added chapter on ACR phantom recon and registration

NiftyPET · Dec 11, 2021 · 7d724dc · 7d724dc
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diff --git a/docs/acr_data.rst → docs/acr_project/acr_data.rst b/docs/acr_data.rst → docs/acr_project/acr_data.rst
diff --git a/docs/acr_project/intro_acr.rst b/docs/acr_project/intro_acr.rst
@@ -0,0 +1,9 @@
+.. _science-section:
+
+************
+Introduction 
+************
+
+A phantom-imaging platform, composed of a collection of software tools, for a novel automated and high precision imaging of the American College of Radiology (ACR) PET phantom for PET/MR and PET/CT systems is presented in the next sections.
+
+The key aspect of the analysis is quantitative measurement of the spatial resolution using the measured knife-edge response :math:`K(x)`, for any phantom insert with different attenuation and activity properties, and thus facilitating comprehensive and quantitative characterisation of PET/MR or (PET/CT) scanners in multisite clinical studies, e.g., in the `Dementias Platform UK <https://www.dementiasplatform.uk/>`_ network.
diff --git a/docs/acr_project/rec_reg.ipynb b/docs/acr_project/rec_reg.ipynb
diff --git a/docs/highlights.rst b/docs/highlights.rst
@@ -1,20 +1,22 @@
 
 .. :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
-*NiftyPET* is a software platform and a Python namespace package encompassing sub-packages for high-throughput PET image reconstruction, manipulation, processing and analysis with high quantitative accuracy and precision.  One of its key applications is **brain imaging in dementia** with the use of amyloid tracers.  See below for the description of the above amyloid PET image reconstructed using *NiftyPET*, superimposed on the MR T1 weighted image [*]_.  
+*NiftyPET* is a software platform and a Python namespace package encompassing sub-packages for high-throughput PET image reconstruction, manipulation, processing and analysis with high quantitative accuracy and precision.  One of its key applications is **brain imaging in dementia** with the use of amyloid tracers.  See below for the description of the above amyloid PET image reconstructed using *NiftyPET*, superimposed on the MR T1 weighted image [*]_.
 
-*NiftyPET* includes two packages:
 
-  * ``nimpa``:  https://github.com/NiftyPET/NIMPA (neuro-image manipulation, processing and analysis)
-  * ``nipet``:  https://github.com/NiftyPET/NIPET (quantitative PET neuroimaging)
+.. note::
+
+   **Latest Research:**
 
-The core routines are written in CUDA C and embedded in Python C extensions to enable user-friendly and high-throughput executions on NVIDIA graphics processing units (GPU).  The scientific aspects of this software platform are covered in two open-access publications:
+   *Advanced quantitative evaluation of PET systems using the ACR phantom and NiftyPET software*
 
-* *NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis* Neuroinformatics (2018) 16:95. https://doi.org/10.1007/s12021-017-9352-y
 
-* *Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis* Physics in Medicine & Biology (2016). https://doi.org/10.1088/0031-9155/61/13/N322
+*NiftyPET* includes two packages:
+
+  * ``nimpa``:  https://github.com/NiftyPET/NIMPA (neuro-image manipulation, processing and analysis)
+  * ``nipet``:  https://github.com/NiftyPET/NIPET (quantitative PET neuroimaging)
 
-For more details on scientific output related to *NiftyPET* see section :ref:`science-section`.
+The core routines are written in CUDA C and embedded in Python C extensions to enable user-friendly and high-throughput executions on NVIDIA graphics processing units (GPU).  For the scientific aspects of this software platform see section :ref:`science-section`.
 
 Although, *NiftyPET* is dedicated to high-throughput image reconstruction and analysis of brain images, it can equally well be used for **whole body imaging**.  Strong emphasis is put on the data, which are acquired using positron emission tomography (PET) and magnetic resonance (MR), especially using the hybrid and simultaneous PET/MR scanners.  
 

diff --git a/docs/index.rst b/docs/index.rst
@@ -36,13 +36,22 @@ NiftyPET: High-throughput image reconstruction and analysis
    tutorials/dynrecon.rst
    tutorials/corrqnt.rst
 
+
+.. toctree::
+   :maxdepth: 2
+   :caption: ACR Phantom Imaging
+
+   acr_project/intro_acr
+   acr_project/rec_reg
+
+
 
 .. toctree::
    :maxdepth: 2
-   :caption: Open-source Data
+   :caption: Open-Source Data
 
    data
-   acr_data
+   acr_project/acr_data
 
 
 .. toctree::

diff --git a/docs/science.rst b/docs/science.rst
@@ -1,11 +1,11 @@
 .. _science-section:
 
-=================
+*****************
 Scientific Output
-=================
+*****************
 
 Scientific aspects of NiftyPET
-------------------------------
+==============================
 
 The core routines are written in CUDA C and embedded in Python C extensions to enable user-friendly and high-throughput executions on NVIDIA graphics processing units (GPU).  The scientific aspects of this software platform are covered in two open-access publications:
 
@@ -14,22 +14,37 @@ The core routines are written in CUDA C and embedded in Python C extensions to e
 * *Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis* Physics in Medicine & Biology (2016). https://doi.org/10.1088/0031-9155/61/13/N322
 
 
-Applications
-------------
 
+Advanced imaging with the ACR phantom
+=====================================
 
+Quantitative measurement of the spatial resolution using the measured knife-edge response :math:`K(x)`, for any phantom insert with different attenuation and activity properties, thus facilitating comprehensive and quantitative characterisation of PET/MR or (PET/CT) scanners in multisite clinical studies, e.g., in the `Dementias Platform UK <https://www.dementiasplatform.uk/>`_ network.
+
+  P.J. Markiewicz, C. da Costa-Luis, J. Dickson, A. Barnes, G. Krokos, J. MacKewn, T. Clark, C. Wimberley, G. MacNaught, M.M. Yaqub, J.D. Gispert, B. F. Hutton, P. Marsden, A. Hammers, A.J. Reader, S. Ourselin, K. Herholz, J.C. Matthews, F. Barkhof. **Advanced quantitative evaluation of PET systems using the ACR phantom and NiftyPET software**. Med. Phys., accepted for publication.
+
 
-* Novel MR-PET registration uncertainty analysis, indicating that registration software has the biggest effect on MR-PET registration precision, followed by reconstruction parameters (i.e., iterations, smoothing) and PET count level.  Although PVC can significantly improve the PET signal, it also increases PET signal variability since it relies on precise MR-PET registration.  More details can be found in :cite:`Markiewicz2021`:
+MR-PET registration uncertainty analysis
+========================================
 
-    P.J. Markiewicz, J.C. Matthews, J. Ashburner, D.M. Cash, D.L. Thomas, E. De Vita, A. Barnes, M.J. Cardoso, M. Modat, R. Brown, K. Thielemans, C. da Costa-Luis, I. Lopes Alves, J.D. Gispert, M.E. Schmidt, P. Marsden, A. Hammers, S. Ourselin, and F. Barkhof (2021).  **Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging**. Neuroimage 655 232, 117821. https://doi.org/10.1016/j.neuroimage.2021.117821
+Novel MR-PET registration uncertainty analysis, indicating that registration software has the biggest effect on MR-PET registration precision, followed by reconstruction parameters (i.e., iterations, smoothing) and PET count level.  Although PVC can significantly improve the PET signal, it also increases PET signal variability since it relies on precise MR-PET registration.  More details can be found in :cite:`Markiewicz2021`:
 
-* An example application of *NiftyPET* in the development of novel image reconstruction using advanced randomized optimization algorithms to solve the PET reconstruction problem for a very large class of non-smooth priors :cite:`Ehrhardt2019`:
+  P.J. Markiewicz, J.C. Matthews, J. Ashburner, D.M. Cash, D.L. Thomas, E. De Vita, A. Barnes, M.J. Cardoso, M. Modat, R. Brown, K. Thielemans, C. da Costa-Luis, I. Lopes Alves, J.D. Gispert, M.E. Schmidt, P. Marsden, A. Hammers, S. Ourselin, and F. Barkhof (2021).  **Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging**. Neuroimage 655 232, 117821. https://doi.org/10.1016/j.neuroimage.2021.117821
 
-    M.J. Ehrhardt, P.J. Markiewicz, C-B. Schönlieb (2019). **Faster PET reconstruction with non-smooth priors by randomization and preconditioning**. Phys. Med. Biol. 64(22), https://doi.org/10.1088/1361-6560/ab3d07
 
-* Dynamic PET image reconstruction for reduced acquisition time PET pharmacokinetic modelling :cite:`Scott2018`:
+Fast PET image reconstruction
+=============================
 
-    C.J. Scott, J. Jiao, A. Melbourne, N. Burgos, D.M. Cash, E. De Vita, P.J. Markiewicz, A. O'Connor, D.L. Thomas, P.S.J. Weston, J.M. Schott, B.F. Hutton, S.Ourselin (2018) **Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL–MRI: proof of concept**. Journal of Cerebral Blood Flow & Metabolism. https://doi.org/10.1177/0271678X18797343
+An example application of *NiftyPET* in the development of novel image reconstruction using advanced randomized optimization algorithms to solve the PET reconstruction problem for a very large class of non-smooth priors :cite:`Ehrhardt2019`:
+
+  M.J. Ehrhardt, P.J. Markiewicz, C-B. Schönlieb (2019). **Faster PET reconstruction with non-smooth priors by randomization and preconditioning**. Phys. Med. Biol. 64(22), https://doi.org/10.1088/1361-6560/ab3d07
+
+
+Dynamic PET image reconstruction
+================================
+
+Dynamic PET image reconstruction for reduced acquisition time PET pharmacokinetic modelling :cite:`Scott2018`:
+
+  C.J. Scott, J. Jiao, A. Melbourne, N. Burgos, D.M. Cash, E. De Vita, P.J. Markiewicz, A. O'Connor, D.L. Thomas, P.S.J. Weston, J.M. Schott, B.F. Hutton, S.Ourselin (2018) **Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL–MRI: proof of concept**. Journal of Cerebral Blood Flow & Metabolism. https://doi.org/10.1177/0271678X18797343