Automatic Skeleton Segmentation in CT Images Based on U-Net

Bone metastasis, emerging oncological therapies, and osteoporosis represent some of the distinct clinical contexts which can result in morphological alterations in bone structure. The visual assessment of these changes through anatomical images is considered suboptimal, emphasizing the importance of precise skeletal segmentation as a valuable aid for its evaluation. In the present study, a neural network model for automatic skeleton segmentation from bidimensional computerized tomography (CT) slices is proposed. A total of 77 CT images and their semimanual skeleton segmentation from two acquisition protocols (whole-body and femur-to-head) are used to form a training group and a testing group. Preprocessing of the images includes four main steps: stretcher removal, thresholding, image clipping, and normalization (with two different techniques: interpatient and intrapatient). Subsequently, five different sets are created and arranged in a randomized order for the training phase. A neural network model based on U-Net architecture is implemented with different values of the number of channels in each feature map and number of epochs. The model with the best performance obtains a Jaccard index (IoU) of 0.959 and a Dice index of 0.979. The resultant model demonstrates the potential of deep learning applied in medical images and proving its utility in bone segmentation.

Robustness of [18F]FDG PET/CT radiomic analysis in the setting of drug-induced cardiotoxicity

BACKGROUND AND OBJECTIVES

Standardization of radiomic data acquisition protocols is still at a very early stage, revealing a strong need to work towards the definition of uniform image processing methodologies The aim of this study is to identify sources of variability in radiomic data derived from image discretization and resampling methodologies prior to image feature extraction. Furthermore, to identify robust potential image-based biomarkers for the early detection of cardiotoxicity.

METHODS

Image post-acquisition processing, interpolation, and volume of interest (VOI) segmentation were performed. Four experiments were conducted to assess the reliability in terms of the intraclass correlation coefficient (ICC) of the radiomic features and the effects of the variation of voxel size and gray level discretization. Statistical analysis was performed separating the patients according to cardiotoxicity diagnosis. Differences of texture features were studied with Mann-Whitney U test. P-values <0.05 after multiple testing correction were considered statistically significant. Additionally, a non-supervised k-Means clustering algorithm was evaluated.

RESULTS

The effect of the variation in the voxel size demonstrated a non-dependency relationship with the values of the radiomic features, regardless of the chosen discretization method. The median ICC values were 0.306 and 0.872 for absolute agreement and consistency, respectively, when varying the discretization bin number. The median ICC values were 0.678 and 0.878 for absolute agreement and consistency, respectively, when varying the discretization bin size. A total of 16 first order, 6 Gray Level Co-occurrence Matrix (GLCM), 4 Gray Level Dependence Matrix (GLDM) and 4 Gray Level Run Length Matrix (GLRLM) features demonstrated statistically significant differences between the diagnosis groups for interim scans (P<0.05) for the fixed bin size (FBS) discretization methodology. However, no statistically significant differences between diagnostic groups were found for the fixed bin number (FBN) discretization methodology. Two clusters based on the radiomic features were identified.

CONCLUSIONS

Gray level discretization has a major impact on the repeatability of the radiomic features. The selection of the optimal processing methodology has led to the identification of texture-based patterns for the differentiation of early cardiac damage profiles.

Radiomics analysis of bone marrow biopsy locations in [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

The combination of visual assessment of whole body [¹⁸F]FDG PET images and evaluation of bone marrow samples by Multiparameter Flow Cytometry (MFC) or Next-Generation Sequencing (NGS) is currently the most common clinical practice for the detection of Measurable Residual Disease (MRD) in Multiple Myeloma (MM) patients. In this study, radiomic features extracted from the bone marrow biopsy locations are analyzed and compared to those extracted from the whole bone marrow in order to study the representativeness of these biopsy locations in the image-based MRD assessment. Whole body [¹⁸F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A methodology for the segmentation of biopsy sites from PET images, including sternum and posterior iliac crest, and their subsequent quantification is proposed. First, starting from the bone marrow segmentation, a segmentation of the biopsy sites is performed. Then, segmentations are quantified extracting SUV metrics and radiomic features from the [¹⁸F]FDG PET images and are evaluated by Mann-Whitney U-tests as valuable features differentiating PET+/PET- and MFC+ /MFC- groups. Moreover, correlation between whole bone marrow and biopsy sites is studied by Spearman ρ rank. Classification performance of the radiomics features is evaluated applying seven machine learning algorithms. Statistical analyses reveal that some images features are significant in PET+/PET- differentiation, such as SUV_max, Gray Level Non-Uniformity or Entropy, especially with a balanced database where 16 of the features show a p value < 0.001. Correlation analyses between whole bone marrow and biopsy sites results in significant and acceptable coefficients, with 11 of the variables reaching a correlation coefficient greater than 0.7, with a maximum of 0.853. Machine learning algorithms demonstrate high performances in PET+/PET- classification reaching a maximum AUC of 0.974, but not for MFC+/MFC- classification. The results demonstrate the representativeness of sample sites as well as the effectiveness of extracted features (SUV metrics and radiomic features) from the [¹⁸F]FDG PET images in MRD assessment in MM patients.

Static first-minute-frame (FMF) PET imaging after 18F-labeled amyloid tracer injection is correlated to [18F]FDG PET in patients with primary progressive aphasia

OBJECTIVE

To study the correlation between a static PET image of the first-minute-frame (FMF) acquired with ¹⁸F-labeled amyloid-binding radiotracers and brain [¹⁸F]FDG PET in patients with primary progressive aphasia (PPA).

MATERIAL AND METHODS

The study cohort includes 17 patients diagnosed with PPA with the following distribution: 9 nonfluent variant PPA, 4 logopenic variant PPA, 1 semantic variant PPA, 3 unclassifiable PPA. Regional SUVRs are extracted from FMFs and their corresponding [¹⁸F]FDG PET images and Pearson's correlation coefficients are calculated.

RESULTS

SUVRs of both images show similar patterns of regional cerebral alterations. Intrapatient correlation analyses result in a mean coefficient of r=0.94±0.06. Regional interpatient correlation coefficients of the study cohort are greater than 0.81. Radiotracer-specific and variant-specific subcohorts show no difference in the similarity between the images.

CONCLUSIONS

The static FMF could be a valid alternative to dynamic early-phase amyloid PET proposed in the literature, and a neurodegeneration biomarker for the diagnosis and classification of PPA in amyloid PET studies.

Differences in Striatal Metabolism in [18F]FDG PET in Parkinson's Disease and Atypical Parkinsonism

Neurodegenerative parkinsonisms affect mainly cognitive and motor functions and are syndromes of overlapping symptoms and clinical manifestations such as tremor, rigidness, and bradykinesia. These include idiopathic Parkinson's disease (PD) and the atypical parkinsonisms, namely progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), multiple system atrophy (MSA) and dementia with Lewy body (DLB). Differences in the striatal metabolism among these syndromes are evaluated using [¹⁸F]FDG PET, caused by alterations to the dopaminergic activity and neuronal loss. A study cohort of three patients with PD, 29 with atypical parkinsonism (10 PSP, 6 CBD, 2 MSA, 7 DLB, and 4 non-classifiable), and a control group of 25 patients with normal striatal metabolism is available. Standardized uptake value ratios (SUVR) are extracted from the striatum, and the caudate and the putamen separately. SUVRs are compared among the study groups. In addition, hemispherical and caudate-putamen differences are evaluated in atypical parkinsonisms. Striatal hypermetabolism is detected in patients with PD, while atypical parkinsonisms show hypometabolism, compared to the control group. Hemispherical differences are observed in CBD, MSA and DLB, with the latter also showing statistically significant caudate-putamen asymmetry (p = 0.018). These results indicate disease-specific metabolic uptake patterns in the striatum that can support the differential diagnosis.

Bone marrow segmentation and radiomics analysis of [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

BACKGROUND AND OBJECTIVES

The last few years have been crucial in defining the most appropriate way to quantitatively assess [¹⁸F]FDG PET images in Multiple Myeloma (MM) patients to detect persistent tumor burden. The visual evaluation of images complements the assessment of Measurable Residual Disease (MRD) in bone marrow samples by multiparameter flow cytometry (MFC) or next-generation sequencing (NGS). The aim of this study was to quantify MRD by analyzing quantitative and texture [¹⁸F]FDG PET features.

METHODS

Whole body [¹⁸F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A segmentation methodology of the skeleton from CT images and an additional manual segmentation tool were proposed, implemented in a software solution including a graphical user interface. Both the compact bone and the spinal canal were removed from the segmentation to obtain only the bone marrow mask. SUV metrics, GLCM, GLRLM, and NGTDM parameters were extracted from the PET images and evaluated by Mann-Whitney U-tests and Spearman ρ rank correlation as valuable features differentiating PET+/PET- and MFC+/MFC- groups. Seven machine learning algorithms were applied for evaluating the classification performance of the extracted features.

RESULTS

Quantitative analysis for PET+/PET- differentiating demonstrated to be significant for most of the variables assessed with Mann-Whitney U-test such as Variance, Energy, and Entropy (p-value = 0.001). Moreover, the quantitative analysis with a balanced database evaluated by Mann-Whitney U-test revealed in even better results with 19 features with p-values < 0.001. On the other hand, radiomics analysis for MFC+/MFC- differentiating demonstrated the necessity of combining MFC evaluation with [¹⁸F]FDG PET assessment in the MRD diagnosis. Machine learning algorithms using the image features for the PET+/PET- classification demonstrated high performance metrics but decreasing for the MFC+/MFC- classification.

CONCLUSIONS

A proof-of-concept for the extraction and evaluation of bone marrow radiomics features of [¹⁸F]FDG PET images was proposed and implemented. The validation showed the possible use of these features for the image-based assessment of MRD.

An image processing tool for the detection of anthracycline-induced cardiotoxicity by evaluating the myocardial metabolic activity in [18F]FDG PET/CT

Purpose

Chemotherapy-induced cardiotoxicity is one of the main complications during and after cancer treatment. While echocardiography is the most used technique in clinical practice to evaluate left ventricular (LV) dysfunction, a multimodal approach is preferred for the early detection of anthracycline-induced cardiotoxicity. In this paper, an image processing tool allowing the qualitative and quantitative analysis of myocardial metabolic activity by [¹⁸F]fluorodeoxyglucose (FDG) positron emission tomography computed tomography (PET/CT) images, acquired routinely during and after cancer treatment, is presented.

Methods

The methodology is based on cardiac single photon emission computed tomography image processing protocols used in clinical practice. LV polar maps are created, and quantitative regional values are calculated. The tool was validated in a study group of 24 patients with Hodgkin or non-Hodgkin lymphoma (HL and NHL, respectively) treated with anthracyclines. Staging, interim and end-of-treatment [¹⁸F]FDG PET/CT images were acquired and the presented tool was used to extract the quantitative metrics of LV metabolic activity.

Results

Results show an overall increase of metabolic activity in the interim PET image acquired while on treatment compared to staging PET, which then decreased in the end-of-treatment scan. Positive correlation coefficients between staging and interim scans, and negative correlation coefficients between interim and end-of-treatment scans also support this finding. Metabolic changes occur predominantly in the septal region.

Conclusion

The proposed methodology and presented software solution provides the capability to assess quantitatively myocardial metabolism acquired by routine [¹⁸F]FDG PET/CT scanning during cancer treatment for evaluating anthracycline-induced cardiotoxicity. The [¹⁸F]FDG PET/CT septal-lateral uptake ratio is proposed as a new quantitative measure of myocardial metabolism.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-021-02508-9

Early-Onset Dementia Associated with a Heterozygous, Nonsense, and de novo Variant in the MBD5 Gene

The haploinsufficiency of the methyl-binding domain protein 5 (MBD5) gene has been identified as the determinant cause of the neuropsychiatric disorders grouped under the name MBD5-neurodevelopment disorders (MAND). MAND includes patients with intellectual disability, behavioral problems, and seizures with a static clinical course. However, a few reports have suggested regression. We describe a non-intellectually disabled female, with previous epilepsy and personality disorder, who developed early-onset dementia. The extensive etiologic study revealed a heterozygous nonsense de novo pathogenic variant in the MBD5 gene. This finding could support including the MBD5 gene in the study of patients with atypical early-onset dementia.

High Correlation of Static First-Minute-Frame (FMF) PET Imaging after 18F-Labeled Amyloid Tracer Injection with [18F]FDG PET Imaging

Dynamic early-phase PET images acquired with radiotracers binding to fibrillar amyloid-beta (Aβ) have shown to correlate with [18F]fluorodeoxyglucose (FDG) PET images and provide perfusion-like information. Perfusion information of static PET scans acquired during the first minute after radiotracer injection (FMF, first-minute-frame) is compared to [18F]FDG PET images. FMFs of 60 patients acquired with [18F]florbetapir (FBP), [18F]flutemetamol (FMM), and [18F]florbetaben (FBB) are compared to [18F]FDG PET images. Regional standardized uptake value ratios (SUVR) are directly compared and intrapatient Pearson's correlation coefficients are calculated to evaluate the correlation of FMFs to their corresponding [18F]FDG PET images. Additionally, regional interpatient correlations are calculated. The intensity profiles of mean SUVRs among the study cohort (r = 0.98, p < 0.001) and intrapatient analyses show strong correlations between FMFs and [18F]FDG PET images (r = 0.93 ± 0.05). Regional VOI-based analyses also result in high correlation coefficients. The FMF shows similar information to the cerebral metabolic patterns obtained by [18F]FDG PET imaging. Therefore, it could be an alternative to the dynamic imaging of early phase amyloid PET and be used as an additional neurodegeneration biomarker in amyloid PET studies in routine clinical practice while being acquired at the same time as amyloid PET images.

Acute hypokinetic-rigid syndrome following SARS-CoV-2 infection

OBJECTIVE

To report a case of a patient infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who acutely developed a hypokinetic-rigid syndrome.

METHODS

Patient data were obtained from medical records from the Hospital Universitario 12 de Octubre in Madrid, Spain. [¹²³I]-ioflupane dopamine transporter (DaT) SPECT images were acquired 4 hours after a single dose of 185 MBq of ¹²³I-FP-CIT. Quantitative analysis was performed with DaTQUANT software providing the specific binding ratio and z score values of the striatum.

RESULTS

We report a previously healthy 58-year-old man who developed hyposmia, generalized myoclonus, fluctuating and transient changes in level of consciousness, opsoclonus, and an asymmetric hypokinetic-rigid syndrome with ocular abnormalities after a severe SARS-CoV-2 infection. DaT-SPECT confirmed a bilateral decrease in presynaptic dopamine uptake asymmetrically involving both putamina. Significant improvement in the parkinsonian symptoms was observed without any specific treatment.

CONCLUSION

This case study provides clinical and functional neuroimaging evidence to support that SARS-CoV-2 can gain access to the CNS, affecting midbrain structures and leading to neurologic signs and symptoms.

Decreased salivary lactoferrin levels are specific to Alzheimer's disease

BACKGROUND

Evidences of infectious pathogens in Alzheimer's disease (AD) brains may suggest a deteriorated innate immune system in AD pathophysiology. We previously demonstrated reduced salivary lactoferrin (Lf) levels, one of the major antimicrobial proteins, in AD patients.

METHODS

To assess the clinical utility of salivary Lf for AD diagnosis, we examine the relationship between salivary Lf and cerebral amyloid-β (Aβ) load using amyloid-Positron-Emission Tomography (PET) neuroimaging, in two different cross-sectional cohorts including patients with different neurodegenerative disorders.

FINDINGS

The diagnostic performance of salivary Lf in the cohort 1 had an area under the curve [AUC] of 0•95 (0•911-0•992) for the differentiation of the prodromal AD/AD group positive for amyloid-PET (PET⁺) versus healthy group, and 0•97 (0•924-1) versus the frontotemporal dementia (FTD) group. In the cohort 2, salivary Lf had also an excellent diagnostic performance in the health control group versus prodromal AD comparison: AUC 0•93 (0•876-0•989). Salivary Lf detected prodromal AD and AD dementia distinguishing them from FTD with over 87% sensitivity and 91% specificity.

INTERPRETATION

Salivary Lf seems to have a very good diagnostic performance to detect AD. Our findings support the possible utility of salivary Lf as a new non-invasive and cost-effective AD biomarker.

FUNDING

Instituto de Salud Carlos III (FIS15/00780, FIS18/00118), FEDER, Comunidad de Madrid (S2017/BMD-3700; NEUROMETAB-CM), and CIBERNED (PI2016/01) to E.C.; Spanish Ministry of Economy and Competitiveness (SAF2017-85310-R) to J.L.C., and (PSI2017-85311-P) to M.A.; International Centre on ageing CENIE-POCTEP (0348_CIE_6_E) to M.A.; Instituto de Salud Carlos III (PIE16/00021, PI17/01799), to H.B.

Relevance of quantification in brain PET studies with 18F-FDG

The inclusion of ¹⁸F-FDG PET as a biomarker in the diagnostic criteria of neurodegenerative diseases and its indication in the presurgical assessment for drug-resistant epilepsies allow to improve specificity of these diagnosis. The traditional interpretation of neurological PET studies has been performed qualitatively, although in the last decade, several quantitative evaluation methods have emerged. This technical development has become relevant in clinical practice, improving specificity, reproducibility and reducing the interrater reliability derived from visual analysis. In this article we update/review the main imaging processing techniques currently used. This may allow the Nuclear Medicine physician to know their advantages and disadvantages when including these procedures in daily clinical practice.

Amyloid PET in neurodegenerative diseases with dementia

Alzheimer's disease (AD) is a neurodegenerative condition characterized by progressive cognitive decline and memory loss, and is the most common form of dementia. Amyloid plaques with neurofibrillary tangles are a neuropathological hallmark of AD that produces synaptic dysfunction and culminates later in neuronal loss. Amyloid PET is a useful, available and non-invasive technique that provides in vivo information about the cortical amyloid burden. In the latest revised criteria for the diagnosis of AD biomarkers were defined and integrated: pathological and diagnostic biomarkers (increased retention on fibrillar amyloid PET or decreased Aβ_1-42 and increased T-Tau or P-Tau in CSF) and neurodegeneration or topographical biomarkers (temporoparietal hypometabolism on ¹⁸F-FDG PET and temporal atrophy on MRI). Recently specific recommendations have been created as a consensus statement on the appropriate use of the imaging biomarkers, including amyloid PET: early-onset cognitive impairment/dementia, atypical forms of AD, mild cognitive impairment with early age of onset, and to differentiate between AD and other neurodegenerative diseases that occur with dementia. Amyloid PET is also contributing to the development of new therapies for AD, as well as in research studies for the study of other neurodegenerative diseases that occur with dementia where the deposition of Aβ amyloid is involved in its pathogenesis. In this paper, we review some general concepts and study the use of amyloid PET in depth and its relationship with neurodegenerative diseases and other diagnostic techniques.