tomography provides high feature stability. • Photon-counting computed tomography may pave the way for implementation of radiomics analysis in clinical routine.

In Vivo Repeatability and Multiscanner Reproducibility of MRI Radiomics Features in Patients With Monoclonal Plasma Cell Disorders: A Prospective Bi-institutional Study.

OBJECTIVES: Despite the extensive number of publications in the field of radiomics, radiomics algorithms barely enter large-scale clinical application. Supposedly, the low external generalizability of radiomics models is one of the main reasons, which hinders the translation from research to clinical application. The objectives of this study were to investigate reproducibility of radiomics features (RFs) in vivo under variation of patient positioning, magnetic resonance imaging (MRI) sequence, and MRI scanners, and to identify a subgroup of RFs that shows acceptable reproducibility across all different acquisition scenarios. MATERIALS AND METHODS: Between November 30, 2020 and February 16, 2021, 55 patients with monoclonal plasma cell disorders were included in this prospective, bi-institutional, single-vendor study. Participants underwent one reference scan at a 1.5 T MRI scanner and several retest scans: once after simple repositioning, once with a second MRI protocol, once at another 1.5 T scanner, and once at a 3 T scanner. Radiomics feature from the bone marrow of the left hip bone were extracted, both from original scans and after different image normalizations. Intraclass correlation coefficient (ICC) was used to assess RF repeatability and reproducibility. RESULTS: Fifty-five participants (mean age, 59 ± 7 years; 36 men) were enrolled. For T1-weighted images after muscle normalization, in the simple test-retest experiment, 110 (37%) of 295 RFs showed an ICC ≥0.8: 54 (61%) of 89 first-order features (FOFs), 35 (95%) of 37 volume and shape features, and 21 (12%) of 169 texture features (TFs). When the retest was performed with different technical settings, even after muscle normalization, the number of FOF/TF with an ICC ≥0.8 declined to 58/13 for the second protocol, 29/7 for the second 1.5 T scanner, and 49/7 for the 3 T scanner, respectively. Twenty-five (28%) of the 89 FOFs and 6 (4%) of the 169 TFs from muscle-normalized T1-weighted images showed an ICC ≥0.8 throughout all repeatability and reproducibility experiments. CONCLUSIONS: In vivo, only few RFs are reproducible with different MRI sequences or different MRI scanners, even after application of a simple image normalization. Radiomics features selected by a repeatability experiment only are not necessarily suited to build radiomics models for multicenter clinical application. This study isolated a subset of RFs, which are robust to variations in MRI acquisition observed in scanners from 1 vendor, and therefore are candidates to build reproducible radiomics models for monoclonal plasma cell disorders for multicentric applications, at least when centers are equipped with scanners from this vendor.

Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.

OBJECTIVES: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone. MATERIALS AND METHODS: In this retrospective multicentric study, 180 MRIs from 54 patients were annotated (semi)manually and used to train an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. The quality of the automatic segmentation was evaluated on 15 manually segmented whole-body MRIs from 3 centers using the dice score. In 3 independent test sets from 3 centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation and manual ADC measurements from 2 independent radiologists was evaluated. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed MM who had undergone bone marrow biopsy, ADC measurements were correlated with biopsy results using Spearman correlation. RESULTS: The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, whereas the interrater experiment gave mean dice scores of 0.86, 0.86, and 0.77, respectively. The agreement between radiologists' manual ADC measurements and automatic ADC measurements was as follows: the bias between the first reader and the automatic approach was 49 × 10 -6 mm 2 /s, 7 × 10 -6 mm 2 /s, and -58 × 10 -6 mm 2 /s, and the bias between the second reader and the automatic approach was 12 × 10 -6 mm 2 /s, 2 × 10 -6 mm 2 /s, and -66 × 10 -6 mm 2 /s for the right pelvis, the left pelvis, and the sacral bone, respectively. The bias between reader 1 and reader 2 was 40 × 10 -6 mm 2 /s, 8 × 10 -6 mm 2 /s, and 7 × 10 -6 mm 2 /s, and the mean absolute difference between manual readers was 84 × 10 -6 mm 2 /s, 65 × 10 -6 mm 2 /s, and 75 × 10 -6 mm 2 /s. Automatically extracted ADC values significantly correlated with bone marrow plasma cell infiltration ( R = 0.36, P = 0.007). CONCLUSIONS: In this study, a nnU-Net was trained that can automatically segment pelvic bone marrow from whole-body ADC maps in multicentric data sets with a quality comparable to manual segmentations. This approach allows automatic, objective bone marrow ADC measurements, which agree well with manual ADC measurements and can help to overcome interrater variability or nonrepresentative measurements. Automatically extracted ADC values significantly correlate with bone marrow plasma cell infiltration and might be of value for automatic staging, risk stratification, or therapy response assessment.

Metal artifacts and artifact reduction of neurovascular coils in photon-counting detector CT versus energy-integrating detector CT - in vitro comparison of a standard brain imaging protocol.

OBJECTIVES: Photon-counting detector computed tomography (PCD-CT) is a promising new technique for CT imaging. The aim of the present study was the in vitro comparison of coil-related artifacts in PCD-CT and conventional energy-integrating detector CT (EID-CT) using a comparable standard brain imaging protocol before and after metal artifact reduction (MAR). METHODS: A nidus-shaped rubber latex, resembling an aneurysm of the cerebral arteries, was filled with neurovascular platinum coils and inserted into a brain imaging phantom. Image acquisition and reconstruction were repeatedly performed for PCD-CT and EID-CT (n = 10, respectively) using a standard brain imaging protocol. Moreover, linear interpolation MAR was performed for PCD-CT and EID-CT images. The degree of artifacts was analyzed quantitatively (standard deviation in a donut-shaped region of interest) and qualitatively (5-point scale analysis). RESULTS: Quantitative and qualitative analysis demonstrated a lower degree of metal artifacts in the EID-CT images compared to the total-energy PCD-CT images (e.g., 82.99 ± 7.89 Hounsfield units (HU) versus 90.35 ± 6.28 HU; p < 0.001) with no qualitative difference between the high-energy bin PCD-CT images and the EID-CT images (4.18 ± 0.37 and 3.70 ± 0.64; p = 0.575). After MAR, artifacts were more profoundly reduced in the PCD-CT images compared to the EID-CT images in both analyses (e.g., 2.35 ± 0.43 and 3.18 ± 0.34; p < 0.001). CONCLUSION: PCD-CT in combination with MAR have the potential to provide an improved option for reduction of coil-related artifacts in cerebral imaging in this in vitro study. KEY POINTS: • Photon-counting detector CT produces more artifacts compared to energy-integrating detector CT without metal artifact reduction in cerebral in vitro imaging after neurovascular coil-embolization. • Spectral information of PCD-CT provides the potential for new post-processing techniques, since the coil-related artifacts were lower in PCD-CT images compared to EID-CT images after linear interpolation metal artifact reduction in this in vitro study.

Ultrahigh resolution whole body photon counting computed tomography as a novel versatile tool for translational research from mouse to man.

X-ray computed tomography (CT) is a cardinal tool in clinical practice. It provides cross-sectional images within seconds. The recent introduction of clinical photon-counting CT allowed for an increase in spatial resolution by more than a factor of two resulting in a pixel size in the center of rotation of about 150 µm. This level of spatial resolution is in the order of dedicated preclinical micro-CT systems. However so far, the need for different dedicated clinical and preclinical systems often hinders the rapid translation of early research results to applications in men. This drawback might be overcome by ultra-high resolution (UHR) clinical photon-counting CT unifying preclinical and clinical research capabilities in a single machine. Herein, the prototype of a clinical UHR PCD CT (SOMATOM CounT, Siemens Healthineers, Forchheim, Germany) was used. The system comprises a conventional energy-integrating detector (EID) and a novel photon-counting detector (PCD). While the EID provides a pixel size of 0.6 mm in the centre of rotation, the PCD provides a pixel size of 0.25 mm. Additionally, it provides a quantification of photon energies by sorting them into up to four distinct energy bins. This acquisition of multi-energy data allows for a multitude of applications, e.g. pseudo-monochromatic imaging. In particular, we examine the relation between spatial resolution, image noise and administered radiation dose for a multitude of use-cases. These cases include ultra-high resolution and multi-energy acquisitions of mice administered with a prototype bismuth-based contrast agent (nanoPET Pharma, Berlin, Germany) as well as larger animals and actual patients. The clinical EID provides a spatial resolution of about 9 lp/cm (modulation transfer function at 10%, MTF10%) while UHR allows for the acquisition of images with up to 16 lp/cm allowing for the visualization of all relevant anatomical structures in preclinical and clinical specimen. The spectral capabilities of the system enable a variety of applications previously not available in preclinical research such as pseudo-monochromatic images. Clinical ultra-high resolution photon-counting CT has the potential to unify preclinical and clinical research on a single system enabling versatile imaging of specimens and individuals ranging from mice to man.

Computed tomography hypoperfusion-hypodensity mismatch vs. automated perfusion mismatch to identify stroke patients eligible for thrombolysis.

Background and purpose: Automated perfusion imaging can detect stroke patients with unknown time of symptom onset who are eligible for thrombolysis. However, the availability of this technique is limited. We, therefore, established the novel concept of computed tomography (CT) hypoperfusion-hypodensity mismatch, i.e., an ischemic core lesion visible on cerebral perfusion CT without visible hypodensity in the corresponding native cerebral CT. We compared both methods regarding their accuracy in identifying patients suitable for thrombolysis. Methods: In a retrospective analysis of the MissPerfeCT observational cohort study, patients were classified as suitable or not for thrombolysis based on established time window and imaging criteria. We calculated predictive values for hypoperfusion-hypodensity mismatch and automated perfusion imaging to compare accuracy in the identification of patients suitable for thrombolysis. Results: Of 247 patients, 219 (88.7%) were eligible for thrombolysis and 28 (11.3%) were not eligible for thrombolysis. Of 197 patients who were within 4.5 h of symptom onset, 190 (96.4%) were identified by hypoperfusion-hypodensity mismatch and 88 (44.7%) by automated perfusion mismatch (p < 0.001). Of 22 patients who were beyond 4.5 h of symptom onset but were eligible for thrombolysis, 5 patients (22.7%) were identified by hypoperfusion-hypodensity mismatch. Predictive values for the hypoperfusion-hypodensity mismatch vs. automated perfusion mismatch were as follows: sensitivity, 89.0% vs. 50.2%; specificity, 71.4% vs. 100.0%; positive predictive value, 96.1% vs. 100.0%; and negative predictive value, 45.5% vs. 20.4%. Conclusion: The novel method of hypoperfusion-hypodensity mismatch can identify patients suitable for thrombolysis with higher sensitivity and lower specificity than established techniques. Using this simple method might therefore increase the proportion of patients treated with thrombolysis without the use of special automated software.The MissPerfeCT study is a retrospective observational multicenter cohort study and is registered with clinicaltrials.gov (NCT04277728).

Influence of local aortic calcification on periaortic adipose tissue radiomics texture features-a primary analysis on PCCT.

Perivascular adipose tissue is known to be metabolically active. Volume and density of periaortic adipose tissue are associated with aortic calcification as well as aortic diameter indicating a possible influence of periaortic adipose tissue on the development of aortic calcification. Due to better spatial resolution and signal-to-noise ratio, new CT technologies such as photon-counting computed tomography may allow the detection of texture alterations of periaortic adipose tissue depending on the existence of local aortic calcification possibly outlining a biomarker for the development of arteriosclerosis. In this retrospective, single-center, IRB-approved study, periaortic adipose tissue was segmented semiautomatically and radiomics features were extracted using pyradiomics. Statistical analysis was performed in R statistics calculating mean and standard deviation with Pearson correlation coefficient for feature correlation. For feature selection Random Forest classification was performed. A two-tailed unpaired t test was applied to the final feature set. Results were visualized as boxplots and heatmaps. A total of 30 patients (66.6% female, median age 57 years) were enrolled in this study. Patients were divided into two subgroups depending on the presence of local aortic calcification. By Random Forest feature selection a set of seven higher-order features could be defined to discriminate periaortic adipose tissue texture between these two groups. The t test showed a statistic significant discrimination for all features (p < 0.05). Texture changes of periaortic adipose tissue associated with the existence of local aortic calcification may lay the foundation for finding a biomarker for development of arteriosclerosis.

Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions.

Feature stability and standardization remain challenges that impede the clinical implementation of radiomics. This study investigates the potential of spectral reconstructions from photon-counting computed tomography (PCCT) regarding organ-specific radiomics feature stability. Abdominal portal-venous phase PCCT scans of 10 patients in virtual monoenergetic (VM) (keV 40-120 in steps of 10), polyenergetic, virtual non-contrast (VNC), and iodine maps were acquired. Two 2D and 3D segmentations measuring 1 and 2 cm in diameter of the liver, lung, spleen, psoas muscle, subcutaneous fat, and air were obtained for spectral reconstructions. Radiomics features were extracted with pyradiomics. The calculation of feature-specific intraclass correlation coefficients (ICC) was performed by comparing all segmentation approaches and organs. Feature-wise and organ-wise correlations were evaluated. Segmentation-resegmentation stability was evaluated by concordance correlation coefficient (CCC). Compared to non-VM, VM-reconstruction features tended to be more stable. For VM reconstructions, 3D 2 cm segmentation showed the highest average ICC with 0.63. Based on a criterion of ≥ 3 stable organs and an ICC of ≥ 0.75, 12-mainly non-first-order features-are shown to be stable between the VM reconstructions. In a segmentation-resegmentation analysis in 3D 2 cm, three features were identified as stable based on a CCC of > 0.6 in ≥ 3 organs in ≥ 6 VM reconstructions. Certain radiomics features vary between monoenergetic reconstructions and depend on the ROI size. Feature stability was also shown to differ between different organs. Yet, glcm_JointEntropy, gldm_GrayLevelNonUniformity, and firstorder_Entropy could be identified as features that could be interpreted as energy-independent and segmentation-resegmentation stable in this PCCT collective. PCCT may support radiomics feature standardization and comparability between sites.

Simplified Assessment of Lesion Water Uptake for Identification of Patients within 4.5 Hours of Stroke Onset: An Analysis of the MissPerfeCT Study.

BACKGROUND AND PURPOSE: Many patients with stroke cannot receive intravenous thrombolysis because the time of symptom onset is unknown. We tested whether a simple method of computed tomography (CT)-based quantification of water uptake in the ischemic tissue can identify patients with stroke onset within 4.5 hours. METHODS: This retrospective analysis of the MissPerfeCT study (August 2009 to November 2017) includes consecutive patients with known onset of symptoms from seven tertiary stroke centers. We developed a simplified algorithm based on region of interest (ROI) measurements to quantify water uptake of the ischemic lesion and thereby quantify time of symptom onset within and beyond 4.5 hours. Perfusion CT was used to identify ischemic brain tissue, and its density was measured in non-contrast CT and related to the density of the corresponding area of the contralateral hemisphere to quantify lesion water uptake. RESULTS: Of 263 patients, 204 (77.6%) had CT within 4.5 hours. Water uptake was significantly lower in patients with stroke onset within (6.7%; 95% confidence interval [CI], 6.0% to 7.4%) compared to beyond 4.5 hours (12.7%; 95% CI, 10.7% to 14.7%). The area under the curve for distinguishing these patient groups according to percentage water uptake was 0.744 with an optimal cut-off value of 9.5%. According to this cut-off the positive predictive value was 88.8%, sensitivity was 73.5%, specificity 67.8%, negative predictive value was 42.6%. CONCLUSIONS: Ischemic stroke patients with unknown time of symptom onset can be identified as being within a timeframe of 4.5 hours using a ROI-based method to assess water uptake on admission non-contrast head CT.