A thermoluminescent method for the evaluation of the 131I effective half-life in the thyroid when treating Graves' disease.

When planning treatment for Graves' disease with 131I, the effective half-life (Teff) should be estimated individually as it depends on biological characteristics such as iodine uptake and excretion, which differ from an individual to another (Berg et al. 1996). All the methods to quantify Teff described in the literature are quite complex and are difficult to be used in clinical routine. With the aim of optimizing this process, a simplified method is proposed here to evaluate Teff of 131I during treatment of Graves' disease. The present study suggests improving the method of determining Teff based on thermoluminescence dosimetry. This involves implementing a new method and includes reduction of TLD (Thermoluminescent Dosimeter) measurements. The proposed method was validated on patients with Graves' disease. The radiation dose delivered to the patients was determined using the MIRD (Medical Internal Radiation Dosimetry) formalism. The relative difference between Teff obtained based on seven measurement intervals at [0-24 h, 24-48 h, 48-72 h, 72-96 h, 96-120 h, 120-144 h, 144-168 h] and based on three measurement intervals at [0-24 h, 72-96 h, 144-168 h] and [0-24 h, 120-144 h, 144-168 h] was 1.9% and 3.81%, respectively. Comparison of doses obtained based on a general Teff and on a personalized Teff gave a statistically significant difference with a correlation coefficient R2of 0.44. The Teff obtained from just three measurements was found to be sufficiently accurate and easily applicable. The results obtained demonstrate the need to determine and use personalized Teff values instead of using a fixed value of 7 days.

Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal.

BACKGROUND: In breast ultrasound elastography, tissues displacements estimation is obtained through a technique that follows the evolution of tissues under stress. However, during the acquisition of B-mode images, tissue displacements are often contaminated with multiplicative noise caused by changes in the speckle pattern in the tissue. Thus, the application of monogenic signal technique on the B-mode image in order to estimate displacement tissue, result in a presence of amplified noise in the deformation tissue image, which severely obscures the useful information. In this paper, we propose a new method based on the monogenic features, that is to improve the old monogenic signal (OMS) technique by improving the filtering step, so that the use of an effective denoising technique is enough to ensure a good estimation of displacement tissue. Our proposed method is based on the use of a robust filtering technique combined with the monogenic model. METHODS: Two models of phantom elasticity are used in our test validation sold by CIRS company. In-vivo testing was also performed on the sets of clinical B-mode images to 20 patients including malignant breast tumors. Shrinkage wavelets has been used to eliminate the noise according to the threshold, then a guided filter is introduced to completely filter the image, the monogenic model is used after excerpting the image feature and estimating analytically the displacement tissue. RESULTS: Accurate and excellent displacement estimation for breast tissue was observed in proposed method results. By adapting our proposed approach to breast B-mode images, we have shown that it demonstrated a higher performance for displacement estimation; it gives better values in term of standard deviation, higher contrast to noise ratio, greater peak signal-to-noise ratio, excellent structural similarity and much faster speed than OMS and B-spline techniques. The results of the proposed model are encouraging, allowing quick and reliable estimations. CONCLUSION: Although the proposed approach is used in ultrasound domains, it has never been used in the estimation of the breast tissue displacement. In this context, our proposed approach could be a powerful diagnostic tool to be used in breast displacement estimation in ultrasound elastography.

Interpretation of cardiac wall motion from cine-MRI combined with parametric imaging based on the Hilbert transform.

OBJECT: The aim of this study was to test and validate the clinical impact of parametric amplitude images obtained using the Hilbert transform on the regional interpretation of cardiac wall motion abnormalities from cine-MR images by non-expert radiologists compared with expert consensus. MATERIALS AND METHODS: Cine-MRI short-axis images obtained in 20 patients (10 with myocardial infarction, 5 with myocarditis and 5 with normal function) were processed to compute a parametric amplitude image for each using the Hilbert transform. Two expert radiologists blindly reviewed the cine-MR images to define a gold standard for wall motion interpretation for each left ventricular sector. Two non-expert radiologists reviewed and graded the same images without and in combination with parametric images. Grades assigned to each segment in the two separate sessions were compared with the gold standard. RESULTS: According to expert interpretation, 264/320 (82.5%) segments were classified as normal and 56/320 (17.5%) were considered abnormal. The accuracy of the non-expert radiologists' grades compared to the gold standard was significantly improved by adding parametric images (from 87.2 to 94.6%) together with sensitivity (from 64.29 to 84.4%) and specificity (from 92 to 96.9%), also resulting in reduced interobserver variability (from 12.8 to 5.6%). CONCLUSION: The use of parametric amplitude images based on the Hilbert transform in conjunction with cine-MRI was shown to be a promising technique for improvement of the detection of left ventricular wall motion abnormalities in less expert radiologists.

Automatic triaging of acute ischemic stroke patients for reperfusion therapies using Artificial Intelligence methods and multiple MRI features: A review.

BACKGROUND: The selection of appropriate treatments for Acute Ischemic Stroke (AIS), including Intravenous (IV) tissue plasminogen activator (tPA) and Mechanical thrombectomy, is a critical aspect of clinical decision-making. Timely treatment is essential, with recommended administration of therapies within 4.5 h of symptom onset. However, patients with unknown Time Since Stroke (TSS), are often excluded from thrombolysis, even if the stroke onset exceeds 6 h. Current clinical guidelines propose using multimodal Magnetic Resonance Imaging (MRI) to assess various mismatches. METHODS: The review explores the significance of automatic methods based on Artificial Intelligence (AI) algorithms that utilize multiple MRI features to identify patients who are most likely to benefit from acute reperfusion therapies. These AI methods include TSS classification and patient selection for therapies in the late time window (>6 h) using MRI images to provide detailed stroke information. RESULTS: The review discusses the challenges and limitations in the existing mismatch methods, which may lead to missed opportunities for reperfusion therapy. To address these limitations, AI approaches have been developed to enhance accuracy and support clinical decision-making. These AI methods have shown promising results, outperforming traditional mismatch assessments and providing improved sensitivity and specificity in identifying patients eligible for reperfusion therapies. DISCUSSION: In summary, the integration of AI algorithms utilizing multiple MRI features has the potential to enhance accuracy, improve patient outcomes, and positively influence the decision-making process in AIS. However, ongoing research and collaboration among clinicians, researchers, and technologists are vital to realize the full potential of AI in optimizing stroke management.

Applications of artificial intelligence for DWI and PWI data processing in acute ischemic stroke: Current practices and future directions.

Multimodal Magnetic Resonance Imaging (MRI) techniques of Perfusion-Weighted Imaging (PWI) and Diffusion-Weighted Imaging (DWI) data are integral parts of the diagnostic workup in the acute stroke setting. The visual interpretation of PWI/DWI data is the most likely procedure to triage Acute Ischemic Stroke (AIS) patients who will access reperfusion therapy, especially in those exceeding 6 h of stroke onset. In fact, this process defines two classes of tissue: the ischemic core, which is presumed to be irreversibly damaged, visualized on DWI data and the penumbra which is the reversibly injured brain tissue around the ischemic tissue, visualized on PWI data. AIS patients with a large ischemic penumbra and limited infarction core have a high probability of benefiting from endovascular treatment. However, it is a tedious and time-consuming procedure. Consequently, it is subject to high inter- and intra-observer variability. Thus, the assessment of the potential risks and benefits of endovascular treatment is uncertain. Fast, accurate and automatic post-processing of PWI and DWI data is important for clinical diagnosis and is necessary to help the decision making for therapy. Therefore, an automated procedure that identifies stroke slices, stroke hemisphere, segments stroke regions in DWI, and measures hypoperfused tissue in PWI enhances considerably the reproducibility and the accuracy of stroke assessment. In this work, we draw an overview of several applications of Artificial Intelligence (AI) for the automation processing and their potential contributions in clinical practices. We compare the current approaches among each other's with respect to some key requirements.

Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images.

BACKGROUND: After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images. METHODS: The database consists of a total of 1005 MRI images with evolutive lymphoma and residual masses. More specifically, we propose a novel approach that leverages: (1)-The complementarity of the functional and anatomical criteria of MRI images through a fusion step based on the discrete wavelet transforms (DWT). (2)- The automatic segmentation of the lesions, their localization, and their enumeration using the Chan-Vese algorithm. (3)- The generation of the parametric image which contains the apparent diffusion coefficient value named ADC map. (4)- The features selection through the application of the sequential forward selection (SFS), Entropy, Symmetric uncertainty and Gain Ratio algorithm on 72 extracted features. (5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN). Moreover, this study is achieved with an evaluation of the classification using 335 DW-MR images where 80% of them are used for the training and the remaining 20% for the test. RESULTS: The obtained accuracy for the five classifiers recorded a slight superiority to the proposed method based on the back-propagation 3-9-1 ANN model which reaches 96,5%. In addition, we compared the proposed method to five other works from the literature. The proposed method gives much better results in terms of SE, SP, accuracy, F1-measure, and geometric-mean which reaches respectively 96.4%, 90.9%, 95.5%, 0.97, and 91.61%. CONCLUSIONS: Our initial results suggest that Combining functional, anatomical, and morphological features of ROI's have very good accuracy (97.01%) for evolutive lymphoma and residual masses recognition when we based on the new proposed approach using the back-propagation 3-9-1 ANN model. Proposed method based on machine learning gives less than Deep learning CNN, which is 98.5%.

[Internal mammary lymph node invasion in breast cancer: myth or reality?]. / L'atteinte de la chaîne mammaire interne dans le cancer du sein: mythe ou réalite?

The internal mammary nodes are often underestimated as breast cancer lymphatic pathway spread. It is yet the first site of lymphatic invasion in central or internal tumors and the second site in external tumors. The intra-thoracic situation of internal mammary nodes explains partly, the difficulty of its exploration. To evaluate the risk of internal mammary node invasion, some predictive factors are established (tumor size, internal or central tumor location, axillary node invasion and young age). Prognostic and therapeutic impact of invasion justifies its systematic research. Without exploring internal mammary nodes status, TNM classification remains, incomplete. CT scanner, magnetic resonnance imaging, positron emission tomography scanner and sentinel node exploration technique are helpful to explore this region and to adapt its irradiation.

Comparison Between 3D Echocardiography and Cardiac Magnetic Resonance Imaging (CMRI) in the Measurement of Left Ventricular Volumes and Ejection Fraction.

BACKGROUND: Echocardiography and Cardiac Magnetic Resonance Imaging (CMRI) are two noninvasive techniques for the evaluation of cardiac function for patients with coronary artery diseases. Although echocardiography is the commonly used technique in clinical practice for the assessment of cardiac function, the measurement of LV volumes and left ventricular ejection fraction (LVEF) by the use of this technique is still influenced by several factors inherent to the protocol acquisition, which may affect the accuracy of echocardiography in the measurement of global LV parameters. OBJECTIVE: The aim of this study is to compare the end systolic volume (ESV), the end diastolic volume (EDV), and the LVEF values obtained with three dimensional echocardiography (3D echo) with those obtained by CMRI (3 Tesla) in order to estimate the accuracy of 3D echo in the assessment of cardiac function. METHODS: 20 subjects, (9 controls, 6 with myocardial infarction, and 5 with myocarditis) with age varying from 18 to 58, underwent 3D echo and CMRI. LV volumes and LVEF were computed from CMRI using a stack of cine MRI images in a short axis view. The same parameters were calculated using the 3D echo. A linear regression analysis and Bland Altman diagrams were performed to evaluate the correlation and the degree of agreement between the measurements obtained by the two methods. RESULTS: The obtained results show a strong correlation between the 3D echo and CMR in the measurement of functional parameters (r = 0.96 for LVEF values, r = 0.99 for ESV and r= 0.98 for EDV, p < 0.01 for all) with a little lower values of LV volumes and higher values of LVEF by 3D echo compared to CMRI. According to statistical analysis, there is a slight discrepancy between the measurements obtained by the two methods. CONCLUSION: 3D echo represents an accurate noninvasive tool for the assessment of cardiac function. However, other studies should be conducted on a larger population including some complicated diagnostic cases.

The Role of Phase Image in the Detection of Myocardial Dyskinesia by Magnetic Resonance Imaging (MRI).

BACKGROUND: The assessment of cardiac wall motion abnormalities plays an important role in the evaluation of many cardiovascular diseases and the prediction of functional recovery. Most of the methods dedicated to identifying the location of wall motion abnormalities have been restricted to study hypokinesia while an accurate way to assess dyskinesia is still needed in Cardiac Magnetic Resonance Imaging (CMRI). OBJECTIVE: The aim of this study is to propose a phase image based on the analytic signal able to assess the extent of the myocardial dyskinetic segments in Cardiac Magnetic Resonance Imaging (CMRI). MATERIALS: 22 subjects were retrospectively enrolled in this study (age 46 ± 11): 15 presenting an aneurysm and 7 control subjects with normal wall motion. For each patient, three standard views (short axis view, 2 chamber and 4 chamber views) were acquired using 3 Tesla Siemens Avanto MRI scanner and a segmented True FISP sequence. All the cine MRI images were analyzed by two experimented observers who were blinded to the diagnostic results. RESULTS: The outcomes of this study show that using the proposed phase image in MRI clinical routine can increase the accuracy of the detection of myocardial dyskinetic segments from 77.23 % to 86.38 %, the sensitivity from 67.48 % to 78.86 % as well the specificity from 80.92 % to 89.23 % compared to the standard method based on cine MRI interpretation. CONCLUSION: The phase image is a promising tool in CMRI for the assessment of dyskinetic segments and the degree of myocardial asynchronism.

Quantitative evaluation of fiber tractography with a Delaunay triangulation-based interpolation approach.

The recent challenge in high angular resolution diffusion imaging (HARDI) is to find a tractography process that provides information about the neural architecture within the white matter of the brain in a clinically feasible measurement time. The great success of the HARDI technique comes from its capability to overcome the problem of crossing fiber detection. However, it requires a large number of diffusion-weighted (DW) images which is problematic for clinical time and hardware. The main contribution of this paper is to develop a full tractography framework that gives an accurate estimate of the crossing fiber problem with the aim of reducing data acquisition time. We explore the interpolation in the gradient direction domain as a method to estimate the HARDI signal from a reduced set of DW images. The experimentation was performed in a first time on simulated data for a quantitative evaluation using the Tractometer system. We used, also, in vivo human brain data to demonstrate the potential of our pipeline. Results on both simulated and real data illustrate the effectiveness of our approach to perform the brain connectivity. Overall, we have shown that the proposed approach achieves competitive results to other tractography methods according to Tractometer connectivity metrics. Graphical Abstract.

Partial Congenital Absence of The Pericardium: A Case Report.

The complete or the partial absence of pericardium is a rare congenital malformation for which the patients are commonly asymptomatic and the diagnosis is incidental. The absence of the left side of the pericardium is the most common anomaly that is reported in the literature while the complete absence of pericardium or the absence of the right side of the pericardium are uncommon and their criteria are still unrecognized given their rare occurrence in clinical practice. This paper aims to report a case of 19-year-old male with the congenital partial absence of both sides of the pericardium and to highlight the symptoms and the different cardiac imaging modalities used to confirm the diagnosis of this defect.

Parametric Imaging for the Assessment of Cardiac Motion: A Review.

The assessment of wall motion abnormalities such as hypokinesia or dyskinesia and the identification of their extent as well as their degree of severity allow an accurate evaluation of several ischemic heart diseases and an early diagnosis of heart failure. These dysfunctions are usually revealed by a drop of contraction indicating a regional hypokinesia or a total absence of the wall motion in case of akinesia. The discrimination between these contraction abnormalities plays also a significant role in the therapeutic decision through the differentiation between the infarcted zones, which have lost their contractile function, and the stunned areas that still retain viable myocardial tissues. The lack of a reliable method for the evaluation of wall motion abnormalities in cardiac imaging presents a major limitation for a regional assessment of the left ventricular function. In the past years, several techniques were proposed as additional tools for the local detection of wall motion deformation. Among these approaches, the parametric imaging is likely to represent a promising technique for the analysis of a local contractile function. The aim of this paper is to review the most recent techniques of parametric imaging computation developed in cardiac imaging and their potential contributions in clinical practice.

Left ventricular MRI wall motion assessment by monogenic signal amplitude image computation.

BACKGROUND: Cardiac Magnetic Resonance Imaging (MRI) is the commonly used technique for the assessment of left ventricular (LV) function. Apart manually or semi-automatically contouring LV boundaries for quantification of By visual interpretation of cine images, assessment of regional wall motion is performed by visual interpretation of cine images, thus relying on an experience-dependent and subjective modality. OBJECTIVE: The aim of this work is to describe a novel algorithm based on the computation of the monogenic amplitude image to be utilized in conjunction with conventional cine-MRI visualization to assess LV motion abnormalities and to validate it against gold standard expert visual interpretation. METHODS: The proposed method uses a recent image processing tool called "monogenic signal" to decompose the MR images into features, which are relevant for motion estimation. Wall motion abnormalities are quantified locally by measuring the temporal variations of the monogenic signal amplitude. The new method was validated by two non-expert radiologists using a wall motion scoring without and with the computed image, and compared against the expert interpretation. The proposed approach was tested on a population of 40 patients, including 8 subjects with normal ventricular function and 32 pathological cases (20 with myocardial infarction, 9 with myocarditis, and 3 with dilated cardiomyopathy). RESULTS: The results show that, for both radiologists, sensitivity, specificity and accuracy of cine-MRI alone were similar and around 59%, 77%, and 71%, respectively. Adding the proposed amplitude image while visualizing the cine MRI images significantly increased both sensitivity, specificity and accuracy up to 75%, 89%, and 84%, respectively. CONCLUSION: Accuracy of wall motion interpretation adding amplitude image to conventional visualization was proven feasible and superior to standard image interpretation on the considered population, in inexperienced observers. Adding the amplitude images as a diagnostic tool in clinical routine is likely to improve the detection of myocardial segments presenting a cardiac dysfunction.

Partial congenital absence of the pericardium: a case report

Abstract The complete or the partial absence of pericardium is a rare congenital malformation for which the patients are commonly asymptomatic and the diagnosis is incidental. The absence of the left side of the pericardium is the most common anomaly that is reported in the literature while the complete absence of pericardium or the absence of the right side of the pericardium are uncommon and their criteria are still unrecognized given their rare occurrence in clinical practice. This paper aims to report a case of 19-year-old male with the congenital partial absence of both sides of the pericardium and to highlight the symptoms and the different cardiac imaging modalities used to confirm the diagnosis of this defect.