Pulmonary 18F-FDG uptake helps refine current risk stratification in idiopathic pulmonary fibrosis (IPF).

PURPOSE: There is a lack of prognostic biomarkers in idiopathic pulmonary fibrosis (IPF) patients. The objective of this study is to investigate the potential of 18F-FDG-PET/ CT to predict mortality in IPF. METHODS: A total of 113 IPF patients (93 males, 20 females, mean age ± SD: 70 ± 9 years) were prospectively recruited for 18F-FDG-PET/CT. The overall maximum pulmonary uptake of 18F-FDG (SUVmax), the minimum pulmonary uptake or background lung activity (SUVmin), and target-to-background (SUVmax/ SUVmin) ratio (TBR) were quantified using routine region-of-interest analysis. Kaplan-Meier analysis was used to identify associations of PET measurements with mortality. We also compared PET associations with IPF mortality with the established GAP (gender age and physiology) scoring system. Cox analysis assessed the independence of the significant PET measurement(s) from GAP score. We investigated synergisms between pulmonary 18F-FDG-PET measurements and GAP score for risk stratification in IPF patients. RESULTS: During a mean follow-up of 29 months, there were 54 deaths. The mean TBR ± SD was 5.6 ± 2.7. Mortality was associated with high pulmonary TBR (p = 0.009), low forced vital capacity (FVC; p = 0.001), low transfer factor (TLCO; p  4.9 was 24 months. Combining PET data with GAP data ("PET modified GAP score") refined the ability to predict mortality. CONCLUSIONS: A high pulmonary TBR is independently associated with increased risk of mortality in IPF patients

Simulation of Breast Lesions in X-Ray Mammography Screening.

X-ray mammography is the imaging modality of choice in screening to detect breast cancer in its early stages. In recent years, film-screen systems have been replaced by various digital mammography technologies as these can deliver better performance than conventional film-screen technology. However, it remains unclear how the physical performance of such systems and the choice of their operating parameters is correlated with the ability to detect early breast cancer. While clinical trials are used to address this issue, they have many associated limitations such as unethical extra exposure, time consuming data collection and completion of trials. Alternatively, a simulation framework whereby suitably realistic synthetic breast cancer pathology is inserted into normal clinical mammograms to form a large database can enable a more efficient comparison of multiple systems and study of technical parameters which influence the detection task. This thesis presents a novel computational model of breast mass appearance using fractal growth which can exhibit a range of lesion appearances. Masses generated using Random Walk (RW) and Diffusion Limited Aggregation (DLA) models were inserted into raw digital 2D mammograms using a physical model of the imaging process, thus avoiding ad hoc post-processing of the final image. The simulation framework accounted for local glandularity, polychromatic X-ray spectra, image degradation caused by the imaging system acquisition process, scatter and finally processing with manufacturer’s image processing software to produce realistic lesion attenuation and contrast. An ROC study of realism gave an average AUC and corresponding 95% CIs of 0.55 (0.51, 0.59) for DLA masses. This suggests that the DLA approach appears to produce a more realistic range of mass appearances compared to the RW approach, which achieved an AUC of 0.60 (0.56, 0.63). Both results demonstrate improvement compared to previously published ROC studies of realism of the simulated masses. The mass simulation models may be used subsequently as part of a tool to evaluate different breast imaging technologies (2D and 3D) and their performance in the detection task. Digital breast tomosynthesis (DBT) may have superior performance compared to 2D mammography in terms of cancer visibility, especially in dense breasts. Lesions grown using the DLA method, previously validated in 2D mammograms, were used to simulate breast masses into clinical DBT projection images. A pilot study was performed where radiologists feedback suggests that DLA masses can be successfully embedded in DBT projections and can produce visually authentic DBT images containing synthetic pathology. However, mass appearance whilst entirely satisfactory in 2D, does not always reliably infer satisfactory appearance in DBT

PCA regression for continuous estimation of head pose in PET/MR

With the availability of improved hardware and local point-spread function modelling, the presence of patient motion has become a major barrier to further improvements in the quality of PET images and their clinical efficacy. Although numerous approaches to compensate for patient motion have been proposed and are even commercially available, the additional hardware and extended setup time can preclude their routine clinical use. The MR modality on combined PET and MR (PET/MR) scanners can be used to correct motion with almost no additional setup time but currently must replace other MR acquisitions that may be required for clinic use. To overcome these problems, principal component analysis (PCA) and other data-driven techniques have been demonstrated to be able to reliably provide a signal related to patient motion based on raw PET data. Typically, these signals are used to split the PET acquisition into a discrete set of approximately motionfree time segments. This work introduces an approach where the PCA-signals are used as direct surrogates for the motion and regressed against rigid head motion parameters, enabling continuous pose estimation. A proof-of-concept is presented in which the approach is applied to upsample a low temporal resolution MR motion estimate. This proof-of-concept uses rapid echo planar imaging (EPI) data together with PET-derived motion signals. In a comparison of four techniques, nearest neighbour (NN) and linear temporal interpolation and linear and radial basis function (RBF) regression of pose against the PCA surrogate, we demonstrate that the model can be used to accurately interpolate pose continuously throughout the scan

Pulmonary 18F-FDG uptake helps refine current risk stratification in idiopathic pulmonary fibrosis (IPF).

PURPOSE: There is a lack of prognostic biomarkers in idiopathic pulmonary fibrosis (IPF) patients. The objective of this study is to investigate the potential of 18F-FDG-PET/ CT to predict mortality in IPF. METHODS: A total of 113 IPF patients (93 males, 20 females, mean age ± SD: 70 ± 9 years) were prospectively recruited for 18F-FDG-PET/CT. The overall maximum pulmonary uptake of 18F-FDG (SUVmax), the minimum pulmonary uptake or background lung activity (SUVmin), and target-to-background (SUVmax/ SUVmin) ratio (TBR) were quantified using routine region-of-interest analysis. Kaplan-Meier analysis was used to identify associations of PET measurements with mortality. We also compared PET associations with IPF mortality with the established GAP (gender age and physiology) scoring system. Cox analysis assessed the independence of the significant PET measurement(s) from GAP score. We investigated synergisms between pulmonary 18F-FDG-PET measurements and GAP score for risk stratification in IPF patients. RESULTS: During a mean follow-up of 29 months, there were 54 deaths. The mean TBR ± SD was 5.6 ± 2.7. Mortality was associated with high pulmonary TBR (p = 0.009), low forced vital capacity (FVC; p = 0.001), low transfer factor (TLCO; p  4.9 was 24 months. Combining PET data with GAP data ("PET modified GAP score") refined the ability to predict mortality. CONCLUSIONS: A high pulmonary TBR is independently associated with increased risk of mortality in IPF patients

STIR Software for Tomographic Image Reconstruction

<h1>Summary of changes in STIR release 5.2.0</h1> <p>This version is 100% backwards compatible with STIR 5.0 as far as usage goes. However, there are changes in the output of scatter estimation and ECAT8 normalisation, see below for more information.</p> <h2>Overall summary</h2> <p>Of course, there is also the usual code-cleanup and improvements to the documentation. See also <a href="https://github.com/UCL/STIR/milestone/6">the 5.2 milestone on GitHub</a>.</p> <p>Overall code management and assistance by Kris Thielemans (UCL and ASC). Other main contributors were Daniel Deidda (NPL) and Markus Jehl (Positrigo).</p> <h2>Patch release info</h2> <ul> <li>5.2.0 released 30/10/2023</li> </ul> <h2>Summary for end users (also to be read by developers)</h2> <h3>Bug fixes</h3> <ul> <li>Scatter estimation was setting initial activity image to 1 at set-up, effectively ignoring the initial image, aside from geometric info.</li> <li>Setting SPECTUB resolution model with STIR python or SIRF divided slope by 10 in error. The problem did not occur when set using parameter file</li> </ul> <h3>Changed functionality</h3> <ul> <li>The ECAT8 normalisation (used for the Siemens mMR) code now takes the 4th component <em>axial effects</em> into account. These normalisation factors are therefore different (even up to ~10%). This gives improved axial uniformity in the images. The use of the axial effects can be switched off by adding setting <code>use_axial_effects_factors:=0</code> to the parameter file (see an example in <code>examples/Siemens-mMR/correct_projdata_no_axial_effects.par</code>), or the class member of the same name. In addition, the Siemens normalisation header is now read (using a new class <code>InterfileNormHeaderSiemens</code>) such that hard-coded variables for the Siemens mMR have been removed. Further testing of this functionality is still required however. <a href="https://github.com/UCL/STIR/pull/1182/">PR #1182</a>.</li> <li>Interfile header parsing now correctly identifies keywords that contain a colon by checking for <code>:=</code>.</li> <li>The <code>set_up()</code> method of the ray-tracing projection matrix now skips further processing if it was already called with data of the same characteristics. This will means that any cached data will be re-used, potentially leading to a speed-up when re-using it from Python. <a href="https://github.com/UCL/STIR/pull/1281/">PR #1281</a>.</li> </ul> <h3>New functionality</h3> <ul> <li><p>The <code>Discretised Shape3D</code> shape/ROI has now an extra value <code>label index</code>. For ROIs, this allows using a single volume with multiple ROIs encoded as labels, such as output by ITKSnap and many others. When used as a shape in <code>generate_image</code>, it could be used to extract a single ROI from such a label image. <a href="https://github.com/UCL/STIR/pull/1196/">PR #1196</a>.</p> </li> <li><p>Global variables in SPECTUB have been substituted by class members, such that multiple SPECTUB projectors can be used. <a href="https://github.com/UCL/STIR/pull/1169/">PR #1169</a>.</p> </li> <li><p>Global variables in PinholeSPECTUB have been substituted by class members, such that multiple PinholeSPECTUB projectors can be used. <a href="https://github.com/UCL/STIR/pull/1212/">PR #1212</a>.</p> </li> <li><p>Scatter estimation is now smoothed in axial direction for BlocksOnCylindrical scanners. <a href="https://github.com/UCL/STIR/pull/1172/">PR #1172</a>.</p> </li> <li><p><code>InverseSSRB</code> now works for BlocksOnCylindrical after a rewrite. <a href="https://github.com/UCL/STIR/pull/1172/">PR #1172</a>. /</p> </li> <li><p>Parallelised function <code>set_fan_data_add_gaps_help</code> across segments to reduce computation time. <a href="https://github.com/UCL/STIR/pull/1168/">PR #1168</a>.</p> </li> <li><p>New utility <code>SPECT_dicom_to_interfile</code> which reads a DICOM file with SPECT projection data and extracts the data and writes one or more Interfile 3.3 headers (still somewhat preliminary). <a href="https://github.com/UCL/STIR/pull/1182/">PR #1182</a>.</p> </li> <li><p>The new <code>stir_timings</code> utility is mostly useful for developers, but you could use it to optimise the number of OpenMP threads to use for your data. <a href="https://github.com/UCL/STIR/pull/1237/">PR #1237</a>.</p> </li> <li><p>New classes <code>SegmentIndices</code>, <code>ViewgramIndices</code> and <code>SinogramIndices</code>, used by <code>ProjData</code> related classes, as opposed to having to specify all the elements directly, e.g. in C++</p> <pre><code> auto sinogram = proj_data.get_sinogram(sinogram_indices);</code></pre> <p>This makes these functions more future proof, in particular for TOF. The older functions are now deprecated. Note that as <code>Bin</code> is now derived from <code>ViewgramIndices</code>, instantations of <code>Bin</code> can now be used to specify the indices as well in most places. There is still more work to do here, mostly related to the symmetries. <a href="https://github.com/UCL/STIR/pull/1273/">PR #1273</a>.</p> </li> </ul> <h4>Python (and MATLAB)</h4> <ul> <li>Examples use <code>stir.ProjData.read_from_file</code> as opposed to <code>stir.ProjData_read_from_file</code>. The former is supported since SWIG 3.0, and the <a href="https://swig.org/Doc4.1/Python.html#Python_nn20">default from SWIG 4.1</a>.</li> <li>Addition of <code>DetectionPosition</code> and <code>DetectionPositionPair</code>.</li> <li><code>bin.time_frame_num</code> is now no longer a function in Python, but acts like a variable (as the other <code>Bin</code> members).</li> <li>Addition of <code>RadionuclideDB</code></li> </ul> <h3>New examples</h3> <ul> <li><code>examples/python/construct_projdata_demo.py</code> illustrates constructing a <code>ProjDataInMemory</code></li> </ul> <h3>Changed functionality</h3> <ul> <li>Scatter estimation was resetting the activity image to 1 before each iteration. This led to cases where the reconstructed image (and therefore the scatter estimation) did not converge, especially when using a small number of sub-iterations. Now, the reconstructed image is continuouslu updated between scatter iterations by default. This should also allow users to use less sub-iterations, therefore saving some time for the scatter estimation. The old behaviour can be re-enabled by setting <code>restart_reconstruction_every_scatter_iteration</code> to true either via a parameter file or via the <code>set_restart_reconstruction_every_scatter_iteration()</code> function. <a href="https://github.com/UCL/STIR/pull/1160/">PR #1160</a>.</li> <li>energy resolution functions and keywords have now more documentation. <code>Scanner::check_consistency</code> also checks if the energy resolution is less than 20 (as it is FWHM/reference_energy). <a href="https://github.com/UCL/STIR/pull/1149/">PR #1149</a>.</li> <li>Errors now throw <code>std::runtime_error</code> instead of <code>std::string</code>. <a href="https://github.com/UCL/STIR/pull/1131/">PR #1131</a>.</li> <li>The parameter <code>use_view_offset</code> was removed from the <code>interpolate_projdata</code> functions. View-offset is now always taken into account. <a href="https://github.com/UCL/STIR/pull/1172/">PR #1172</a>.</li> <li>The info, warning and error calls are thread safe now (which makes them slower), and the logging output in <code>distributable.cxx</code> was changed from verbosity 2 (which is the STIR default) to verbosity 3. This is to reduce the default output during iterative reconstructions. <a href="https://github.com/UCL/STIR/pull/1243/">PR #1243</a>.</li> <li>The <code>Succeeded</code> class has a new method <code>bool succeeded()</code> enabling more concise code (avoiding the need for comparing with <code>Succeeded::yes</code> which is especially verbose in Python).</li> <li>The example files for the Siemens mMR now use lower min/max thresholds for the (single) scatter scale. This gives better results, see <a href="https://github.com/UCL/STIR/issues/1163/">Issue #1163</a>. <a href="https://github.com/UCL/STIR/pull/1279/">PR #1279</a>.</li> </ul> <h3>Deprecated functionality and upcoming changes to required tool versions</h3> <ul> <li>The following functions (previously used for upsampling the scatter estimate) have been made obsolete or replaced, and will be removed in STIR version 6.0.0: <code>interpolate_axial_position</code>, <code>extend_sinogram_in_views</code> and <code>extend_segment_in_views</code></li> <li>Constructors/functions in <code>ProjData</code> related classes that explicitly use <code>axial_pos_num</code>, <code>view_num</code> etc in their arguments are now deprecated, and should be replaced by their respective versions that use <code>SegmentIndices</code>, <code>ViewgramIndices</code> or <code>SinogramIndices</code>. The former will not be compatible with TOF information that will be introduced in version 6.0.0.</li> <li>Use of the AVW library to read Analyze files will be removed in 6.0, as this has not been checked in more than 15 years. Use ITK instead.</li> <li>GE VOLPET and IE support will be removed in 6.0, as we have no files to test this, and it's obsolete anyway.</li> <li>STIR version 6.0.0 will require C++ 14 (currently we require C++ 11, but already support C++ 20) and CMake 3.14.</li> </ul> <h3>Build system and dependencies</h3> <ul> <li>CMake 3.12 is now required on Windows.</li> <li>We now use CMake's <a href="https://gitlab.kitware.com/cmake/community/-/wikis/doc/tutorials/Object-Library">OBJECT library feature</a> for the registries. This avoids re-compilation of the registries for every executable and therefore speeds-up building time. Use of STIR in an external project is not affected as long as the recommended practice was followed. This is now documented in the User's Guide. <a href="https://github.com/UCL/STIR/pull/1141/">PR #1141</a>.</li> <li>The <code>error</code> and <code>warning</code> functions are now no longer included from <code>common.h</code> and need to be included manually when used (as was already the case for <code>#include "stir/info.h"</code>). <a href="https://github.com/UCL/STIR/pull/1192/">PR #1192</a>.</li> <li>add .h and .i as dependencies for SWIG generated wrappers to make sure they get rebuild. (Currently adding all .h files, which is too much, but CMake needs a fix before we can do this properly). <a href="https://github.com/UCL/STIR/pull/1218/">PR #1218</a>.</li> </ul> <h3>Changes for developers</h3> <ul> <li>moved all functionality in <code>CListEventCylindricalScannerWithDiscreteDetectors</code> to template class <code>CListEventScannerWithDiscreteDetectors</code> (templated in <code>ProjDataInfoT</code>). This enables re-use for generic/blocksoncylindrical scanners. <a href="https://github.com/UCL/STIR/pull/1222/">PR #1222</a>.</li> <li>rewritten <code>ProjDataInMemory</code> to avoid streams, causing a speed-up of some operations, and removing a limit of total size of 2GB. <a href="https://github.com/UCL/STIR/pull/1260/">PR #1260</a>.</li> </ul> <h3>Known problems</h3> <ul> <li>See <a href="https://github.com/UCL/STIR/labels/bug">our issue tracker</a>.</li> </ul> <h3>Minor (?) bug fixes</h3> <ul> <li>Small change in scatter simulation to how non-arccorrected bins are computed. Added a check in the construction of non-arccorrected projdata that the number of tangential bins is not larger than the maximum non-arccorrected bins. <a href="https://github.com/UCL/STIR/pull/1152/">PR #1152</a>.</li> <li><code>extend_segment_in_views</code> does not handle view offsets correctly and does not work for BlocksOnCylindrical scanners <a href="https://github.com/UCL/STIR/issues/1177/">issue #1177</a>. A new function <code>extend_segment</code> was added that works for Cylindrical and BlocksOnCylindrical and allows extension in tangential and axial direction as well. <a href="https://github.com/UCL/STIR/pull/1172/">PR #1172</a>.</li> <li><code>sample_function_on_regular_grid</code> did not handle offset correctly in all places <a href="https://github.com/UCL/STIR/issues/1178/">issue #1178</a>. <a href="https://github.com/UCL/STIR/pull/1172/">PR #1172</a>.</li> <li>Ray tracing projection for BlocksOnCylindrical scanner geometries contained a bug where some bins were swapped across oblique segments <a href="https://github.com/UCL/STIR/issues/1223/">issue #1223</a>. This sometimes lead to large artifacts in reconstructions. <a href="https://github.com/UCL/STIR/pull/1231/">PR #1231</a>.</li> </ul> <h3>Documentation changes</h3> <ul> <li>Updated the STIR developers guide, which was quite out-of-date w.r.t. C++ features etc.</li> </ul> <h3>recon_test_pack changes</h3> <ul> <li>Updated headers of most images and projection data to avoid warnings.</li> </ul> <h3>Other changes to tests</h3> <ul> <li><code>test_Scanner.cxx</code> tests for energy resolution, <a href="https://github.com/UCL/STIR/pull/1149/">PR #1149</a>.</li> <li>New file <code>test_interpolate_projdata</code>, <a href="https://github.com/UCL/STIR/pull/1141/">PR #1141</a>.</li> </ul>If you use this software, please cite it using the metadata from this file