Extended nnU-Net for Brain Metastasis Detection and Segmentation in Contrast-Enhanced Magnetic Resonance Imaging With a Large Multi-Institutional Data Set.

PURPOSE: The purpose of this study was to investigate an extended self-adapting nnU-Net framework for detecting and segmenting brain metastases (BM) on magnetic resonance imaging (MRI). METHODS AND MATERIALS: Six different nnU-Net systems with adaptive data sampling, adaptive Dice loss, or different patch/batch sizes were trained and tested for detecting and segmenting intraparenchymal BM with a size ≥2 mm on 3 Dimensional (3D) post-Gd T1-weighted MRI volumes using 2092 patients from 7 institutions (1712, 195, and 185 patients for training, validation, and testing, respectively). Gross tumor volumes of BM delineated by physicians for stereotactic radiosurgery were collected retrospectively and curated at each institute. Additional centralized data curation was carried out to create gross tumor volumes of uncontoured BM by 2 radiologists to improve the accuracy of ground truth. The training data set was augmented with synthetic BMs of 1025 MRI volumes using a 3D generative pipeline. BM detection was evaluated by lesion-level sensitivity and false-positive (FP) rate. BM segmentation was assessed by lesion-level Dice similarity coefficient, 95-percentile Hausdorff distance, and average Hausdorff distance (HD). The performances were assessed across different BM sizes. Additional testing was performed using a second data set of 206 patients. RESULTS: Of the 6 nnU-Net systems, the nnU-Net with adaptive Dice loss achieved the best detection and segmentation performance on the first testing data set. At an FP rate of 0.65 ± 1.17, overall sensitivity was 0.904 for all sizes of BM, 0.966 for BM ≥0.1 cm3, and 0.824 for BM <0.1 cm3. Mean values of Dice similarity coefficient, 95-percentile Hausdorff distance, and average HD of all detected BMs were 0.758, 1.45, and 0.23 mm, respectively. Performances on the second testing data set achieved a sensitivity of 0.907 at an FP rate of 0.57 ± 0.85 for all BM sizes, and an average HD of 0.33 mm for all detected BM. CONCLUSIONS: Our proposed extension of the self-configuring nnU-Net framework substantially improved small BM detection sensitivity while maintaining a controlled FP rate. Clinical utility of the extended nnU-Net model for assisting early BM detection and stereotactic radiosurgery planning will be investigated.

Co-ordinate-based positional embedding that captures resolution to enhance transformer's performance in medical image analysis.

Vision transformers (ViTs) have revolutionized computer vision by employing self-attention instead of convolutional neural networks and demonstrated success due to their ability to capture global dependencies and remove spatial biases of locality. In medical imaging, where input data may differ in size and resolution, existing architectures require resampling or resizing during pre-processing, leading to potential spatial resolution loss and information degradation. This study proposes a co-ordinate-based embedding that encodes the geometry of medical images, capturing physical co-ordinate and resolution information without the need for resampling or resizing. The effectiveness of the proposed embedding is demonstrated through experiments with UNETR and SwinUNETR models for infarct segmentation on MRI dataset with AxTrace and AxADC contrasts. The dataset consists of 1142 training, 133 validation and 143 test subjects. Both models with the addition of co-ordinate based positional embedding achieved substantial improvements in mean Dice score by 6.5% and 7.6%. The proposed embedding showcased a statistically significant advantage p-value< 0.0001 over alternative approaches. In conclusion, the proposed co-ordinate-based pixel-wise positional embedding method offers a promising solution for Transformer-based models in medical image analysis. It effectively leverages physical co-ordinate information to enhance performance without compromising spatial resolution and provides a foundation for future advancements in positional embedding techniques for medical applications.

Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans.

Purpose: To present a method that automatically detects, subtypes, and locates acute or subacute intracranial hemorrhage (ICH) on noncontrast CT (NCCT) head scans; generates detection confidence scores to identify high-confidence data subsets with higher accuracy; and improves radiology worklist prioritization. Such scores may enable clinicians to better use artificial intelligence (AI) tools. Materials and Methods: This retrospective study included 46 057 studies from seven "internal" centers for development (training, architecture selection, hyperparameter tuning, and operating-point calibration; n = 25 946) and evaluation (n = 2947) and three "external" centers for calibration (n = 400) and evaluation (n = 16 764). Internal centers contributed developmental data, whereas external centers did not. Deep neural networks predicted the presence of ICH and subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and/or epidural hemorrhage) and segmentations per case. Two ICH confidence scores are discussed: a calibrated classifier entropy score and a Dempster-Shafer score. Evaluation was completed by using receiver operating characteristic curve analysis and report turnaround time (RTAT) modeling on the evaluation set and on confidence score-defined subsets using bootstrapping. Results: The areas under the receiver operating characteristic curve for ICH were 0.97 (0.97, 0.98) and 0.95 (0.94, 0.95) on internal and external center data, respectively. On 80% of the data stratified by calibrated classifier and Dempster-Shafer scores, the system improved the Youden indexes, increasing them from 0.84 to 0.93 (calibrated classifier) and from 0.84 to 0.92 (Dempster-Shafer) for internal centers and increasing them from 0.78 to 0.88 (calibrated classifier) and from 0.78 to 0.89 (Dempster-Shafer) for external centers (P < .001). Models estimated shorter RTAT for AI-prioritized worklists with confidence measures than for AI-prioritized worklists without confidence measures, shortening RTAT by 27% (calibrated classifier) and 27% (Dempster-Shafer) for internal centers and shortening RTAT by 25% (calibrated classifier) and 27% (Dempster-Shafer) for external centers (P < .001). Conclusion: AI that provided statistical confidence measures for ICH detection on NCCT scans reliably detected and subtyped hemorrhages, identified high-confidence predictions, and improved worklist prioritization in simulation.Keywords: CT, Head/Neck, Hemorrhage, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images.

Purpose: We investigate the impact of various deep-learning-based methods for detecting and segmenting metastases with different lesion volume sizes on 3D brain MR images. Approach: A 2.5D U-Net and a 3D U-Net were selected. We also evaluated weak learner fusion of the prediction features generated by the 2.5D and the 3D networks. A 3D fully convolutional one-stage (FCOS) detector was selected as a representative of bounding-box regression-based detection methods. A total of 422 3D post-contrast T1-weighted scans from patients with brain metastases were used. Performances were analyzed based on lesion volume, total metastatic volume per patient, and number of lesions per patient. Results: The performance of detection of the 2.5D and 3D U-Net methods had recall of > 0.83 and precision of > 0.44 for lesion volume > 0.3 cm 3 but deteriorated as metastasis size decreased below 0.3 cm 3 to 0.58 to 0.74 in recall and 0.16 to 0.25 in precision. Compared the two U-Nets for detection capability, high precision was achieved by the 2.5D network, but high recall was achieved by the 3D network for all lesion sizes. The weak learner fusion achieved a balanced performance between the 2.5D and 3D U-Nets; particularly, it increased precision to 0.83 for lesion volumes of 0.1 to 0.3 cm 3 but decreased recall to 0.59. The 3D FCOS detector did not outperform the U-Net methods in detecting either the small or large metastases presumably because of the limited data size. Conclusions: Our study provides the performances of four deep learning methods in relationship to lesion size, total metastasis volume, and number of lesions per patient, providing insight into further development of the deep learning networks.

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies.

Can cerebellar and brainstem apparent diffusion coefficient (ADC) values predict neuromotor outcome in term neonates with hypoxic-ischemic encephalopathy (HIE) treated with hypothermia?

BACKGROUND AND PURPOSE: To determine the apparent diffusion coefficient (ADC) in specific infratentorial brain structures during the first week of life and its relation with neuromotor outcome for Hypoxic-ischemic encephalopathy (HIE) in term neonates with and without whole-body hypothermia (TH). MATERIALS AND METHODS: We retrospectively evaluated 45 MRI studies performed in the first week of life of term neonates born between 2010 and 2013 at Boston Children's Hospital. Selected cases were classified into three groups: 1) HIE neonates who underwent TH, 2) HIE normothermics (TN), and 3) controls. The neuromotor outcome was categorized as normal, abnormal and death. The ADCmean was calculated for six infratentorial brain regions. RESULTS: A total of 45 infants were included: 28 HIE TH treated, 8 HIE TN, and 9 controls. The mean gestational age was 39 weeks; 57.8% were male; 11.1% were non-survivors. The median age at MRI was 3 days (interquartile range, 1-4 days). A statistically significant relationship was shown between motor outcome or death and the ADCmean in the vermis (P = 0.002), cerebellar left hemisphere (P = 0.002), midbrain (P = 0.009), pons (P = 0.014) and medulla (P = 0.005). In patients treated with TH, the ADC mean remained significantly lower than that in the controls only in the hemispheres (P = 0.01). In comparison with abnormal motor outcome, ADCmean was lowest in the left hemisphere (P = 0.003), vermis (P = 0.003), pons (P = 0.0036) and medulla (P = 0.008) in case of death. CONCLUSION: ADCmean values during the first week of life in the left hemisphere, vermis, pons and medulla are related to motor outcome or death in infants with HIE either with or without hypothermic therapy. Therefore, this objective tool can be assessed prospectively to determine if it can be used to establish prognosis in the first week of life, particularly in severe cases of HIE.

High-angular resolution diffusion imaging tractography of cerebellar pathways from newborns to young adults.

INTRODUCTION: Many neurologic and psychiatric disorders are thought to be due to, or result in, developmental errors in neuronal cerebellar connectivity. In this connectivity analysis, we studied the developmental time-course of cerebellar peduncle pathways in pediatric and young adult subjects. METHODS: A cohort of 80 subjects, newborns to young adults, was studied on a 3T MR system with 30 diffusion-weighted measurements with high-angular resolution diffusion imaging (HARDI) tractography. RESULTS: Qualitative and quantitative results were analyzed for age-based variation. In subjects of all ages, the superior cerebellar peduncle pathway (SCP) and two distinct subpathways of the middle cerebellar peduncle (MCP), as described in previous ex vivo studies, were identified in vivo with this technique: pathways between the rostral pons and inferior-lateral cerebellum (MCP cog), associated predominantly with higher cognitive function, and pathways between the caudal pons and superior-medial cerebellum (MCP mot), associated predominantly with motor function. DISCUSSION: Our findings showed that the inferior cerebellar peduncle pathway (ICP), involved primarily in proprioception and balance appears to have a later onset followed by more rapid development than that exhibited in other tracts. We hope that this study may provide an initial point of reference for future studies of normal and pathologic development of cerebellar connectivity.

Single-Breath Washout Tests to Assess Small Airway Disease in COPD.

BACKGROUND: Current functional assessments do not allow a reliable assessment of small airways, which are a major site of disease in COPD. Single-breath washout (SBW) tests are feasible and reproducible methods for evaluating small airway disease. Their relevance in COPD remains unknown. METHODS: We performed a cross-sectional study in 65 patients with moderate to severe COPD. Phase III slope of nitrogen (SIIIN2) and double tracer gas (SIIIDTG) SBW tests were used as a measure of ventilation inhomogeneity. The association of both markers with established physiological and clinical features of COPD was assessed. RESULTS: Ventilation inhomogeneity as measured by SIIIN2 and SIIIDTG was increased in patients with COPD compared with healthy subjects (P < .001 and P < .001, respectively). SIIIN2 was associated with FEV1 predicted, residual volume (RV)/total lung capacity (TLC) and diffusing capacity of the lung for carbon monoxide (Dlco) (all P < .001). Furthermore, SIIIN2 was related to dyspnea, exercise-induced desaturation, and exercise capacity (P = .001, P < .001, and P = .047, respectively). SIIIDTG was associated with TLC, Dlco, and cough (P < .001, P = .001, and P = .009, respectively). In multivariate regression models, we demonstrated that these associations are largely independent of FEV1 and mostly stronger than associations with FEV1. In contrast, FEV1 was superior in predicting emphysema severity. CONCLUSIONS: SIIIN2 and SIIIDTG, two fast and clinically applicable measures of small airway disease, reflect different physiological and clinical aspects of COPD, largely independent of spirometry. TRIAL REGISTRY: ISRCTN99586989, Ethics committee Beider Basel (approval number 295/07).

Magnetic Resonance Fiber Tracking in a Neonate with Hemimegalencephaly.

A magnetic resonance diffusion fiber tracking study in neonate diagnosed with left hemisphere hemimegalencephaly is presented. Despite diffuse morphologic deformities identified in conventional imaging, all major pathways were identifiable bilaterally with minor aberrations in vicinity of morphologic lesions.

Prenatal magnetic resonance imaging detection of temporal lobes and hippocampal anomalies in hypochondroplasia.

Hypochondroplasia (HCH) is a genetic skeletal dysplasia, inherited in an autosomal dominant fashion. About 50-70% of HCH patients have a mutation in FGFR3 gene and in the majority of cases it is a de novo mutation. Recent magnetic resonance imaging studies on relative large cohorts of HCH patients have showed a central nervous system involvement with a high incidence of characteristic temporal lobe and hippocampal abnormalities. To the best of our knowledge, this report shows the first magnetic resonance imaging prenatal detection of characteristic brain anomalies in a case of HCH, molecularly confirmed through postnatal FGFR3 analysis.

Autoimmune pancreatitis: multidetector-row computed tomography (MDCT) and magnetic resonance (MR) findings in the Italian experience.

Multidetector-row computed tomography (MDCT) and magnetic resonance (MR) imaging are currently the most frequently performed imaging modalities for the study of pancreatic disease. In cases of suspected autoimmune pancreatitis (AIP), a dynamic quadriphasic (precontrast, contrast-enhanced pancreatic, venous and late phases) study is recommended in both techniques. In the diffuse form of autoimmune pancreatitis (DAIP), the pancreatic parenchyma shows diffuse enlargement and appears, during the MDCT and MR contrast-enhanced pancreatic phase, diffusely hypodense and hypointense, respectively, compared to the spleen because of lymphoplasmacytic infiltration and pancreatic fibrosis. During the venous phase of MDCT and MR imaging, the parenchyma appears hyperdense and hyperintense, respectively, in comparison to the pancreatic phase. In the delayed phase of both imaging modalities, it shows retention of contrast media. A "capsule-like rim" may be recognised as a peripancreatic MDCT hyperdense and MR hypointense halo in the T2-weighted images, compared to the parenchyma. DAIP must be differentiated from non-necrotizing acute pancreatitis (NNAP) and lymphoma since both diseases show diffuse enlargement of the pancreatic parenchyma. The differential diagnosis is clinically difficult, and dynamic contrast-enhanced MDCT has an important role. In the focal form of autoimmune pancreatitis (FAIP), the parenchyma shows segmental enlargement involving the head, the body-tail or the tail, with the same contrast pattern as the diffuse form on both modalities. FAIP needs to be differentiated from pancreatic adenocarcinoma to avoid unnecessary surgical procedures, since both diseases have similar clinical and imaging presentation. The differential diagnosis is clinically difficult, and dynamic contrast-enhanced MDCT and MR imaging both have an important role. MR cholangiopancreatography helps in the differential diagnosis. Furthermore, MDCT and MR imaging can identify the extrapancreatic manifestations of AIP, most commonly biliary, renal and retroperitoneal. Finally, in all cases of uncertain diagnosis, MDCT and/or MR follow-up after short-term treatment (2-3 weeks) with high-dose steroids can identify a significant reduction in size of the pancreatic parenchyma and, in FAIP, normalisation of the calibre of the upstream main pancreatic duct.

Magnetic resonance neurography of peroneal palsy in crossed-legs habit: case report.

"Drop foot" palsy attributed to the prolonged and repetitive maintenance of the crossed-leg posture has been occasionally reported. We report, to the best of our knowledge, the first case of magnetic resonance imaging evidence of peroneal nerve abnormalities related to right drop-foot palsy in a tall healthy subject with habit of prolonged daily leg crossing.

Enhancing pancreatic adenocarcinoma delineation in diffusion derived intravoxel incoherent motion f-maps through automatic vessel and duct segmentation.

Diffusion-based intravoxel incoherent motion imaging has recently gained interest as a method to detect and characterize pancreatic lesions, especially as it could provide a radiation- and contrast agent-free alternative to existing diagnostic methods. However, tumor delineation on intravoxel incoherent motion-derived parameter maps is impeded by poor lesion-to-pancreatic duct contrast in the f-maps and poor lesion-to-vessel contrast in the D-maps. The distribution of the diffusion and perfusion parameters within vessels, ducts, and tumors were extracted from a group of 42 patients with pancreatic adenocarcinoma. Clearly separable combinations of f and D were observed, and receiver operating characteristic analysis was used to determine the optimal cutoff values for an automated segmentation of vessels and ducts to improve lesion detection and delineation on the individual intravoxel incoherent motion-derived maps. Receiver operating characteristic analysis identified f = 0.28 as the cutoff for vessels (Area under the curve (AUC) = 0.901) versus tumor/duct and D = 1.85 µm(2) /ms for separating duct from tumor tissue (AUC = 0.988). These values were incorporated in an automatic segmentation algorithm and then applied to 42 patients. This yielded clearly improved tumor delineation compared to individual intravoxel incoherent motion-derived maps. Furthermore, previous findings that indicated that the f value in pancreatic cancer is strongly reduced compared to healthy pancreatic tissue were reconfirmed.

Intravoxel incoherent motion MRI for the differentiation between mass forming chronic pancreatitis and pancreatic carcinoma.

PURPOSE: To determine which of the quantitative parameters obtained from intravoxel incoherent motion diffusion weighted imaging (DWI) is the most significant for the differentiation between pancreatic carcinoma and mass-forming chronic pancreatitis. MATERIALS AND METHODS: Twenty-nine patients with pancreatic masses were included, 9 proved to have a mass-forming pancreatitis and 20 had a pancreatic carcinoma. The patients were studied using intravoxel incoherent motion DWI with 11 b-values and the apparent diffusion coefficient (ADC), the true diffusion constant (D) and the perfusion fraction (f) were calculated. The diagnostic strength of the parameters was evaluated using receiver operating characteristic analysis. RESULTS: The ADC in chronic pancreatitis was higher than in pancreatic carcinoma with significant differences at b = 50, 75, 100, 150, 200, 300 s/mm (ADC50 = 3.17 ± 0.67 vs. 2.55 ± 1.09, ADC75 = 2.46 ± 0.4 vs. 1.93 ± 0.52, ADC100 = 2.28 ± 0.48 vs. 1.73 ± 0.45, ADC150 = 1.97 ± 0.26 vs. 1.63 ± 0.40, ADC200 = 1.98 ± 0.24 vs. 1.53 ± 0.28, and ADC300 = 1.76 ± 0.19 vs. 1.46 ± 0.31 × 10(-3) mm2/s). No significant differences were found at b = 25, 400, 600, and 800 s/mm (ADC25 = 4.69 ± 0.65 vs. 4.04 ± 1.35, ADC400 = 1.57 ± 0.21 vs. 1.37 ± 0.30, ADC600 = 1.38 ± 0.18 vs. 1.24 ± 0.25, and ADC800 = 1.27 ± 0.10 vs. 1.18 ± 0.19 × 10(-3) mm2/s) nor using ADCtot (1.42 ± 0.23 vs. 1.28 ± 0.12 × 10(-3) mm2/s). The perfusion fraction f was significantly higher in pancreatitis compared with pancreatic carcinoma (16.3% ± 5.30% vs. 8.2% ± 4.00%, P = 0.0001). There was no significant difference between groups for D (1.07 ± 0.224 × 10(-3) mm2/s for chronic pancreatitis and 1.09 ± 0.3 × 10(-3) mm2/s for pancreatic carcinoma, P = 0.66). For f, the highest area under the curve (0.894) and combined sensitivity (80%) and specificity (89.9%) were found. CONCLUSIONS: There were significant differences in ADC50-300 between chronic pancreatitis and pancreatic carcinoma. Because D is not significantly different between groups, differences in ADC can be attributed mainly to differences in perfusion. The perfusion fraction f proved to be the superior DWI-derived parameter for differentiation of mass-forming pancreatitis and pancreatic carcinoma.

Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters.

OBJECTIVES: To evaluate in detail the diagnostic performance of diffusion-weighted imaging (DWI) to differentiate pancreas carcinoma from healthy pancreas using the apparent diffusion coefficient (ADC) and parameters derived from the intravoxel incoherent motion (IVIM) theory. MATERIALS AND METHODS: Twenty-three patients with pancreas carcinoma and 14 volunteers with healthy pancreas were examined at 1.5 Tesla using a single-shot echo-planar imaging DWI pulse sequence. Eleven b-values ranging from 0 to 800 s/mm2 were used. The acquisition was separated into blocks (b0, b25), (b0, b50),...(b0, b800) and each block was acquired in a single expirational breath-hold (TA = 26 seconds) to avoid motion artifacts. The ADC was calculated for all b-values using linear regression yielding ADC(tot). By applying the IVIM model, which allows for the estimation of perfusion effects in DWI, the perfusion fraction f and the perfusion free diffusion parameter D were calculated. The diagnostic performance of ADC, f and D as a measure for the differentiation between healthy pancreas and pancreatic carcinoma was evaluated with receiver operating characteristics analysis. RESULTS: In the healthy control group, the ADC(tot) ranged from 1.53 to 2.01 microm2/ms with a mean value of 1.71 +/- 0.19 microm2/ms, the perfusion fraction f ranged from 18.5% to 40.4% with a mean value of 25.0 +/- 6.2%, and the diffusion coefficient D from 0.94 to 1.28 microm2/ms with a mean value of 1.13 +/- 0.15 microm2/ms. In patients with pancreas carcinoma, the ADC(tot) ranged from 0.98 to 1.81 microm2/ms with a mean value of 1.31 +/- 0.24 microm2/ms, the perfusion fraction f ranged from 0% to 20.4% with a mean value of 8.59 +/- 4.6% and the diffusion coefficient D from 0.74 to 1.60 microm2/ms with a mean value of 1.15 +/- 0.22 microm2/ms. In comparison to healthy pancreatic tissue, a significant reduction of the perfusion fraction f and of ADC(tot) was found in pancreatic carcinoma (P < 0.00001, 0.0002, respectively). The f value showed more than a 10-fold higher significance level in distinguishing cancerous from normal tissue when compared with the ADC(tot) value. No significant difference in the diffusion coefficient D was observed between the 2 groups (P > 0.5). In the receiver operating characteristic-analyses, the area under curve for f was 0.991 and significantly larger than ADC(tot) (P < 0.05). f had the highest sensitivity, specificity, negative predictive value, and positive predictive value with 95.7%, 100%, 93.3%, and 100%, respectively. CONCLUSIONS: Using the IVIM-approach, the f value proved to be the best parameter for the differentiation between healthy pancreas and pancreatic cancer. The acquisition of several b-values strongly improved the stability of the parameter estimation thus increasing the sensitivity and specificity to 95.7% and 100% respectively. The proposed method may hold great promise for the non invasive, noncontrast-enhanced imaging of pancreas lesions and may eventually become a screening tool for pancreatic cancer.

Percutaneous vertebroplasty with a high-quality rotational angiographic unit.

We evaluated the reliability of a rotational angiographic unit (RA) with flat-panel detector as a single technique to guide percutaneous vertebroplasty (PVP) and for post-procedure assessment by 2D and 3D reformatted images. Fifty-five consecutive patients (104 vertebral bodies) were treated under RA fluoroscopy. Rotational acquisitions with 2D and 3D reconstruction were obtained in all patients for immediate post-procedure assessment. In complex cases, this technique was also used to evaluate the needle position during the procedure. All patients underwent CT scan after the procedure. RA and CT findings were compared. In all cases, a safe trans-pedicular access and an accurate control of the bone-cement injection were successfully performed with high-quality fluoroscopy, even at the thoracic levels and in case of vertebra plana. 2D and 3D rotational reconstructions permitted CT-like images that clearly showed needle position and were similar to CT findings in depicting intrasomatic implant-distribution. RA detected 40 cement leakages compared to 42 demonstrated by CT and showed overall 95% sensitivity and 100% specificity compared to CT for final post-procedure assessment. Our preliminary results suggest that high-quality RA is reliable and safe as a single technique for PVP guidance, control and post-procedure assessment. It permits fast and cost-effective procedures avoiding multi-modality imaging.

Aceruloplasminemia: a novel mutation in a family with marked phenotypic variability.

Hereditary aceruloplasminemia (HA) is a rare inherited disease characterized by anemia, iron overload, diabetes, and neurodegeneration. HA is caused by the homozygous mutation of the ceruloplasmin (CP) gene. We report two siblings with markedly different phenotypes carrying a novel mutation: a homozygous deletion of two nucleotides (1257-1258 TT del) causing the premature stop of the Cp protein translation (Y401X). An early diagnosis of iron overload was made in the female sibling who was subsequently treated with deferoxamine. At the age of 54, her neurologic symptoms were limited to mild akinetic signs and a history of seizures; moreover, her fasting blood glucose level never exceeded 120 mg/dL. The male sibling, who had not received any specific treatment for HA, developed severe diabetes at the age of 32 and at 48 manifested a progressively disabling neurologic disease. Possible physiopathological bases of these intrafamilial phenotypic variations are discussed.